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1
Operations Research Society of Israel
(ORSIS)
Annual Meeting 2013
May 19-20
Leonardo Hotel, Beer-Sheva
Sponsored by:
Organizing Committee:
Gad Rabinowitz
Arieh Gavious
Ella Segev
Yoav Kerner
2
הכנס השנתי של האגודה הישראלית לחקר ביצועים
0292מאי ב 02 - 91 ,באר שבע, 4הנרייטה סולד , מלון ליאונרדו נגב
Sunday Monday 9:00 Registration 8:45 Registration
10:00-11:15 Parallel Sessions S1
Transportation 1 מצפה
Continuous Optimization תמנע
Scheduling 1 ערד
9:30-10:45 Parallel Sessions M1
Strategic Behavior in Queues מצפה
Multi Agent Optimization תמנע
Combinatorial Optimization ערד
Military OR נגב
11:15-11:30 Opening Session 10:45-11:00 מצפה Prize Award Ceremony מצפה
11:30-12:25 Naor Plenary Lecture: Dorit S. Hochbaum מצפה
11:00-12:00 Plenary Lecture:
Sergiu Hart מצפה
12:25-13:55 Lunch & ORSIS General Assembly ( 13:30) 12:00-12:15 Break
13:55-15:10 Parallel Sessions S2
Queueing 1 מצפה
Scheduling 2 תמנע
Game Theory 1 ערד
Mutli Criteria Optimization ערבה
12:15-13:05 Tutorial T2:
Shlomo Yitzhaki נגב
Parallel Sessions M2
ORSIS and Mehrez prize winners מצפה
Applied Optimization ערד
Supply Chain תמנע
15:10-15:25 Break 13:05-14:10 lunch
15:25-16:15 Tutorials T1:
Nicole Adler מצפה
Iddo Eliazar תמנע
14:10-15:30 Symposium: OR in Public Service (Hebrew) מצפה
16:20-17:35 Parallel Sessions S3 Simulation
(in Memory of Reuven Rubinstein) מצפה
Queuing 2 ערד
Data Envelopment Analysis תמנע
Heavy Scheduling Problems ערבה
15:30-16:15 Tutorials T3:
Ron Holzman מצפה
Avraham Shtub תמנע
17:35-17:45 Break 16:15-16:30 Break
17:45-21:30 Evening Program (bus leaves at 5:50pm)
Tour of Beer-Sheva – Yishai Avital
Wild Plants Agriculture – Yossi Mizrachi
Dinner @ Arabica (downtown)
16:30-17:45 Parallel Session M3
Scheduling 3 מצפה
Game Theory 2 תמנע
Queueing Network in Production ערד
Transportation 2 נגב
17:45-18:00 Closing Session
(. Dקומה ) נגב, (Mקומה ) ערבה, (תיאטרון) Dקומה -ערד Dקומה -תמנע, ( מליאה)Dקומה -מצפה: מיקום החדרים
:(חלו שינויים) מועדי רכבות 00:44, 00:72, 01:44, 01:72 ,9:44 ,9:72 ,8:44 ,8:72 :בבוקרמרכז לבאר שבע ההגע 70:47 ,71:47 ,09:47 ,08:47 ,08:17 ,02:47 ,02:17 , 01:47, 01:17 :בערבמרכז מבאר שבע היציא
3
Detailed Program - Sunday, 19.5
9:00 Registration
10:00-11:15 Parallel Session S1
Transportation 1 - Chair:
Michal Tzur מצפה
Iris Forma, Tal Raviv and Michal
Tzur,
A Clustering Algorithm for the
Static Repositioning Problem
in a Bike-Sharing System
Mor Kaspi, Tal Raviv and
Michal Tzur,
Parking Reservations in
Shared Mobility Systems
Shoshana Anily and Aharona
Pfeffer,
The Uncapacitated Dial-a-Ride
Problem on a Tree
Continuous Optimization -
Chair: Amir Beck תמנע
Marc Teboulle and Amir Beck,
Nonsmooth Convex
Minimization: To Smooth or
Not to Smooth?
Ron Shefi, Marc Teboulle,
Rate of Convergence Analysis
for Proximal-Lagrangian
Methods
Shoham Sabach, Jerome Bolte,
and Marc Teboulle,
A Simple Algorithm for
Nonconvex and Nonsmooth
Minimization Problems
Scheduling 1 - Chair: Gur
Mosheiov ערד
Shlomo Karhi, Dvir Shabtay and
Daniel Oron,
Multipurpose Machine
Scheduling with Rejection and
Identical Job Processing Times
Enrique (Tzvi) Gerstl and Gur
Mosheiov,
The optimal number of used
machines in a two-stage flow
shop scheduling problem
Baruch Mor and Gur Mosheiov,
Polynomial time solutions for
scheduling problems on a
proportionate flowshop with
two competing agents
11:15-11:30 Opening Session מצפה
11:30-12:55 Naor Plenary Lecture מצפה
Chair and opener: Aharon Ben-Tal, Technion, Speaker: Dorit S. Hochbaum, Berkeley
12:25-13:55 Lunch & ORSIS General Assembly ( 13:30)
13:55-15:10 Parallel Sessions S2
Queueing 1 - Chair: Uri Yechiali מצפה
Efrat Perel, Kostia Avrachnkov, Uri Yechiali,
The Traffic Policeman Problem: A Two-Queue
System with Switching Policy Depending on the
Queue which is NOT Being Served
Nir Perel, Uri Yechiali,
The Israeli Queue with Priorities
Shlomi Reuveni, Iddo Eliazar, Uri Yechiali,
Occupation Probabilities and Catalan Numbers in
the Asymmetric Inclusion Process
Scheduling 2 - Chair: Michal Penn תמנע
Ilan Cohen, Yossi Azar, Seny Kamara, Bruce
Shepherd
Tight Bounds for Online Vector Bin Packing
Sagi Halleli, Ephraim Korach and Michal Stern, למציאת מרכזים של קבוצות ושימושים OS-שיפור בשיטת ה
Vladimir Lipets, Eitan Israeli, Zahar Chikishev, Dvora
Lopez, Avraham Har-Paz,
Crew Assignment and Pairing Optimization
System for El Al
Game Theory 1 - Chair: Ella Segev ערד
Oscar Volij, Casilda Lasso de la Vega
Segregation, Informativeness and Lorenz
domination
Ro'i Zultan
Timing of messages and the Aumann conjecture
Shoshana Anily, Moshe Haviv
Regular cooperative games: classification and total
balancedness
Multi Criteria optimization - Chair: Moshe Kaspi
ערבה
Yarden Belsky, Yossi Bukchin, Michael Masin
DMA based multi-objective optimization
David Strimling
Multidisciplinary/Multicriteria Design Space
Exploration and Optimization
Yahel David and Nahum Shimkin
Simple Confidence Bounds Algorithms for Coupled
Multi-armed Bandit Problems
15:10-15:25 Break
4
15:25-16:15 Tutorials T1
Nicole Adler, HUJI
OR Research in the field of Aviation מצפה
Iddo Eliazar, HIT & Morrel Cohen, Princeton & Rutgers
The Econophysics of Wealth תמנע
16:20-17:35 Parallel Sessions S3
Simulation (In memory of Reuven Rubinstein) -
Chair: Joseph Kremer מצפה
Nahum Shimkin
Some Applications of Rubinstein's Cross Entropy
Method
Reuven Rubinstein, Ilya Gertsbakh, Yoseph Shpungin,
Radislav Vaisman
Permutational Methods for Performance Analysis
of Stochastic Flow Networks
Slava Vaisman
Counting vertex covers with Monte Carlo
Queueing 2 - Chair: Yoav Kerner ערד
Yuval Nov, Nafna Blanghaps, Gideon Weiss
Sojourn Time Estimation in an M/G/ Queue with
Partial Information
Yonit Barron
A Fluid EOQ model with Markovian Environment
Data Envelopment Analysis - Chair: Zilla Sinuany-
Stern תמנע
Nicole Adler, Nicola Volta, Gianmaria Martini
DEED: a Directional Economic Environmental
Distance function
Zilla Sinuany-Stern, Nir Shakohi, Osnat Cohen
Efficiency of Hospitals Operating Departments
Heavy Scheduling Problems - Chair: Gabi Pinto
ערבה Offer Reshef
Manufacturing environments and scheduling
problems overview
Avraham Mordoch
Using Exepron Software to Resolve Scheduling
Dilemmas in a Multi Projects Management
Environments
Gaby Pinto, Yariv T. Ben-Dov, Gad Rabinowitz
Formulating and Solving a Multi-Mode Resource-
Collaboration and Constrained Scheduling
Problem (MRCCSP)
17:35-17:45 Break
17:45-21:30 Evening Program (bus leaves at 5:50pm)
Tour of Beer-Sheva – Yishai Avital (Beer-Sheva City Hall)
Wild plants for the benefit of agriculture in Israel in the millennium
- Yossi Mizrachi (BGU)
Dinner @ Arabica (downtown)
5
Detailed Program – Monday, 20.5
8:45 Registration
9:30-10:45 Parallel Sessions M1
Strategic Behavior In Queues - Chair: Moshe Haviv
מצפהLiron Ravner, Moshe Haviv
Arrival Times to a Queue With Order Penalties
Binyamin Oz
Equilibrium Service Length Demand in a Single
Server Queue System
Moshe Haviv
Regulating an M/G/1 queue when customers know
their demand
Multi Agent Optimization – Chair: Roie Zivan תמנע
Sofia Amador, Steven Okamoto, Roie Zivan
Combining Fairness and Efficiency in Dynamic
Task Allocation with Spatial and Temporal
Constraints
Raz Nissim, Ronen I. Brafman,
Cost-Optimal Planning by Self-Interested Agents
Zahy Bnaya, Roni Stern, Ariel Felner, Roie Zivan,
Steven Okamoto
Multi-Agent Path Finding for Self Interested
Agents
Combinatorial Optimization - Chair: Liron
Yedidsion ערד
Mark Last, Guy Danon, Shlomo Biderman, Eli Miron
Optimizing a Batch Manufacturing Process
through Interpretable Data Mining Models
Liron Yedidsion, Moshe Kaspi, Dvir Shabtai
Complexity Analysis Of an Assignment Problem
with Controllable Assignment Costs and its
Applications in Scheduling
Nir Halman, Giacomo Nannicini, James Orlin
An Efficient FPTAS for Convex Stochastic
Dynamic Programs
Military OR - Chair: Eilam Gofer נגב
Ben Levav מודל נפגעים מאש ישירה -ר "פגיעות חיילי חי
Eilam Gofer השפעת עומס המשקל על תפקודם של לוחמים רגליים
Yonatan Grinshpun
Deliver or Search? Transportation Tactics in the
Presence of Improvised Explosive Devices
10:45-11:00 Prize Award Ceremony מצפה
11:30-12:55 Plenary Lecture מצפה
Chair and opener: Shoshana Anily, TAU, Speaker: Sergiu Hart, HUJI
12:15-13:05 Tutorial T2
12:15-13:05 Parallel Sessions M2
ORSIS and Mehrez Prize –
Chair: Moshe Haviv מצפה
ORSIS prize winner: Yoel Drori
and Marc Teboulle
A novel approach for analyzing
the performance of first-order
methods
Mehrez prize winner: Ricky
Roet-Green and Refael Hassin
Equilibrium in a two
dimensional queueing game:
When inspecting the queue is
costly
Applied Optimization - Chair:
Ofer Levi ערד
Yaniv Zaks
The Optimal Asset and
Liability Portfolio for a
Financial Institution with
Multiple Lines of Businesses
Ofer Levi, Shaul Ladany
Optimal Sailing Policy
Supply Chain - Chair: Yael
Perlman תמנע
Yael Perlman, Elad Crispil
Shift scheduling of preventive
maintenance for wafer
fabrication plants
Yael Perlman, Yaacov Oz
Reducing shoplifting by
investment in security
12:00-12:15 Break
Shlomo Yitzhaki, HUJI
Gini's Mean Difference offers a response to Leamer's critique נגב
6
13:05-14:10 lunch
סימפוזיון 14:10-15:30
מצפה חקר ביצועים בשירות הציבורי גדי רבינוביץ :מנחה . שלמה מזרחי, עודד מימון, אב-זיו אמיר, שלמה יצחקי: משתתפי הפאנל
15:30-16:15 Tutorials T3
Ron Holzman, Technion
Combinatorial auctions: selling many items at
once מצפה
Avraham Shtub, Technion
Project Planning Monitoring and Control –
Unsolved Problem and Research Opportunities
תמנע
16:15-16:30 Break
16:30-17:45 Parallel Sessions M3
Scheduling 3 - Chair: Liron Yedidsion מצפה
Danny Heremelin, Reuven Bar-Yehuda and Dror
Rawitz
Admission control in line topologies via rectangle
graphs
Yossef Luzon, Yariv Marmor, Eugene Khmelnitsky
Optimal Control of a Flow-Shop Network
Shay Hakim, Michael Masin, Michal Penn,
Asymptotically Optimal Approximation for the
(High) Multiplicity Multi-Skill Scheduling Problem
Game Theory 2 - Chair: Arieh Gavious תמנע
Itai Arieli, Manuel Mueller-Frank
Ram Orzach, Ezra Einy, Ori Haimanko, Aner Sela
Common-Value All-Pay Auctions with Asymmetric
Information
Amitay Kauffmann, Gal Zahavi
FEER Index – Forecasting Extreme Events Risk
Queueing Networks in Production systems - Chair:
Yoav Kerner ערד
Ruth Shilman, Dean Grosbard, Gadi Rabinowitz,
Israel Tirkel
Queueing Network Models for Manufacturing
Systems with Downtimes
Shai Goren, Gad Rabinowitz and Yoav Kerner
A QBD approach for resources allocation
Dean Grosbard
Rapid feasibility check for small size constrained
capacity assignment problems
Transportation 2 - Chair: Hillel Bar-Gera נגב
Uri Yovel, Asaf Levin
Local Search Algorithms for Multiple-Depot
Vehicle Routing and for Multiple Traveling
Salesman Problems with Proved Performance
Guarantees
Michal Blumberg Nitzani, Hillel Bar-Gera
Representing signalized intersections in analytic
dynamic traffic assignment model of transportation
networks.
Sarit Freund and Hillel Bar-Gera
An Optimization Framework for Travel Pattern
Interpretation of Cellular Data
17:45-18:00 Closing Session מצפה
7
Plenary Lectures
Naor Plenary Lecture
Chair and opener: Aharon Ben-Tal, Technion
Speaker: Dorit S. Hochbaum, Berkeley
Combinatorial algorithms for the Markov Random Fields problem and implications for
ranking, clustering, group decision making and image segmentation
One of the classical optimization models for image segmentation is the well known Markov
Random Fields (MRF) model. The MRF problem involves minimizing pairwise-separation and
singleton-deviation terms. This model is shown here to be powerful in representing classical
problems of ranking, group decision making and clustering. The techniques presented are stronger
than continuous techniques used in image segmentation, such as total variations, denoising, level
sets and some classes of Mumford-Shah functionals. This is manifested both in terms of running
time and in terms of quality of solution for the prescribed optimization problem We present the
first known efficient, and flow-based, algorithms for the convex MRF (the non-convex is shown to
be NP-hard). We then discuss the power of the MRF model and algorithms in the context of
aggregate ranking. The aggregate ranking problem is to obtain a ranking that is fair and
representative of the individual decision makers' rankings. We argue here that using cardinal
pairwise comparisons provides several advantages over score-wise or ordinal models. The
aggregate group ranking problem is formalized as the MRF model and is linked to the inverse
equal paths problem. This combinatorial approach is shown to have advantages over other
pairwise-based methods for ranking, such as PageRank and the principal eigenvector technique.
Plenary Lecture
Chair and opener: Shoshana Anily, TAU
Speaker: Sergiu Hart, HUJI
Two(!) Good To Be True
How to sell goods optimally? While the mechanism-design literature has solved this problem
neatly when there is only one good, the multiple goods case turns out to be extremely difficult,
mathematically and conceptually. Much of what is true for one good does not extend to multiple
goods. We will try to explain the difficulties, show what can go wrong, and then present some
universal approximation results. The talk is essentially self-contained; no background in
mechanism design is necessary.
8
Tutorials
T1 Nicole Adler, HUJI
OR Research in the field of Aviation
In this tutorial I will discuss the application of various operational research techniques to questions that
have arisen in the aviation industry, such as how to design a tender for thin airline markets, how to
encourage the aviation industry to consider the environment in their decision making processes and under
which conditions agreements between airline carriers can also increase consumer surplus.
Socially-oriented Flight Scheduling and Fleet Assignment Model with an Application to Norway One of the direct results of air transport liberalization has been the development of publicly supported links
on thin markets in remote regions through a tender process. In this article, we present a flight scheduling
and fleet assignment optimization model to determine a network configuration that minimizes the social
costs of a subsidized air transport system. With the results of the optimization model, a second stage
welfare analysis of the network is carried out that distinguishes between passenger, airline, airport and
government surpluses. The optimization model and subsequent welfare analysis were applied to the
Norwegian Public Service Obligation network, which is currently the largest in Europe. The results
obtained indicate that improvements over the current network can be achieved for all relevant stakeholders
simultaneously, with savings in the order of $1.2 million daily.
The Directional Economic-Environmental Distance function: The case of the global aviation fleet
We develop a directional economic-environmental distance function (DEED) which accounts for the
production of both desirable and undesirable output and the potential for constrained increases in input
utilization from the production of airline services. This research applies the modeling framework to analyze
the potential to reduce noise and airborne pollutants emitted by aircraft-engine combinations given the
current state of aeronautical technology. The global engine-aircraft market is viewed from the regulatory
perspective in order to compare the single environmental and operational efficient frontier to that of the
airline carriers and environmental objectives. The results of DEED are then applied in order to substitute
the fleets serving Schipol, Amsterdam and Arlanda, Stockholm airports in June 2010 with the benchmark
aircraft. The results highlight the inefficiencies of the current airline fleets and that the IPCC values of
externalities are a magnitude of TEN too low to encourage changes in the global fleet hence the need for
government intervention.
Airline agreements: Who are they good for?
The major objective of this research is to develop a model framework that analyzes airline-to-airline
agreements and their impact on both producers and multiple consumer types thus permitting airlines and
governmental authorities to fully evaluate the effects of specific agreement types. Our focal point will be to
utilize a hybrid competitive-cooperative game theoretic model in order to analyze airline agreements, from
competition (no agreement), through bi-lateral or multi-laterals, free sale, hard block, soft block, royalties
and pooling agreements up to anti-trust immunity. We assume that demand for flights depends on
frequency as well as price (i.e. inventory dependent demand as described in the operations management
literature). From a managerial perspective, alliances maximize profits whereas codesharing agreements in
which airlines still compete in frequency and price rank highest from a consumer surplus and social welfare
perspective, which should be of interest to regulators. However, (partial) mergers appear preferable to no
agreement on `thin' markets, in which both demand and profit margins are relatively low. A numerical
analysis demonstrates under asymmetric and uncertain demand that codesharing on parallel links may be
preferable to competitive outcomes for multiple consumer types.
Iddo Eliazar, HIT and Morrel Cohen, Princeton & Rutgers
The Econophysics of Wealth
In this tutorial we amalgamate ideas and concepts from various scientific disciplines – economics,
mathematics, operations research, physics, probability, and statistics – to explore a topic that was pioneered
by the celebrated Italian scientist Vilfredo Pareto: the distribution of wealth in human societies. The tutorial
is split into two parts which are outlined as follows.
9
Part I. Prolog: Gauss law and “mild” randomness, Pareto tails and “wild” randomness § The distribution of
wealth: Log-Normal bulk and Power-Law tails § The Central Limit Theorem cannot explain the distribution
of wealth § Langevin’s equation: potentials, forces, white noise, and equilibria § Ornstein-Uhlenbeck
dynamics: quadratic potentials, linear forces and Gauss equlibria § Shifting from linear to sigmoidal forces,
and from Gauss to Gauss-Exponential equlibria § Shifting from additive to multiplicative dynamics: the
emergence of a phase transition and of the composite Log-Normal–Power-Law statistical structure § The
linear-force limit and its Log-Normal structure § The Heaviside-force limit and its Log-Laplace structure §
Universality of the composite Log-Normal–Power-Law statistical structure.
Part II. Setting: complex “black-box” systems with multiplicative outputs § Entropy: the notion of entropy,
the maximization of entropy, and the notion temperature § The economic interpretation of entropy and its
shadow prices § The coincidence of the Langevin and entropy approaches: micro-scale and macro-scale,
noise and temperature § Reverse-engineering of “black-boxes”: from observed outputs to intrinsic forces §
The shapes of the forces underlying our economies: physical visualization of Adam Smith’s invisible hand
§ Physical segmentation of socioeconomic classes § Physical interpretation of socioeconomic feedback
mechanisms: rich-get-richer and poor-get-poorer § Model or interpretation? The analogue of the Merton-
Black-Scholes formula for option prices: implied volatilities and market temperatures § Epilog: Mandelbrot
and the classification of “mild” and “wild” randomness.
T2 Shlomo Yitzhaki, HUJI
Gini's Mean Difference offers a response to Leamer's critique
Gini's mean difference has decomposition properties that nest the decomposition of the variance as a special
case. By using it one may reveal the implicit assumptions imposed on the data by using the variance. I
argue that some of those implicit assumptions can be traced to be the causes of Leamer's critique. By
requiring the econometrician to report whether those assumptions are violated by the data, we may be able
to offer a response to Leamer's critique. This will reduce the possibility of supplying "empirical proofs"
which in turn may increase the trust in econometric research.
T3 Ron Holzman, Technion
Combinatorial auctions: selling many items at once
In a combinatorial auction, a number of items are being offered for sale to a group of agents whose
valuations for combinations of items may not be additive (i.e., the utility that an agent derives from owning
two of the items need not be the sum of the utilities derived from owning each of them separately).
Examples include spectrum auctions for wireless services, the sale of airport time slots, etc.
The design of such auctions meets with several challenges: obtaining economic efficiency of the allocation
of items, giving the agents the right strategic incentives, and keeping the communication and computation
burden at a feasible level.
The talk will introduce a formal model of combinatorial auctions, and present a few ideas for achieving
some of these goals, while pointing out the tradeoffs involved.
Avraham Shtub, Technion
Project Planning Monitoring and Control – Unsolved Problem and Research Opportunities
Operations research tools for solving Project Scheduling problems are focusing on the work content of the
project (the project scope). Scheduling of project activities under resource and budget constraints is an issue
that attracted the OR community attention for many years. Deterministic and stochastic models and a large
variety of solution algorithms are discussed in the literature and in OR conferences. In this tutorial we
introduce the systems engineering aspect (the product scope) and the need to develop Project Planning
Monitoring and Control methodologies that integrates the project scope and the product scope. Several
problems and the opportunity to develop new models and solution techniques will be presented along with a
teaching environment developed and tested at the Technion. The teaching environment helps students
understand the complexity of the problems and the need to find good solutions.
10
:סימפוזיון בנושא
חקר ביצועים בשירות הציבורי
:רקענצא מהשגרה של הרצאות אקדמיות כדי ; יתקיים סימפוזיון -לאחר ארוחת הצהריים , למאי 71ביום שני
? לדון בשאלה האם רצוי שחוקרי ביצועים ייטלו חלק פעיל יותר בהשבחת השירות הציבורי בישראל
:משתתפי הפאנל ,טיקהשימש עד לאחרונה כראש הלשכה המרכזית לסטטיס, כלכלן –שלמה יצחקי
,התחבורה משרד של ראשיה מדעןה – אב-זיו אמיר ,שותף פעיל למחקרים בשירות הציבורי, א"ת' אונ, המחלקה להנדסת תעשייה –עודד מימון
. חוקר את ועם השירות הציבורי, ג"ב' אונ, ראש המחלקה למנהל ומדיניות ציבורית –שלמה מזרחי
:ינחה את הדיון גדי רבינוביץ
: הדיון יתנהל בארבעה סבבי שאלות :להלן רשימת שאלות טנטטיבית
? בישראל הציבורי בשירות בחקר ביצועים השימוש מידת מה. 9
? האם מומחים אחרים נותנים מענה לצורך זהו, צורך לכךהאם קיים •
?בשירות הציבורימופעלים פחות מאחרים חוקרי ביצועיםהאם •
?בארץ עקב כך חקר ביצועיםשטח או/ו, הציבוריהשירות האם וכיצד נפגם •
? בישראל הציבורי בשירות בחקר ביצועים שימוש המעכבים הגורמים, ומהם, ישנם האם. 0
?בשירות הציבורי מתקיימיםהם והאם ,חקר ביצועיםלפרויקטי הדרושים מהם התנאים •
?הצדדים בכיוון זההאם נעשים מאמצים משני ו, האם המערכת תומכת במחקרים מסוג זה •
האם יש לחוקרים את הרצון והיכולתו, מאיים על המזמין אותו חקר ביצועיםהאם פרויקט • ?לבצעו
?בשיטות חקר ביצועיםהאם יש סכנה בשימוש יתר ו ?האם יש מכשולים מנהליים והסכמיים •
?לכך בהקשר לעשות ראוי ומה האם. 2
?לעניין ביצועיםחוקרי האם אין די בשינוי המודעות של •
?בפרויקטים גדולים חקר ביצועיםלחייב בחוק יש האם •
?בישראל לחקר ביצועיםלהקים לשכה מרכזית כדאי האם •
?בישראל לחקר ביצועים בשירות הציבוריהאם לייחד קרן לאומית •
?יישומי חקר ביצועיםלהרחיב את ההכשרה בכיוון יש האם •
שאלות מהקהל. 4
11
Book of abstracts (grouped by sessions)
Sunday, 19.5
S1
Transportation 1 - Chair: Michal Tzur
Iris Forma, Tal Raviv and Michal Tzur
A Clustering Algorithm for the Static Repositioning Problem in a Bike-Sharing System
These last years a new mode of transportation called Bike-Sharing was implemented in a large number of big cities in
the world. This young kind of mobility deals with a lot of difficulties, as bicycles' maintenance and stations' capacities.
But one major problem of bike-sharing systems is their ability to response to the fluctuating demands for bicycles and
for vacant lockers at each station, which influences directly the service level. Raviv et al. (2012) concentrated their
research on the static repositioning operation, which consists of transferring bicycles from stations with high return
rates to stations with high demand rates using a dedicated fleet. The service level is determined by the inventory at the
stations that is set by the repositioning operation, which consists of decisions on routes that the trucks should follow,
and of decisions of the number of bicycles that should be removed or placed in each station at each visit of the truck.
In this paper, we propose a three-step math heuristic solution to the existing problematic model proposed by Raviv et
al.(2012): first, stations are clustered according to geographic and inventory considerations. Second, the vehicles are
routed through the clusters while inventory decisions are made for each individual station separately. Finally, the
inventory routing problem is solved for all stations, but traversal of the vehicles is allowed only between stations of
the same cluster, or stations that belong to two consecutive clusters, according to the decision made in the previous
step. The first step is solved by a specialized saving heuristic and the last two steps are formulated as Mixed Integer
Programs and solved by a commercial solver. The method is tested on instances of up to 200 stations and three
repositioning vehicles. It is shown to perform better than previously introduced heuristics that are based on mixed
integer programming.
Mor Kaspi, Tal Raviv and Michal Tzur
Parking Reservations in Shared Mobility Systems
Shared mobility systems are increasingly deployed in cities around the world. Such systems allow users to rent a
vehicle (either a car or a bicycle) for a short period and return it in any of the system's stations scattered throughout the
city. There are many advantages to shared mobility systems. Among others, they reduce city traffic congestion and
improve utilization of city land resources, as the need for parking spaces is decreased. Financially, the sharing of
resources results in lower costs per user compared to owning a private vehicle. In addition, users are not troubled
about securing the vehicle when not in use and are not troubled about repairs
The main challenge of shared mobility system operators is to satisfy demands for vehicles (on rent) and for vacant
parking spaces (on return). The demands are typically stochastic, non-stationary and asymmetric processes.
Occasionally, users may find that some stations are empty (no available vehicles) and some are full (no available
parking spaces). In this study we measure the performance of the system by the total excess time spent by users due to
unfulfilled requests for vehicles or parking spaces.
For the first time, we propose using reservation policies in shared-mobility systems and examine their effect. In
particular, we focus on reservations of the parking spaces. In this study we investigate a policy which we refer to as
the Complete-Parking-Reservation (CPR) policy. According to this policy, when a user rents a vehicle he declares his
destination station and a vacant parking space in that station is reserved for him, if one is available. This assures that
when the user reaches his destination he will be able to return his vehicle. If there is no available parking at the
destination the transaction is denied and the user can either abandon the system or try to reserve a parking space in a
different station.
The CPR policy is compared to the base policy, entitled No-Reservation (NR). We prove that if the demand rate is not
too high, the CPR policy always performs better than the NR policy. Furthermore, we demonstrate analytically that the
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CPR policy outperforms the NR policy whenever the capacity of the system, relative to workload, is large enough to
provide adequate service level. This claim is also verified via extensive simulation study of large realistic systems.
Shoshana Anily, Aharona Pfeffer
The Uncapacitated Dial-a-Ride Problem on a Tree
This research considers the Dial-a-Ride Problem (DaRP) on a tree with a set of objects of different types: A single
vehicle of unlimited capacity is assumed to be positioned at the root of the tree. Each vertex of the tree is associated
with a pair of object types, the object type that is currently held by the vertex and the object type that is needed by the
vertex. We assume that each vertex owns a single unit of its object type and it needs a single unit of an object type.
The problem is assumed to be balanced, i.e., the total supply of each object type equals the total demand of this object
type. The objective is to design a minimum length feasible route that starts and ends at the root of the tree, so that the
vehicle while following the route loads and unloads objects at the vertices in order to satisfy the requirements of all the
vertices.
We start by proving that this version of DaRP is NP-hard. Then tight lower and upper bounds on the optimal solution
are presented. The lower bound is based on a lower bound that we developed for the Minimum Weighted Feedback
Vertex Set Problem. Next, two algorithms for solving the problem are presented. The first is an exact algorithm based
on a dynamic programming formulation. For this sake, we present some structural properties that can be assumed
without loss of generality to be satisfied by an optimal route. The second is a heuristic that determines a feasible route
while following a depth-first scanning of the tree. The heuristic is shown to give near optimal results, via a
computational study.
Continuous Optimization - Chair: Amir Beck
Marc Teboulle Amir Beck
Nonsmooth Convex Minimization: To Smooth or Not to Smooth?
Smoothing is a well-known approach to tackle nonsmooth optimization problems via adequate algorithms applied to
their smoothed counterparts. Within this approach the resulting schemes only provide a $\varepsilon$-optimal solution
to the approximated smoothed problem, which regretfully also depends on a smoothing parameter, but do not find a
$\varepsilon$-optimal solution to the original nonsmooth problem.
In this talk, we prove that independently of the structure of the convex nonsmooth function to be minimized and of a
given fast first order iterative scheme, by solving an adequately smoothed approximation counterpart, the original
nonsmooth problem can be solved with an $O(\varepsilon^{-1})$ efficiency estimate, thus improving by a square root
factor standard nonsmooth algorithms.
Our approach relies on the notion of smoothable functions that we introduce combined with a natural extension of the
Moreau infimal convolution technique and other related convex analytical tools. This allows for clarification and
unification of several issues on the design, analysis, and the potential applications of smoothing methods when
combined with fast first order algorithms, and eventually answer to the question posed in the title!
Ron Shefi, Marc Teboulle
Rate of Convergence Analysis for Proximal-Lagrangian Methods
Augmented Lagrangians based methods have attracted renewed interest recently due to their relevance for solving
large scale convex structured minimization problems arising in many applications. This talk presents two generic
classes of Proximal Lagrangian Methods (PLM) and focuses on their theoretical efficiency. We first show that the
PLM framework is a natural vehicle to build and analyze novel schemes, and is at the root of many past and recent
algorithmic variants suggested in the literature. We then prove various types of global convergence rate results for the
two proposed generic classes. Our approach relies on elementary convex analytic arguments and allows revisiting
seemingly different algorithms for which new and refined rate of convergence results are established within a unifying
framework.
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Shoham Sabach , Jerome Bolte, and Marc Teboulle
A Simple Algorithm for Nonconvex and Nonsmooth Minimization Problems
We introduce a new algorithm for a broad class of nonconvex and nonsmooth problems. It relies on an elementary
mixture of first order methods and data information. We outline a self-contained convergence analysis framework
describing the main tools and methodology to prove that the sequence generated by the proposed scheme globally
converge to a critical point. A by-product of our framework also shows that our results are new even in the convex
setting. The resulting scheme involves elementary iterations and is particularly adequate for solving many problems
arising in a wide variety of fundamental applications.
Scheduling 1 - Chair: Gur Mosheiov
Shlomo Karhi, Dvir Shabtay and Daniel Oron
Multipurpose Machine Scheduling with Rejection and Identical Job Processing Times
We study a scheduling problem where there are n jobs and m uniform machines. All jobs have the same processing
time and each job can either be rejected or be processed on one machine among a predefined subset of the m
machines. A solution to our problem is given by partitioning the jobs into two subsets, A and A, which are the set of
accepted and the set of rejected jobs, respectively, and by scheduling the jobs in set A on the m machines. We evaluate
the quality of a solution by two criteria. The first, F₁ , can be any regular scheduling criterion and the second, F₂ , is
the total rejection cost. We consider two possible types of regular scheduling criterion, the first is a maximization
criterion and the second is a summation criterion. For each criterion type we study four different problem variations.
We prove that all four variations are solvable in polynomial time for any maximization type of a regular scheduling
criterion while for a summation type of a regular scheduling criterion only one of the four problem variations is
solvable in polynomial time. We provide a pseudo-polynomial time algorithm that solves various variations of the NP-
hard problems as well as a polynomial time algorithm that solves various other special cases.
Enrique (Tzvi) Gerstl, Gur Mosheiov
The optimal number of used machines in a two-stage flow shop scheduling problem
We study practical scheduling problems with a major decision referring to the number of machines to be used. We
focus on a two-stage flowshop, where each job is processed on the first (critical) machine, and then continues to one of
the second-stage machines. Jobs are assumed to have identical processing times, and are processed in batches. A setup
time is required when starting a new batch. We consider two objective functions: minimum makespan, and minimum
flowtime. In both cases, a closed form expression for the optimal number of machines to be used is introduced, and a
unique and unusual sequence of decreasing batch sizes is shown to be optimal.
Baruch Mor, Gur Mosheiov
Polynomial time solutions for scheduling problems on a proportionate flowshop with two competing agents
In scheduling problems with two competing agents, each one of the agents has his own set of jobs and his own
objective function, but both share the same processor. The goal is to minimize the value of the objective function of
one agent, subject to an upper bound on the value of the objective function of the second agent. In this paper we study
two-agent scheduling problems on a proportionate flowshop. Three objective functions of the first agent are
considered: minimum maximum cost of all the jobs, minimum total completion time, and minimum number of tardy
jobs. For the second agent, an upper bound on the maximum allowable cost is assumed. We introduce efficient
polynomial time solution algorithms for all cases.
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S2
Queueing 1 - Chair: Uri Yechiali
Efrat Perel, Kostia Avrachnkov, Uri Yechiali
The Traffic Policeman Problem: A Two-Queue System with Switching Policy Depending on the Queue which is
NOT Being Served
A policeman regulates two streams of traffic crossing an intersection. When one direction has the right-of-way and the
accumulating number of cars in the other direction reaches a threshold, the right-of-way is transferred to the latter
direction, and vice versa. Consequently, we study a polling-type system of two non-identical and separate finite-buffer
Markovian queues served by a single server whose switching policy is based on a threshold determined by the state of
the queue which is not being served. We provide a probabilistic analysis of this system and investigate the effect of
buffer sizes and arrival rates, as well as service rates, on the system's performance.
Nir Perel, Uri Yechiali
The Israeli Queue with Priorities
The 'Israeli Queue' model was first introduced [1] when studying an N-queue, single server polling system with
unlimited-size batch-service governed by the following server's visit-order rule: After completion of a visit at a queue,
the next queue to be served is the one where its first customer in line has been waiting for the longest time. The model
was extended in [2].
In this presentation we consider a 2-class, single-server, preemptive priority queueing model in which the high priority
customers form a classical M/M/1 queue, while the low priority customers form the Israeli Queue with at most N
different groups. We present a probabilistic analysis of the model using both Probability Generating Functions and
Matrix Geometric methods and calculate various performance measures. Special cases are analyzed and numerical
results are presented.'
[1] O. Boxma, Jan v.d. Wal and U. Yechiali, Polling with Batch Service. Stochastic Models 24, 604-625 (2008).
[2] N. Perel and U. Yechiali, The Israeli Queue with Finite and Infinite Number of Families. Submitted
Shlomi Reuveni, Iddo Eliazar, Uri Yechiali
Occupation Probabilities and Catalan Numbers in the Asymmetric Inclusion Process
The Asymmetric Inclusion Process (ASIP) is a system of n Markovian queues in tandem, each with unbounded
capacity and batch service [1]. That is, when service is completed at queue k, all particles present at that queue move
simultaneously to queue k+1, or out of the system when k=n. Numerical simulations demonstrated that the probability
that site k is occupied (by one or more particles) is inversely proportional to the square root of k [2]. Consequently,
limit laws for various variables (e.g. busy period, draining time, etc.) have been established [3].
In this presentation we show that occupation probabilities in the ASIP obey a discrete two-dimensional boundary
value problem. Solving this problem we find explicit expressions for the probability that site k is occupied by l
particles (l=0,1,2,…). Catalan's numbers are shown to naturally arise in the context of these occupation probabilities.
[1] S. Reuveni, I. Eliazar and U. Yechiali, Asymmetric Inclusion Process. Physical Review E 84, 041101, (2011).
[2] S. Reuveni, I. Eliazar and U. Yechiali, The Asymmetric Inclusion Process: A Showcase of Complexity. Physical
Review Letters 109, 020603, (2012).
[3] S. Reuveni, I. Eliazar and U. Yechiali, Limit Laws for the Asymmetric Inclusion Process. Physical Review E 86,
061133 (2012).
15
Scheduling 2 - Chair: Michal Penn
Ilan Cohen, Yossi Azar, Seny Kamara, Bruce Shepherd
Tight Bounds for Online Vector Bin Packing
Recently the usage of cloud computing is increasingly wide spreading. Therefore, we study the problem of assigning
tasks with d-dimensional demand (CPU, Memory, GPU, etc.) to identical machines (computers) without overloading
in any machine d-dimensional capacity constraint. Our goal is to minimize the number of machines opened by the
algorithm. This problem reduced to the vector bin packing problem (VBP).
We study the online VBP, where the tasks arrive in an online fashion and need to be assigned immediately. We
present a lower bound of $\Omega(d^{\frac{1}{B}-\epsilon}$ where $B$ is the minimum ratio between the
capacity of machine and any task demand (in any coordinate). Additionally, we discuss almost-matching upper bound
results for general values of $B$. We show an upper bound whose exponent is additively ``shifted by 1" from the
lower bound exponent.
Sagi Halleli, Ephraim Korach and Michal Stern
למציאת מרכזים של קבוצות ושימושים OS-שיפור בשיטת ה
הינה נתון היפרגרף עם קבוצת קודקודים ואוסף של תתי קבוצות ( OS - בקיצור) Optimal Stars – clustering tree -בעיית המקבוצת הקודקודים ונתון גרף מלא על כל הקודקודים ומשקל על כל צלע רוצים למצוא עץ פורש כך שכל קבוצת קודקודים תשרה
ג חסם קומבינטורי להסתברות פיזיבילי לבעייה ונציג אלגוריתם פתרון ונצי ,בעץ כוכב וכך שסך כל המשקל של העץ יהיה מינימום .קיום
כדי להגדיל את ההסתברות למציאת פתרון פיזיבילי נרחיב את השיטה לשיטה שמתגברת על הבעיתיות של יצירת מעגלים בשיטה א "אנו משתמשים בשיטה למציאת מרכזיות של חברות בבורסה במדד ת. הקודמת ובכך נגדיל את ההסתברות לקיום פתרון פיזיבילי
011.
Vladimir Lipets, Eitan Israeli, Zahar Chikishev, Dvora Lopez, Avraham Har-Paz
Crew Assignment and Pairing Optimization System for El Al
CAPOS (Crew Assignment and Pairing Optimization System) is a robust, powerful, user-friendly system for crew
pairing and assignment optimization, designed to meet the rigorous and dynamic requirements of commercial airlines.
CAPOS is the result of intensive collaborative research and development between IBM Israel Software Labs and El Al
Israel Airlines Ltd.. Finding optimal assignments is especially complicated, since demands for different activities and
availability of human resources vary over time. Added to the complexity are such factors as distinct types of crew
members, night and weekend flights, high rates of attrition, and changes in skill availability.
CAPOS allows the user to handle crew preferences and availability constraints, and make sure business rules such as
fairness are maintained when shifts are assigned – and all this dynamically, without compromising business
objectives, labor regulations, or operational demands. CAPOS provides optimal results quickly and easily - solving the
problems of optimal pairing creation and crew member assignments. With a set of hard constraints, the system
balances hundreds of dynamic rules and criteria defined by the user, thereby minimizing costs for management and
operation, and improving satisfaction.
Game Theory 1 - Chair: Ella Segev
Oscar Volij, Casilda Lasso de la Vega
Segregation, Informativeness and Lorenz domination
A city consists of a population of two ethnic groups distributed across neighborhoods. The more different the two
groups are distributed across the various neighborhoods, the more segregated the city is. For any city, one can build its
Lorenz segregation curve and then partially order the set of cities accordingly. Similarly, for any city one can build an
associated experiment and partially order the cities according to their informativeness. We show that the partial orders
based on Lorenz segregation curves and on informativeness of the experiment coincide.
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Ro'i Zultan
Timing of messages and the Aumann conjecture
The Aumann (1990) conjecture states that cheap-talk messages do not necessarily help to coordinate on efficient Nash
equilibria. In an experimental test of Aumann’s conjecture, Charness (2000) found that cheap-talk messages facilitate
coordination when they precede the action, but not when they follow the action. Standard game-theoretical modeling
abstracts from this timing effect, and therefore cannot account for it. To allow for a formal analysis of the timing
effect, I study the sequential equilibria of the signaling game in which the sender is modeled as comprising two selves:
an acting self and a signaling self. I interpret Aumann’s argument in this context to imply that all of the equilibria in
this game are ‘babbling’ equilibria, in which the message conveys no information and does not affect the behavior of
the receiver. Using this framework, I show that a fully communicative equilibrium exists—only if the message
precedes the action but not when the message follows the action. In the latter case, no information is transmitted in any
equilibrium. This result provides a game-theoretical explanation for the puzzling experimental results obtained by
Charness (2000). I discuss other explanations for this timing-of-message effect and their relationship to the current
analysis.
Shoshana Anily, Moshe Haviv
Regular cooperative games: classification and total balancedness
The research on cooperative games with transferrable utility in supply chains and service systems is growing as a
result of increasing collaboration among partners in the association, and the need for fair cost allocation schemes. In
this talk we present a new framework that fits many cooperative games in operations management, where each player
is fully characterized by a vector of quantitative properties, representing his/her contribution to any coalition he/she
may join, and the cost of a coalition is a closed algebraic expression of the vectors of properties of the coalition's
members. In such games the characteristic function values of different coalitions are strongly related, as all are
obtained by applying the same algebraic expression on the input of vectors of properties associated with each
coalition. We call such games regular games. We discuss some new theoretical results that hold within the proposed
framework, and we provide new conditions for verification of total balancedness of such games. A few examples in
the areas of retailing, service management and facility location will be presented.
Multi Criteria optimization - Chair: Moshe Kaspi
Yarden Belsky, Yossi Bukchin, Michael Masin
DMA based multi-objective optimization
In Multi-Objective Optimization multiple conflicting objective functions are optimized simultaneously subject to a set
of constraints. In this research, we first provide a formal problem definition where the Pareto-optimal solutions and
efficient frontier approach replace the notion of optimal solution in single-objective optimization. Then, we investigate
an interaction with the decision maker (DM), and provide a method for finding a suitable solution for the decision
maker, based on DMA (a method for finding a diverse representation of non-dominated solutions on the efficient
frontier). Different types of decision makers are considered including decision maker's risk attitude, feedback
inaccuracies and resolution.
In the proposed approach, decision makers are required to rank a limited number of diversified non-dominated
solutions, and the algorithm, in return, provides estimated parameters of the unknown utility function, and additional
potential solutions, based on the above ranking. Finally, a wide empirical experiment verifies the performance of the
proposed algorithm for different types of decision makers and problem types. In all experiments the algorithm finds a
solution that is just few percent far from the optimal solution of the actual decision maker's utility function, while on
average the distance is less than 1%..
David Strimling
Multidisciplinary/Multicriteria Design Space Exploration and Optimization
Multidisciplinary models are system level representation of products that require multiple engineering
disciplines for design. These multiple engineering disciplines could include Finite Element Analysis (FEA),
Computational Fluid Dynamics (CFD) and other Computer Aided Engineering (CAE) or Computer Aided Design
(CAD) tools to represent the system functionality. A multidisciplinary model precludes sub-optimal designs by
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integrating these multidisciplinary engineering analysis tools into a system level model that allows integrated analysis
at the system rather than subsystem level. Analysis of multidisciplinary systems uses a multidisciplinary, system
level model to understand the design space and seek optimum solutions that satisfy customer requirements.
This multidisciplinary system level model enables system level design space exploration and optimization to exploit
all of the interactions between the disparate engineering disciplines required to represent the details of the product
being designed –
1) Design space exploration will isolate critical design parameters and feasible regions of the system
level design space.
2) Multicriteria optimization will generate an efficient frontier of solutions at the system level focusing
on the critical design parameters and feasible regions of interest discovered during the design space
exploration.
This paper will provide an introduction to the concepts and processes of multidisciplinary/multicriteria design
space exploration and optimization. The paper will introduce the MEGA process;
Yahel David and Nahum Shimkin
Simple Confidence Bounds Algorithms for Coupled Multi-armed Bandit Problems
The classical framework of the statistical multi-armed bandit problem assumes independent parameterization of the
mean rewards of different arms. We consider here the extended model, where the parameters of the different arms may
be coupled, so that the obtained rewards at one arm can provide meaningful statistical information on the expected
reward at other arms. We propose simple low-regret control laws for this model, extending the UCB1 algorithm of
Auer et al. (2002) to this case. We provide logarithmic upper bounds on the regret, and show that the effective use of
cross-arm coupling can reduce the logarithmic coefficient of increase of the regret for certain arms, while reducing
logarithmically-increasing regret to a finite one for other arms.
S3
Simulation (In memory of Reuven Rubinstein) - Chair: Joseph Kremer
Nahum Shimkin
Some Applications of Rubinstein's Cross Entropy Method
The celebrated Cross Entropy method has been introduced by Reuven Rubinstein in the late 1990's, first for rare event
simulation, and then extended as a powerful heuristic for combinatorial optimization. It has since found numerous
applications in a variety of challenging optimization problems. In this talk I will briefly survey some applications in
which I had part, from multiple-target tracking to reinforcement learning and basis function adaptation.
Reuven Rubinstein, Ilya Gertsbakh, Yoseph Shpungin, Radislav Vaisman
Permutational Methods for Performance Analysis of Stochastic Flow Networks
In this paper we show how the permutation Monte Carlo method, originally developed for reliability networks, can be
successfully adapted for stochastic flow networks, and in particular for estimation of the probability that the maximal
flow in such a network is above some fixed level, called the threshold. A stochastic flow network is defined as one,
where the edges are subject to random failures. A failed edge is assumed to be erased (broken) and, thus, not able to
deliver any flow. We consider two models; one where the edges fail with the same failure probability and another
where they fail with different failure probabilities. For each model we construct a different algorithm for estimation of
the desired probability; in the former case it is based on the well known notion of the D-spectrum and in the later one -
on the permutational Monte Carlo.
Slava Vaisman
Counting vertex covers with Monte Carlo
In graph theory, a vertex cover of a graph is a set of vertices such that each edge of the graph is incident to at least one
vertex of the set. We are interested in counting all vertex covers in a graph. The area of counting, and in particular the
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definition of #P complete class introduced by Valiant in 1979 received much attention in the Computer Science
community. For example, Karp and Lubby introduced a FPRAS (A fully polynomial randomized approximation
scheme) for counting the solutions of DNF satisfiability formula. Similar results were obtained for the Knapsack and
Permanent problems. On the other hand, there are many 'negative' results. Dyer, Frieze and Jerrum showed that there
is no FPRAS for #IS (Independent Set) if the maximum degree of the graph is 25 unless RP = NP. Counting the
number of vertex covers remains hard even when restricted to planar bipartite graphs of bounded degree, or regular
graphs of constant degree.
We propose a Sequential Importance Sampling procedure for counting the number of vertex covers in a graph. In spite
of the fact that counting algorithms based on Importance Sampling seems to perform very well in practice, it is also
known that their performance depends heavily on the closeness of the proposal distribution to the uniform one. Our
algorithm introduces probabilistic relaxation technique combined with Dynamic Programming, in order to obtain
efficient estimate of this distribution. At each step, the procedure decides whether to include the given vertex in the
cover set or not. The decision is based on the approximated number of covers in each case. During the algorithm
execution, we guarantee that the sequential procedure always produces a valid vertex cover while saving computation
effort. Moreover, the algorithm can supply a probabilistic lower bound that is calculated online.
Queueing 2 - Chair: Yoav Kerner
Yuval Nov, Nafna Blanghaps, Gideon Weiss
Sojourn Time Estimation in an M/G/ Queue with Partial Information
We propose an estimator for the CDF G of the sojourn time in a steady-state M/G/ queueing system, when the
available data consists of the arrival and departure epochs alone, without knowing which arrival corresponds to which
departure. The estimator is a generalization of an estimator proposed by Brown in 1970, and is based on a functional
relationship between G and the distribution of the time between a departure and the rth latest arrival preceding it. The
estimator is shown to outperform Brown's estimator, especially when the system is heavily loaded.
Yonit Barron
A Fluid EOQ model with Markovian Environment
We consider a single bu¤er fluid system in which the rate of change of the fluid is determined by the current state of a
background stochastic process called environment. When the fluid level hits zero, it can undergo an instantaneous
transition to state i 2 f1; :; ng and jumps to a predetermined positive level qi. The costs (purchasing and holding costs)
are modulated by the state at the order epoch time. Using matrix analytic approach, fluid flow analysis and martingales
we develop methods to compute the expected discounted order and holding costs and the long run average costs.
Data Envelopment Analysis - Chair: Zilla Sinuany-Stern
Nicole Adler, Nicola Volta, Gianmaria Martini
DEED: a Directional Economic Environmental Distance function
We propose a new data envelopment analysis model in order to compute efficiency from an eco-environmental point
of view. The directional, eco-efficient distance (DEED) function model includes undesirable as well as standard
variables hence is suitable for multiple industrial applications. Moreover, the model designs a single frontier which
permits a constrained increase in inputs where relevant. In addition, given the prices of inputs, the objective function
estimates the maximum monetary savings potential for inefficient decision making units. We show that the DEED
function improves on existing models in the literature by substantially reducing negative externalities within a budget
constraint and utilizing only efficient decision making units as benchmarks.
Zilla Sinuany-Stern, Nir Shakohi, Osnat Cohen
Efficiency of Hospitals Operating Departments
In this study we measured the efficiency of 24 operating department in Soroka Hospital, in Beer-Sheva, Israel. Data
Envelopment Analysis (DEA) was used with 5 inputs and 3 outputs over 2008 and 2009. The 5 inputs used are: 1.the
average time between consecutive operations, 2. the no. of hours allocated to the department, 3. the number of
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spillover hours beyond the daily shift, 4. the no. of repeated operations, and 5. the average hour when the first
operation starts in the daily shift. The 3 outputs used are: 1. income, 2. average hour of ending the daily operations,
and 3. total no. of operating hours. There are three operating sites in the hospital serving 24 operating departments.
There was no significant difference among the three sites efficiency. There was no difference between the efficiency
of the two years considered (2008 and 2009). Looking at the operating rooms utilization (the old measure of
effectiveness of operating rooms, we found that it differs significantly from the DEA rating. In the input oriented
model we used input no. 1 required the highest percentage of reduction (43%), input no 3 was the next (23%). All the
other inputs required 5% reduction.
Heavy Scheduling Problems - Chair: Gabi Pinto
Offer Reshef
Manufacturing environments and scheduling problems overview
This lecture provides an overview to some industry manufacturing environments scheduling problems. Starting with a
brief presentation of OPTUM-Dispatcher, OPTUM-IES scheduling and dispatching platform followed by a
presentation f a few manufacturing environments with their unique problems, issues & goals.
For each environment the lecture will review:
Initial dispatching methods in use and management planning capabilities.
Main characteristics, scheduling/dispatching issues.
Facility goals and management focus.
Mathematical problem overview
Implementation highlights
Improvements achieved and the new scheduling and dispatching method.
We will finalize with the view of the most critical information required as an outcome of the scheduling to the
planners & production manager
Keywords: dispatching; scheduling; manufacturing environment.
Avraham Mordoch
Using Exepron Software to Resolve Scheduling Dilemmas in a Multi Projects Management Environments
Every project manager is well aware of the dilemma he many times has in prioritization between tasks when the
resource needed is the execution of these tasks is the same resource. The very popular method of the Critical Path
cannot provide the project managers with the right direction in resolving this dilemma since Critical Path has an
almost hidden assumption that the availability of the resources is unlimited. Unlike the Critical Path, the Critical Chain
methodology, which is the derivative of the Theory of Constraints (TOC), identifies the chain of tasks with sequence
of both precedence- and resource-dependent tasks that prevent a project from being completed in a shorter time, given
finite resources. The Exepron software, which is only about a year in the market, was developed to support the Critical
Chain methodology.
Exepron is a cloud based, real time, planning and execution tool that provides the right planning based on the Critical
Chain methodology not only during the planning phase of the project but, not less important, during the execution
phase also. Every day that passes, the software calculates the penetration into the different buffers of the project and
provides the project manager with the sense of urgency of the different, still uncompleted, tasks and as a result the
project managers have the right priority for all tasks of the project.
The problem is, of course, getting more complicated in the very common multi-projects environment, when the same
resources are shared between projects in the same group (division or department). In this case the first issue is how to
significantly decrease the high probability that delays in one project, not necessarily a strategic one, are causing delays
in many of the other projects in the same group, sharing the same resources. The method to avoid these delays, which
is a part of the Critical Chain, is called Drum and is based on the following steps:
a) Strategic prioritization between the projects
b) Identification the most loaded resource (the Drum) across all participating projects in the horizon of time
between today and the end of the last task of all the projects
c) Synchronization between the projects taking into account the priority (step A), the load on the Drum (step B)
and a capacity buffer of the Drum resource
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The result is a planned delay of some of the projects but with realistic planned timetable for all projects vs. unrealistic
planning with no delays in the planning phase but by far more delays in the execution phase of the participating
projects.
Gaby Pinto, Yariv T. Ben-Dov, Gad Rabinowitz
Formulating and Solving a Multi-Mode Resource-Collaboration and Constrained Scheduling Problem
(MRCCSP)
The main motivation of this study is to provide for the first time a formulation and solution for a class of production
scheduling problems (as in cluster tools) characterized mainly by resource collaboration to perform an operation,
while allowing batches and considering alternative production methods. We develop a formulation for the new
problem and term it a multiple mode per operation, resource collaboration, and constrained scheduling problem
(MRCCSP). Some of the important new characteristics we consider are: multiple products (families); Multiple orders
(jobs) per family; Precedence restrictions among the operations that constitute a job; Alternative modes for the
performance of an operation (each of which needs a set of collaborating resources) may be defined; Complementary
and exclusive restrictions between operation-modes; Batch production is allowed; and setup times may depend on
sequence and batch-size. The objective of the MRCCSP is to minimize makespan. We formulate the MRCCSP as a
mixed integer linear programming model, and acknowledging the considerable size of the monolithic formulation
required, we prescribe a specific method to achieve size reduction. A customized branch and bound (B&B) algorithm
for optimally solving this problem is proposed and examined experimentally. Finally, we developed a genetic
algorithm (GA) and compared the effectiveness and runtime of the GA versus a B&B algorithm. The results
demonstrate that the GA reaches optimality in most cases, and its runtime was much less sensitive to the size of the
problem instance.
Keywords: resource-sharing and scheduling problem; resource constrained scheduling problem; mixed integer linear
programming; branch and bound algorithm.
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Monday 20.5
M1
Strategic Behavior In Queues - Chair: Moshe Haviv
Liron Ravner, Moshe Haviv
Arrival Times to a Queue With Order Penalties
Customers are often faced with a choice of when to arrive to a congested queue with some desired service at the end.
Suppose the server operates for a certain time interval , and customers are served according to their arrival order. We
study a model where the customers incur not only congestion (waiting) costs but also penalties for their index of
arrival. Arriving before other customers is desirable when the value of service decreases with every admitted
customer. Examples of such scenarios are arriving at a concert or a bus with unmarked seats and going to lunch in a
cafeteria. We provide game theoretic analysis of such queueing systems, specifically we characterize the arrival
process which constitutes a symmetric Nash equilibrium.
Binyamin Oz
Equilibrium Service Length Demand in a Single Server Queue System
In this talk we propose a model of an unobservable M/?/1 queue where costumers chooses their service length
strategically in order the maximize their own welfare. We assume a quasilinear utility function with increasing and
concave utility from the service length and linear waiting costs. We assume that the costumers differ from each other
by their evaluation of the service, which is random and private information. We show that in a symmetric equilibrium,
the joining strategy will be based on threshold of the service evaluation, and the service length chosen by the
costumers will be increasing function of their service evaluation. Next, we find the socially optimal strategies and
discuss various ways of regulation.
Moshe Haviv
Regulating an M/G/1 queue when customers know their demand
Selfish customers do not necessarily join a queue at a socially optimal rate. Hence, queueing systems may call for
regulation. For customers in an M/G/1 unobservable (not necessarily FCFS) M/G/1 queue and homogeneous with
respect to waiting costs and service rewards, we show how queueing systems can be regulated by imposing entry,
holding, or service fees in the case where customers know their service requirements. We start with a unified
approach, assuming minimal assumptions on the waiting functions, and state the socially optimal fees.
We show that customers are always worse off under a flat entry free in comparison with holding and service fees.
As for holding vs. service fees, the answer depends on the queueing regime and/or the service length. For example,
under FCFS, service fees are preferred by all. Details are given on some common service regimes. For the sake of
completeness we also review the case where customers know only their distribution, but not its requirement.
Multi Agent Optimization – Chair: Roie Zivan
Sofia Amador, Steven Okamoto, Roie Zivan
Combining Fairness and Efficiency in Dynamic Task Allocation with Spatial and Temporal Constraints
Realistic multiagent team applications often feature distributed dynamic environments with soft deadlines that
penalize late execution of tasks. This puts a premium on quickly allocating tasks to agents, but finding the optimal
allocation is NP-hard due to temporal and spatial constraints that require tasks to be executed sequentially by agents.
We propose a novel task allocation algorithm that allows tasks to be easily sequenced to yield high quality solutions
by finding allocations that are fair (envyfree), balancing the load and sharing important tasks between agents, and
efficient (Pareto optimal).We compute such allocations in polynomial time using a Fisher market with agents as
buyers and tasks as goods, then sequence the allocations by maximizing utility at each step. We empirically compare
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our algorithm to two state-of-the-art incomplete methods on synthetic problems and on realistic law enforcement
problems inspired by real police logs. The results show a clear advantage for our algorithm in measures commonly
used by law enforcement authorities.
Raz Nissim, Ronen I. Brafman,
Cost-Optimal Planning by Self-Interested Agents
As our world becomes better connected and autonomous agents no longer appear to be science fiction, a natural need
arises for enabling groups of selfish agents to cooperate in generating plans for diverse tasks that none of them can
perform alone in a cost-effective manner. While most work on planning for/by selfish agents revolves around finding
stable solutions (e.g., Nash Equilibrium), this work combines techniques from mechanism design with a recently
introduced method for distributed planning, in order to find cost optimal (and, thus, social welfare maximizing)
solutions. Based on the Vickrey-Clarke-Groves mechanisms, we present both a centralized, and a privacy-preserving
distributed mechanism.
Zahy Bnaya, Roni Stern, Ariel Felner, Roie Zivan, Steven Okamoto
Multi-Agent Path Finding for Self Interested Agents
Multi-agent pathfinding (MAPF) deals with planning paths for individual agents such that a global goal function (e.g.,
the sum of costs) is minimized, while avoiding collisions between agents. Previous work proposed centralized or fully
cooperative decentralized algorithms assuming that agents follow paths assigned to them. However, when agents are
self-interested, they are not expected to follow paths unless they consider them to be their most beneficial option. In
his paper we propose the use of taxation schemes to implicitly coordinate self-interested agents in MAPF. We propose
several taxation schemes and compare them experimentally. We show that intelligent taxation schemes can result in a
lower total cost than the non coordinated scheme even if we take into consideration both the costs of the original graph
and the taxes paid by agents.
Combinatorial Optimization - Chair: Liron Yedidsion
Mark Last, Guy Danon, Shlomo Biderman, Eli Miron
Optimizing a Batch Manufacturing Process through Interpretable Data Mining Models
In this talk, we present a data mining based methodology for optimizing the outcome of a batch manufacturing
process. Predictive data mining techniques are applied to a multi-year set of manufacturing data with the purpose of
reducing the variation of a crystal manufacturing process, which suffers from frequent fluctuations of the average
outgoing yield. Our study is focused on specific defects that are the most common causes for scraping a manufactured
crystal. A set of probabilistic rules explaining the likelihood of each defect as a function of interaction between the
controllable variables are induced using the Single-Target and the Multi-Target Information Network algorithms. The
rules clearly define the worst and the best conditions for the manufacturing process, also providing a complete
explanation of all major fluctuations in the outgoing quality observed over the recent years. In addition, we show that
an early detection of nearly the same predictive model was possible almost two years before the end of the data
collection period, which could save many of the flawed crystals. The paper provides a detailed description of the
optimization process, including the decisions taken at various stages and their outcomes. Conclusions applicable to
similar engineering tasks are also outlined.
The talk is based on the paper published in the Journal of Intelligent Manufacturing.
Liron Yedidsion, Moshe Kaspi, Dvir Shabtai
Complexity Analysis Of an Assignment Problem with Controllable Assignment Costs and its Applications in
Scheduling
We extend the classical linear assignment problem to the case where the cost of assigning agent j to task i is a
multiplication of task i's cost parameter by a cost function of agent j. The cost function of agent j is a linear function of
the amount of resource allocated to the agent. A solution for our assignment problem is defined by the assignment of
agents to tasks and by a resource allocation to each agent. The quality of a solution is measured by two criteria. The
first criterion is the total assignment cost and the second is the total weighted resource consumption. We address these
criteria via four different problem variations. We prove that our assignment problem is NP-hard for three of the four
23
variations even if all the resource consumption weights are equal. However, and somewhat surprisingly, we find that
the fourth variation is solvable in polynomial time. In addition, we find that our assignment problem is equivalent to a
large set of important scheduling problems whose complexity has heretofore been an open question for three of the
four variations.
Nir Halman, Giacomo Nannicini, James Orlin
An Efficient FPTAS for Convex Stochastic Dynamic Programs
We propose an efficient Fully Polynomial-Time Approximation Scheme (FPTAS) for stochastic convex dynamic
programs using the technique of K-approximation sets and functions introduced by Halman et al. This paper deals
with the convex case only, and it has the following contributions: First, we improve on the worst-case running time
given by Halman et al. Second, we design an FPTAS with excellent practical performance, and show that it is faster
than an exact algorithm even for small problems instances and small approximation factors, becoming orders of
magnitude faster as the problem size increases. Third, we show that with careful algorithm design, the errors
introduced by floating point computations can be bounded, so that we can provide a guarantee on the approximation
factor over an exact infinite-precision solution. Our computational evaluation is based on randomly generated problem
instances coming from applications in supply chain management and finance.
Military OR - Chair: Eilam Gofer
Ben Levav
מודל נפגעים מאש ישירה -ר "ת חיילי חיפגיעו
הראשונה היא אפקט הדיכוי אשר מנחית את יכולות הלוחמים : האש בקרב היבשה משפיעה על הכוחות הלוחמים בשתי דרכים שונות, העתידיתנפגעים בכוח לוחם משפיעים על יכולתו לעמוד במשימתו הנוכחית או . לפרקי זמן שונים והשני הוא הנפגעים שסופג הכוח
.מחייבים תשומת לב והקצאת כוחות לשם פינוי וטיפול ופוגעים במורל המסגרת
לרוב זו אש , המצויה בקשר עין עם היורה, אש הנורית לעבר מטרה -ואש ישירה ( ארטילרית)אנו מבדילים בין שני סוגי אש עקיפה אש ישירה . ל ומקלעים"נק -כזו הנורית מנשק קליעי ר האש הישירה הרלוונטית היא"בהקשר של היפגעות כוח חי. שטוחת מסלול
. עם זאת היא שכיחה יותר בשלבי הלחימה אל היעד ובלחימה על היעד. ר בכל שלבי ההתקפה"יכולה להיות מופעלת כנגד כוח חי
אש עקיפה מוגבלת מכיוון שיכולת התגובה שלו כנגד , זאת. כוח שתוקף רגלית יגיב באופן שונה לאש ישירה מאשר כלפי אש עקיפהלרוב . ר עם אש ישירה גדול יותר"מרחב אפשרויות ההתמודדות של כוח חי, לעומת זאת. מאוד וכוללת בעיקר צעדי מניעה והתמגנות
. אל עבר מקור הירי על מנת לגרום לשיתוקו ולפגיעה בו, תוך כדי הפעלת אש ישירה משל עצמו, ישאף הכוח התוקף להתקדם .צדדי-בעוד תרחיש הפעלת אש ישירה מוגדר כדו, צדדי-ר תוקף כחד"גדיר תרחיש הפעלת אש עקיפה כנגד כוח חיניתן לה, מתודולוגית
הוא מתאר את פעולותיהם של הכוחות . מודל הנפגעים מאש ישירה הוא מודל מתמטי הסתברותי הממומש באמצעות סימולציה.על התפלגות מספר הנפגעים בקרב השונים בשלבי הקרב השונים באמצעות כלים מתמטיים ומסיק מכך
Eilam Gofer
השפעת עומס המשקל על תפקודם של לוחמים רגליים
הנדרש לא רק להפעיל , ר הוא האדם הלוחם"המוקד בחי. אינו מזוהה עם מערכת נשק עיקרית, בניגוד לחילות אחרים, חיל הרגליםיכולת הלוחמים לנוע רגלית עם משקל . מזון ומים, ציוד אישי, תחמושת, מגוון גדול של מערכות נשק אלא גם לשאת אמצעי לחימה
לנסח ולקבוע אילוצי , לזהות, ל בפרט"ובצה, לפיכך נעשה מאמץ רב בצבאות העולם. ר"של החימהווה את אחד המרכיבים העיקריים אולם הניסיון מראה כי קשה מאוד לעמוד באילוצי המשקל . ר"משקל מחמירים שבכפוף להם יתבצעו תהליכי בניין הכוח עבור החי
היתר ושל אופן השפעתו על היכולות -ה של משמעויות משקלמכאן החשיבות לפתח מּודעות והבנ. הללו והם מופרים באופן כמעט קבוע .ר"המבצעיות של לוחם החי
על ידי ניתוח הקשר שבין משך פעילות הלוחם בתנועה רגלית לבין המשקל שהוא ר"העבודה שתוצג מעריכה את יכולותיו של לוחם החיח וההצטיידות בו כלים גמישים יותר בבואם לבצע את "ניתוח כזה ייתן למקבלי ההחלטות בתחום פיתוח האמל, לדעתנו. נושא
.משימתםהמשימה המוטלת עליו והסביבה , שונים של הלוחםמתמטי הקושר בין פרמטרים -לצורך הניתוח האמור פותח בעבודה מודל פיזיולוגי
.שבה הוא פועל לבין משך הפעילות שהוא מצליח לבצע
Yonatan Grinshpun
Deliver or Search? Transportation Tactics in the Presence of Improvised Explosive Devices
Many military conflicts in recent years are asymmetric, in the sense that a relatively large, organized, trained and well
equipped government force is confronted by a relatively small, loosely organized and poor equipped insurgency. The
insurgents, realizing that direct confrontation with the government force may lead to the insurgency demise, use
guerrilla tactics to attack the government forces. As demonstrated in Southern Lebanon, Iraq and Afghanistan, one of
the most popular weapons used by insurgents and most lethal and damaging to the government forces, is improvised
explosive devices, planted along roads with the objective to hit and damage vehicles traveling on that road.
24
In this research, we present Markov-based probability models for an important problem related to current combat
situations, Improvised Explosive Devices (IED) attacks on convoys transporting supply and troops. We develop
continuous and discrete time Markov models to capture the key aspects that describe the situation and implement them
computationally to obtain some tactical insights regarding the effect of various operational parameters on the outcome
of convoy transportation missions in the presence of IED.
M2
ORSIS and Mehrez Prize Winners – Chair: Moshe Haviv
ORSIS prize winner: Yoel Drori and Marc Teboulle
A novel approach for analyzing the performance of first-order methods
We introduce a novel approach for analyzing the performance of first-order black-box optimization methods.
Following the seminal work of Nemirovski and Yudin (1983) in the complexity analysis of convex optimization
methods, we measure the computational cost based on the oracle model of optimization. Building on this model, our
approach relies on the observation that by definition, the worst case behavior of a black-box optimization method is by
itself an optimization problem, which we call the Performance Estimation Problem (PEP). We analyze the properties
of the resulting PEP for various black-box first order schemes. This allows us to prove a new tight analytical bound for
the classical gradient method, as well as to derive numerical bounds that can be efficiently computed for a broad class
of first order schemes. Moreover, we derive an efficient procedure for finding step sizes which produces a first-order
black-box method that achieves best performance.
Mehrez prize winner: Ricky Roet-Green and Refael Hassin
Equilibrium in a two dimensional queueing game: When inspecting the queue is costly
The classical model of customers' decision making in an unobservable queue assumes that it is too costly to acquire
the queue length information. However, due to recent advances in communication technology, various services make
that kind of information accessible to customers at a reasonable cost. Our model reflects this new opportunity. In our
model, customers of an unobservable queue can choose from three options: join the queue, balk, or acquire
information about the queue length and then decide whether or not to join it. When customers' decision problem
includes only two possible actions, it is intuitively expected that the action taken by an individual who tries to avoid
congestion will be inversely correlated with the actions taken by the others. In a model of more than two actions, the
strategy of the customers in equilibrium is not straightforward. We compute the equilibrium in this model and prove
its existence and uniqueness. We also show that the monopoly firm, that wishes to maximize the number of customers
who enter queue, can benefit from offering this third possibility of obtaining queue length information. Comparing to
the classical queue policies, the observable and unobservable queue models, we show that in many cases, the number
of customers that enter the queue will be larger with the queue policy that is presented in our model.
Applied Optimization - Chair: Ofer Levi
Yaniv Zaks
The Optimal Asset and Liability Portfolio for a Financial Institution with Multiple Lines of Businesses
In this paper we present an optimization framework to deal with the asset-liability portfolio selection problem. We
consider a financial institution that has multiple lines of business. The capital allocation is obtained by minimizing the
sum of the expected squared differences between the liability in each line of business and the value of the
corresponding investment portfolio. We show that in certain circumstances the bottom-up approach is consistent with
the top-down approach, where the optimal capital is determined for the whole portfolio rather than its individual
components. Such a case happens for example if the same weight function is used for all lines of business in the two
approaches. Finally, we obtain investment portfolios under some limitations on short sales.
25
Ofer Levi, Shaul Ladany
Optimal Sailing Policy
A dynamic programming model has been formulated to derive the minimal sailing route for a vessel to sail from an
origin to a destination, while passing n different segments in which deterministic wind conditions prevail. Turning
times and crossing wind-lines were considered. Complicated discrete computer solution procedure was programmed
which had to cope with ranges of inadmissibility of the continuous decision variables caused by inability to sail at
certain angles around a facing wind. Binary search method and iterative refinement schemes were applied to reduce
calculation times to the order of magnitude of ne minute, while obtaining sufficient accuracy. Numerical examples are
provided. Sensitivity analysis has indicated robust behavior of the optimal solutions with respect to changes in the
values of the different parameters
Supply Chain - Chair: Yael Perlman
Yael Perlman, Elad Crispil
Shift scheduling of preventive maintenance for wafer fabrication plants
Preventive maintenance (PM) scheduling is crucial to the performance of wafer fabrication plants (fabs). This research
proposes a short-term scheduling model that seeks to minimize the cost incurred during a fabrication shift. The
schedule needs to fulfill the following conditions: (i) the shift’s required output, measured by work-in-process (WIP)
levels, must be achieved; (ii) PM must be carried out in compliance with the manufacturer’s recommended PM policy;
and (iii) the equipment must undergo cleaning operations, as recommended by the manufacturer, in a way that reduces
the production of blank wafers. A linear programming model is formulated, and a practical procedure is provided to
solve the PM scheduling problem. Data from a real fab are used in a case study to determine which equipment requires
maintenance in the upcoming shift and to provide a work plan that enables the remaining equipment to attain the target
production quantities. Results of what-if analysis demonstrate how fabrication managers can use the proposed model
to analyze different scheduling alternatives.
Keywords: Semiconductor manufacturing, Scheduling, Preventive Maintenance.
Yael Perlman, Yaacov Oz
Reducing shoplifting by investment in security
We consider a single retailer with a given potential revenue, who sells a product that is subject to shoplifting. In order
to decrease losses due to shoplifting and to maximize his profit, the retailer can invest in security measures. In
particular, we assume that the retailer purchases security services from a single security supplier. The security supplier
decides which price to charge the retailer for these services, with the purpose of maximizing his own profit, and the
retailer decides on the quantity of security services to purchase. We address this problem using a game theoretic
approach, where the retailer competes with the supplier—the leader—who specifies first the service price. The retailer
responds by deciding how much to invest in security. We study the conditions under which both players are profitable
and the extent to which double marginalization affects the supply chain performance.
Keywords: Supply Chain; Shoplifting; Security Pricing, Game Theory
M3
Scheduling 3 - Chair: Liron Yedidsion
Danny Heremelin, Reuven Bar-Yehuda and Dror Rawitz
Admission control in line topologies via rectangle graphs
In this talk we consider the following scenario: You want to schedule connection requests in a network which has the
line topology. Each connection request is between a pair of nodes in the network, and is for a specific time interval.
The assumption is that only a single pair of computers can use a connection line in the network, and so in a feasible
schedule no two requests should conflict on the network in the same time. Our goal is to reject as little requests as
possible so as to obtain a feasible schedule.
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We model this problem as the vertex cover problem in intersection graphs of axis-parallel rectangles on the plane. We
present two algorithms: The first is an EPTAS for non-crossing rectangle families, rectangle families where in every
pair of intersecting rectangles one rectangle contains a corner of the other. This algorithm extends to intersection
graphs of pseudo-disks. The second algorithm achieves a factor of (1.5 + epsilon) in general rectangle families, for any
fixed epsilon > 0, and works also for the weighted variant of the problem. Both algorithms exploit the plane properties
of axis-parallel rectangles in a novel way, and can be efficiently implemented.
Yossef Luzon, Yariv Marmor, Eugene Khmelnitsky
Optimal Control of a Flow-Shop Network
In this paper we suggest a new, intuitive and easy to use method for scheduling customers in a two server flow-shop
network with the maximum utilization objective. Multiple types of customers arrive to the network over a finite time
interval with corresponding time-varying arrival rates and constant service times. An analogue fluid network is
proposed and its optimal fluid control policy is determined. We make use of this optimal control policy to suggest a
new method for scheduling customers in the original discrete flow-shop network. Numerical simulations are used to
evaluate the performance of the suggested method and show that it performs optimally in almost all simulated
instances. Some additional properties of the network are discussed and illustrated.
Shay Hakim, Michael Masin, Michal Penn,
Asymptotically Optimal Approximation for the (High) Multiplicity Multi-Skill Scheduling Problem
The Multi-Skill Scheduling problem (MSP) is a generalization of job shop scheduling and resource-constrained
project scheduling (RCSPS) problems in which each activity has its known processing time and is processed by a
single resource from its subset of the capacitated resources. Each activity has its own subset of resources that can
execute it. The aim is to assign activities to resources and to order the activities on the resources so that the process
will terminate in the smallest amount of time under the precedence relations and the resource availabilities constraints.
MSP is NP-hard since RCSPS is known to be NP-hard and has many practical applications in the industry, service
applications and others.
We present the LP Synchronization Algorithm (LPSA) which is an LP-based asymptotically optimal approximation
scheme for the high multiplicity non-preemptive version of the problem. Our LPSA is a dispatching rule for the MSP
that imitates the fractional solution of the linear relaxation of a Mixed Integer Programing formulation of the problem.
Some numerical experiments will be presented as well.
Game Theory 2 - Chair: Arieh Gavious
Itai Arieli, Manuel Mueller-Frank
This paper considers single agent decision making under uncertainty and addresses the question to which degree the
private information of an agent is revealed through his optimal action. We show that if the set of actions is rich
(formally, a perfect set), then the agents optimal action reveals his posterior distribution for all but a negligible subset
(formally, a meager subset) of the space of continuous utility functions. If the action set is uncountable (not
necessarily perfect), then there exists a continuous utility function such that actions reveal beliefs. On the basis of the
single agent belief revelation result we establish information aggregation results in the sequential social learning
model and in the framework of repeated interaction in social networks
Ram Orzach, Ezra Einy, Ori Haimanko, Aner Sela
Common-Value All-Pay Auctions with Asymmetric Information
We study two-player common-value all-pay auctions with asymmetric information, assuming that one of the players
has an information advantage over his opponent. We characterize the unique equilibrium of this contest, and examine
the role of information on the players’ expected efforts, probabilities of winning, and expected payoffs. In particular,
we show that the players always have the same probability of winning the contest, their expected efforts are the same,
but they do not have the same expected payoffs. We also show that budget constraints may have non-trivial effect on
the players’ expected payoffs.
27
Amitay Kauffmann, Gal Zahavi
FEER Index – Forecasting Extreme Events Risk
In 2008, the S&P500 aggregated a loss of 30.16% during three selected days. Unfortunately, benchmark risk measures
didn't forecast these hazards. Consequently, we witness a growing interest in coherent risk measures, sensitive to high
moments and heavy tail risk. Such measures were proposed by Aumann-Serrano (2007) and Foster-Hart (2008). As a
generalization of these measures, we construct the FEER Index, coherent down-side risk measure "Forecasting
Extreme Events Risk", sensitive to heavy tail risk. We present closed-form solution as a function of the return
moments, VaR and CVaR. Furthermore, the FEER Index is dynamically calibrated to the market, becoming a live
seismograph for market catastrophes.
Queueing Networks in Production systems - Chair: Yoav Kerner
Ruth Shilman, Dean Grosbard, Gadi Rabinowitz, Israel Tirkel
Queueing Network Models for Manufacturing Systems with Downtimes
Queuing networks are frequently used to model the performance of complex manufacturing systems. Yet, existing
queuing network approximations cannot cope with some of the manufacturing lines’ complexities which frequently
generate non-renewable and highly variable internal flows. Scenarios of manufacturing systems with downtimes (e.g.
set-up, maintenance, and breakdown) present notable challenges. In this research, decomposition methods are used to
improve queuing network approximations suitable for such scenarios, with investigating the down-times' impact on
bottleneck queues.
We consider an open and tandem multi-class queuing network with class dependent service and routing. Each queue
has a single server, and an unlimited waiting line. Since an approximation of a multi-class system can be reduced to
the two-class case, where one class is the class of interest and the other class is an aggregated class of the rest of the
classes, two-class systems are considered. We assume that the aggregated class represents the down-time period.
Arrivals from the same class are processed following first-come-first-serve discipline, prioritizing the aggregated
class. We allow general distributions for inter-arrival and service times for the class of interest, but set deterministic
distributions for the inter-arrival and service times of the aggregated class. All distributions are assumed independent.
Our analysis illustrates that the departure variability can be expressed as the sum of two components. The first
component reflects the within-class effect while the second reflects the between-class interference effect,
demonstrating a novel approximation approach. The proposed development relies in addition on modifying the
variability functions for multi-class systems. The within-class effect variability function is class-dependent, while the
between-class variability function is a result of non-renewal processes. The proposed approximation has been
examined via simulation, for scenarios of multi-class systems with deterministic downtimes. For such scenarios, the
results demonstrate a relative error five times smaller than the best relevant existing procedures in the literature.
Shai Goren, Gad Rabinowitz and Yoav Kerner
A QBD approach for resources allocation
Allocation of limited resources in a stochastic production environment is a complicated task. Variation resources may
include production units’ inter-arrival times, production duration and quality, support resources availability, pace and
reliability, and more. This complexity drives practitioners to utilize static and simulation models as their main decision
support tools. The limitation of such model is being either naïve (static models) or high calculation resources
consuming (simulation models).
We suggest an analytical modeling approach to the resources allocation in a stochastic production environment. The
proposed model utilizes an integrative evaluation of the yield and flow time performance measures, as elements of an
objective function. Enabling yield evaluation and analysis is a significant needed extension of queueing theory.
We define and solve a typical production stage, which will become a node of a queueing network in future studies.
The stage consists of a single processing machine. The process deteriorates randomly from in-control to out-of-control
(with lower yield), and returns back to in-control only by repair. Repair is triggered via inspection of a recently
produced item. Inspection and repair resources are allocated to the stage according to an operational policy.
Production, inspection and repair are assumed to be non-perfect (i.e. not always successful), and their durations are
randomly distributed.
Since solving the general case is very difficult, we start with defining a relaxed model in which all of the durations
follow the exponential distribution, and the arrival process is Poissonian. This enables us to define the single stage as a
28
Quasi Birth and Death (QBD) process, and derive its steady state solution via matrix-geometric techniques. Yield and
FT are calculated as integrative outcomes of the inspection and repair resources allocation. The results of the
analytical model are validated against ones obtained through a simulation model. We also demonstrate how the single
stage model may be used as a building block for defining and solving multi-stage topologies.
Dean Grosbard
Rapid feasibility check for small size constrained capacity assignment problems
The problem of assigning server capacity to tasks under a given set of constraints is solvable using dynamic
programming. However, in production environment it is often the actual feasibility of the assignment problem under
the given constraints that is of more importance than the actual solution space. In this work we present a simple and
rapid method of checking the feasibility of a constrained capacity assignment problem. A set of sufficient and
necessary conditions for feasibility are presented and proven, we then go on to describe an algorithm for checking the
proposed conditions for small sized problems (N<20) followed by a network search algorithm for feasibility checks on
larger problems. Finally we discuss the importance of such feasibility checks in modern manufacturing systems and
present examples for their use.
Transportation 2 - Chair: Hillel Bar-Gera
Uri Yovel, Asaf Levin
Local Search Algorithms for Multiple-Depot Vehicle Routing and for Multiple Traveling Salesman Problems
with Proved Performance Guarantees
We consider two related problems: the Multiple-Depot Vehicle Routing Problem (MDVRP) and the Multiple
Traveling Salesman Problem (mTSP). In both of them, given is the complete graph on n vertices G=(V,E) with
nonnegative edge lengths that form a metric on V. Also given is a positive integer k. In typical applications, V
represents locations of customers and k represents the number of available vehicles. In MDVRP, we are also given a
set of k depots, and the goal is to find a minimum-length cycle cover of G of size k, that is, a collection of k (possibly
empty) cycles such that each vertex is in exactly one cycle, and each cycle in the cover contains exactly one depot. In
mTSP, no depots are given, so the goal is to find (any) minimum-length cycle cover of G of size k. We present local
search algorithms for both problems, and we prove that their approximation ratio is 2.
Michal Blumberg Nitzani, Hillel Bar-Gera
Representing signalized intersections in analytic dynamic traffic assignment model of transportation networks.
Traffic delays and queues at signalized intersections influence the flow of vehicles through the entire transportation
network and are responsible for main dynamic phenomena such as spillback, which is a state where a queue that spills
over to more upstream road sections. The estimation of signal delay time and the resulting queues are an essential part
of any dynamic traffic assignment model that aims to provide realistic traffic pattern predictions.
The analysis of traffic signal delay may focus on one of two states: over saturation - where the focus is on the dynamic
accumulation and dissipation of queues, and under saturation – where steady state conditions can be expected. In the
steady state analysis it is common to distinguish between a ’deterministic’ component of the delay, which represents
the delay caused to vehicles during the red phase of their arrival, and a ’random’ component of the delay which is due
to the variation in the number of vehicles arriving in each cycle. This variation leads to occasional overflow queues
(cycle failures) even in situations of under saturated intersections. To summarize, traffic signal delays have three
components: 1. steady-state deterministic component; 2. steady-state random component; and 3. dynamic queue
accumulation component.
We present an integrated framework to treat all three delay components within an analytic model that incorporates
Markov chain evaluation of the length of the queue at isolated fixed-cycle signalized intersections. Numerical
examples illustrate that overall the model behaves properly, and captures interesting impacts of random queuing traffic
signal delays on route choice and network level solutions.
Keywords: Solution stability; Signalized intersections; Dynamic traffic assignment; Dynamic network loading;
Embedded Markov chains.
29
Sarit Freund and Hillel Bar-Gera
An Optimization Framework for Travel Pattern Interpretation of Cellular Data
Collection of travel data by traditional survey methods is costly and time consuming, thus limiting the amount of data
being collected, as well as collection frequency and coverage.
Recent technologies offer new types of data collection options. In particular, cellular systems generate substantial
amounts of data, including records regarding the connection between handsets (phones) and base stations (antenna).
These records, collected by cellular service providers for various internal purposes, may provide an excellent source of
information regarding travel, with several critical advantages relative to traditional travel surveys: low cost, large
sample, long duration, and high response rate. Cellular data has some limitations too, particularly with respect to
accuracy and traveler identity; therefore, such data cannot provide a complete replacement for traditional surveys, but
it can complement and enhance them.
This study explores methods for identifying travel patterns from cellular data. The dataset used in this study includes
information regarding 9454 user cellular handsets, containing 3.7 M records, collected over 132 hours (nearly one
week). In a weekly perspective it is possible to observe substantial movements, when the handset user was probably
travelling, and periods of minor base station location changes, when the handset user was probably stationary,
participating in a certain activity. A primary challenge in this research is to provide an interpretation of the raw data
that distinguishes between activity durations and travel durations. A novel framework is proposed for this purpose,
based on a grading scheme for candidate interpretations of the raw data. A genetic algorithm is used to find
interpretations with high grades, which are considered as the most reasonable ones.
30
List of authors and participants (Session is indicated for active participants only)
Last Name Name Email Affiliation
Adler Nicole msnic@mscc.huji.ac.il Hebrew U. of Jerusalem T1 Tutorial
Adler Nicole msnic@mscc.huji.ac.il Hebrew U. of Jerusalem S3 Data Envelopment Analysis
Amador Sofia sofiamador@gmail.com Ben-Gurion U. of the Negev M1 Multi Agent Optimization
Anily Shoshana anily@post.tau.ac.il Tel Aviv U. S2 Game Theory 1
Anily Shoshana anily@post.tau.ac.il Tel Aviv U. P2 Plannery Lecture (Chair)
Arieli Itai iarieli@tx.technion.ac.il The Technion M3 Game Theory 2
Bar-Gera Hillel bargera@bgumail.bgu.ac.il Ben-Gurion U. of the Negev M3 Transportation 2
Baron Yonit ybarron@univ.haifa.ac.il U. of Haifa S3 Queueing 2
Beck Amir becka@tx.technion.ac.il The Technion S1 Continuous Optimization
Belsky Yarden yarden.belsky@gmail.com Tel Aviv U. S2 Multi Criteria optimization
Ben-Tal Aharon abental@ie.technion.ac.il The Technion P1 Naor Plenary Lecture (Chair)
Blumberg Nitzani Michal michal.nitzani@gmail.com Ben-Gurion U. of the Negev M3 Transportation 2
Bnaya Zahy zahy@bgu.ac.il Ben-Gurion U. of the Negev M1 Multi Agent Optimization
Bukchin Yossi bukchin@tau.ac.il Tel Aviv U.
Cohen Ilan ilanrcohen@gmail.com Tel Aviv U. S2 Scheduling 2
Cohen Tamar tamar.cg@gmail.com The Technion
Cohen Yaarit yaaritc@tx.technion.ac.il The Technion
Crispil Elad elad.crispil@gmail.com Intel M2 Supply Chain
David Yahel yahel83@gmail.com The Technion S2 Multi Criteria optimization
Drori Yoel dyoel@post.tau.ac.il Tel Aviv U. M2 Prize w inners
Eliazar Iddo eliazar@post.tau.ac.il Holon Institute of Technology T1 Tutorial
Forma Iris irisforma@gmail.com Tel Aviv U. S1 Transportation 1
Freund Sarit sarit.freund@gmail.com Ben-Gurion U. of the Negev M3 Transportation 2
Fruchter Gila Gila.Fruchter@biu.ac.il Bar Ilan U.
Gati Elad gatie@bgu.ac.il Ben-Gurion U. of the Negev
Gavious Arieh ariehg@bgu.ac.il Ben-Gurion U. of the Negev M3 Game Theory 2
Gerstl Tzvi tgerstl@nds.com Hebrew U. of Jerusalem S1 Scheduling 1
Gertz Tali tali.gertz@gmail.com The Technion
Gilenson Miri gilenson@tx.technion.ac.il The Technion
Gofer Eilam eylamg@rafael.co.il Rafael M1 Military OR
Goren Shai shaigoren100@gmail.com Ben-Gurion U. of the Negev M3 Queueing Netw orks in Production
Grinshpun Yonatan yoni_grinshpun@yahoo.com Rafael M1 Military OR
Grosbard Dean dean.grosbard11@gmail.com Intel M3 Queueing Netw orks in Production
Hakim Shay shayhakim@gmail.com The Technion M3 Scheduling 3
Halleli Sagi sagihilleli@gmail.com Ben-Gurion U. of the Negev S2 Scheduling 2
Halman Nir halman@mit.edu Hebrew U. of Jerusalem M1 Combinatorical Optimization
Hanany Eran hananye@post.tau.ac.il Tel Aviv U.
Hart Sergiu hart@huji.ac.il Hebrew U. of Jerusalem P2 Plannery Lecture (Speaker)
Haviv Moshe moshe.haviv@gmail.com Hebrew U. of Jerusalem M1 Strategic Behaviour in Queues
Heremelin Danny hermelin@bgu.ac.il Ben-Gurion U. of the Negev M3 Scheduling 3
Hochbaum Dorit hochbaum@ieor.berkeley.edu Berkeley U. P1 Naor Plenary Lecture (Speaker)
Holzman Ron holzman@tx.technion.ac.il The Technion T3 Tutorial
Karhi Shlomo shlomok@bgu.ac.il Ben-Gurion U. of the Negev S1 Scheduling 1
Kaspi Mor morkaspi@post.tau.ac.il Tel Aviv U. S1 Transportation 1
Kaspi Moshe moshe@bgumail.bgu.ac.il Ben-Gurion U. of the Negev S2 Multi Criteria optimization
Kauffman Amitai Zahavi.gal@gmail.com The Technion M3 Game Theory 2
Kerner Yoav kerneryo@bgu.ac.il Ben-Gurion U. of the Negev S3 Queueing 2
Kerner Yoav kerneryo@bgu.ac.il Ben-Gurion U. of the Negev M3 Queueing Netw orks in Production
Korach Efraim korachephraim@gmail.com Ben-Gurion U. of the Negev
Kremer Joseph kremer@bgumail.bgu.ac.il Ben-Gurion U. of the Negev S3 Simulation
Last Mark mlast@bgu.ac.il Ben-Gurion U. of the Negev M1 Combinatorical Optimization
Lev Varda varda@iec.co.il The Israel Electric Corp.
Levav Ben benlevav@gmail.com Rafael M1 Military OR
Levi Ofer levio123@gmail.com Ben-Gurion U. of the Negev M2 Applied Optimization
Lipets Vladimir lipets@il.ibm.com IBM S2 Scheduling 2
Luzon Yossi Yossi.luzon@gmail.com The Technion M3 Scheduling 3
Maimon Oded maimon@eng.tau.ac.il Tel Aviv U. Symposium
Session
31
Last Name Name Email Affiliation
Maman Shimrit shimritma@gmail.com Rafael
Mizrahi Shlomo mizrahi@bgu.ac.il Ben-Gurion U. of the Negev Symposium
Mor Baruch baruchm@ariel.ac.il Ariel U. S1 Scheduling 1
Mordoch Avraham mordoch@orange.net.il TOC Solutions S3 Heavy Scheduling Problems
Mosheiov Gur msomer@mscc.huji.ac.il Hebrew U. of Jerusalem S1 Scheduling 1
Nissim Raz raznissim@gmail.com Ben-Gurion U. of the Negev M1 Multi Agent Optimization
Nov Yuval yuval@stat.haifa.ac.il U. of Haifa S3 Queueing 2
Onn Shmuel onn@ie.technion.ac.il The Technion
Orzach Ram Orzach@oakland.edu Oakland U. M3 Game Theory 2
Oz Binyamin Binyamin.oz@gmail.com Hebrew U. of Jerusalem M1 Strategic Behaviour in Queues
Penn Michal mpenn@ie.technion.ac.il The Technion S2 Scheduling 2
Perel Efrat fgnaamati@gmail.com Tel Aviv U. S2 Queueing 1
Perel Nir perelnir@post.tau.ac.il Tel Aviv U. S2 Queueing 1
Perlman Yael yael.perlman@biu.ac.il Bar Ilan U. M2 Supply Chain
Pfeffer Aharona rona.pfeffer@gmail.com Tel Aviv U. S1 Transportation 1
Pinker Edieal edieal.pinker@yale.edu Yale U.
Pinto Gabi pintog@post.bgu.ac.il Azrieli college of eng. jerusalem S3 Heavy Scheduling Problems
Rabinowitz Gad rgadi@bgu.ac.il Ben-Gurion U. of the Negev Symposium
Raviv Tal talraviv@eng.tau.ac.il Tel Aviv U.
Ravner Liron lravner@gmail.com Hebrew U. of Jerusalem M1 Strategic Behaviour in Queues
Reif Barak Yair baraky1@post.tau.ac.il Tel Aviv U.
Reshef Offer offer@optum-ies.com Optum-Ies S3 Heavy Scheduling Problems
Roet-Green Ricky rgricky@gmail.com Tel Aviv U. M2 Prize w inners
Sabach Shoham ssabach@gmail.com Tel Aviv U. S1 Continuous Optimization
Sadeh Arik sadeh@hit.ac.il Holon Institute of Technology
Sarid Adi adi@sarid-ins.co.il Tel Aviv U.
Schechtman Edna ednas@bgu.ac.il Ben-Gurion U. of the Negev
Segev Ella ellasgv@bgu.ac.il Ben-Gurion U. of the Negev S2 Game Theory 1
Shaki Yair yair_sh@shoresh.org.il Ariel U.
Shefi Ron ronshefi@post.tau.ac.il Tel Aviv U. S1 Continuous Optimization
Sher Mali mali_sher@yahoo.com Israel Police
Shertzer Eliran scherzter@gmail.com Ben-Gurion U. of the Negev
Shilman Ruth ruthishilman@gmail.com Ben-Gurion U. of the Negev M3 Queueing Netw orks in Production
Shimkin Nahum Shimkin@ee.technion.ac.il The Technion S3 Simulation
Shpungin Yoseph yosefs@sce.ac.il Sami Shamun S3 Simulation
Shtern Shimrit shimrits@tx.technion.ac.il The Technion
Shtub Avraham shtub@ie.technion.ac.il The Technion T3 Tutorial
Sinuany-Stern Zilla zilla@bgu.ac.il Ben-Gurion U. of the Negev S3 Data Envelopment Analysis
Strimling David david.strimling@decisionaiding.com Decision Aiding S2 Multi Criteria optimization
Teboulle Mark teboulle@post.tau.ac.il Tel Aviv U. S1 Continuous Optimization
Tirkel Israel tirkel@bgu.ac.il Ben-Gurion U. of the Negev
Tzur Michal tzur@eng.tau.ac.il Tel Aviv U. S1 Transportation 1
Vaisbourd Yakov yakov.vaisbourd@gmail.com The Technion
Vaisman Slava slava@tx.technion.ac.il The Technion S3 Simulation
Volij Oscar ovolij@exchange.bgu.ac.il Ben-Gurion U. of the Negev S2 Game Theory 1
Yechiali Uri uriy@post.tau.ac.il Tel Aviv U. S2 Queueing 1
Yedidsion Liron yedidsion.liron@gmail.com The Technion M3 Scheduling 3
Yedidsion Liron yedidsion.liron@gmail.com The Technion M1 Combinatorial Optimization
Yitzhaki Shlomo shlomo.yitzhaki@huji.ac.il Hebrew U. of Jerusalem T2 Tutorial
Yitzhaki Shlomo shlomo.yitzhaki@huji.ac.il Hebrew U. of Jerusalem Symposium
Yovel Uri uyovel@hotmail.com The Technion M3 Transportation 2
Zahavi Jacob jacobz@tauex.tau.ac.il Tel Aviv U.
Zaks Yaniv zaksya@math.biu.ac.il Bar Ilan U. M2 Applied Optimization
Zivan Roie zivan.roie@gmail.com Ben-Gurion U. of the Negev M1 Multi Agent Optimization
Ziv-Av Amir zivava@mot.gov.il Ministry of Transportation Symposium
Zultan Ro'i zultan@bgu.ac.il Ben-Gurion U. of the Negev S2 Game Theory 1
Session
32
33
הכנס הרחובות בסמוך למלון
לתל אביב
לעיר העתיקה
מלון מלון
ליאונרדו ליאונרדו
נגבנגב
רכבת רכבת
אגדאגד מרכזמרכז
קניוןקניון
דוארדואר
קניוןקניון
טיילתטיילת
הקמפוס הקמפוס
הישן של הישן של
..גג..בב..אא
חניית חניית
מלון מלון הה
כספומטכספומט
34
האגודה הישראלית לחקר ביצועים
( ב"איל )
3102הכינוס השנתי
למאי 02 – 91
באר שבע, מלון ליאונרדו נגב
:בתמיכת
:הוועדה המארגנת
רבינוביץגדי יוסבאריה ג
אלה שגב יואב קרנר
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