emmanuel fernandez associate professor [email protected]

Emmanuel FernandezECECS Dept.Univ. Cincinnati

Emmanuel FernandezAssociate Professor

[email protected]


INTERESTS

Stochastic Models,Decision & Control

Processes,Dynamic Programming

TelecommunicationsInformation Technology

Operations & Logistics:

Semiconductor fabs

Basic Methodology

Algorithms,Software Tools


OVERVIEW

• Phase 1: 1990-1996: Learning and Adaptive Systems, Models with Partial Information, Average Optimality Criteria.

• Phase 2: 1994-1998: Non-standard Optimality Criteria, Modeling Applications, Algorithms & Software Tools.

• Phase 3: 1998-Present: Risk-Sensitive Models, Security & Fault Management in Telecommunication Networks, Operational Methods in Semiconductor Manufacturing.

• Over 61 refereed publications(6 b, 18+ j, 37 c)

• Four Ph.D.s, 3 M.Sc., 18+ undergrad. RA’s.

• Honors:– Tau Beta Pi Professor of the Year, David Rist Prize MORS, IEEE

Life Member Fund Research Initiation Award (Eng. Foundation).


OUTLINE

• Motivation: Applications

– Semiconductor manufacturing operations

– Logistics

– Information Networkso Fault & Security Management in communication networkso Routing in the Intelligent Network

• Stochastic Decision & Control Models:

• Optimality Criteria: Why Risk-Sensitivity?

• Basic Research Risk sensitive results:– Optimality equations & the Vanishing Discount Approach (AC).

– Modular functions & structured policies (DC).


APPLICATIONS

• Semiconductor Manufacturing:– Capacity expansion & allocation,

– Preventive maintenance scheduling (AMD).

• Information Networks:– Routing in the Intelligent Network (AT&T);

– Security & fault management..

• Operations & Logistics:– Workforce management;

– Scheduling military training resources (Army).


Semiconductor Manufacturing:Capacity Expansion & Allocation

• NSF/SRC Project at U. Maryland (PI’s: M. Fu & S. Marcus)

• EF Sabbatical project (begun Fall 98)

• EF liaison with industry (AMD) during 99

• Integrate transient product dynamics over entire fab life cycle: Markov Decision Process (MDP) models

– allocating/adding tool and process capacity – dynamic uncertain demands (e.g., market shifts)– transient dynamics (e.g., technology shrinks/shifts)

• Computational Investigation & Cost Modeling

• Tool: SYSCODE (University of Arizona software)– Stochastic Systems Control and Decision Algorithms Software Laboratory

• Find optimal policy for different parameters : – demand distribution – inventory cost and/or backlogging cost

• Simple policies vs. optimal policy

• Infinite horizon results vs. finite horizon

A Markov Decision Process Model for

Capacity Expansion and Allocation: IEEE Conf.

Decision & Control, 1999.


Industry Interaction:Advanced Micro Devices

• Joint effort UA & ISR

• On-site visits

• Preventive maintenance– Within allowed window,

when to do PM?

• Information Technology:– “Torrents” of information!– Inefficient “manual” methods– Do not use available information– No models

• Develop basic models & solution SRC/ISMT


Information Technology &Telecommunication Networks

• Routing calls in the Intelligent Network

• Security and Fault Management

• Software and Web tools:– SYSCODE

– Computations & MATLABWeb course.


The Intelligent Network:Routing Toll-free Calls (AT&T)

• AT&T - UA project

• Route 800- traffic tocall centers

• State information:– Workload at call centers

– Incomplete information

– Periodic updates

• Solution:– POMDP model

– Heuristic Policy Iteration Algorithm

R. Milito & E. Fernandez: (a) IEEE TAC 1995, (b) IEEE Conf. Decision & Control 1995


Information Networks: Security and Fault Management

• Joint project with M. Shayman, U. Maryland.

• Searching for faults in a given domain: – Scheduling tests

• Single/Multiple faults

• Test sequence constraints

• Risk-sensitive criterion

• Interchange argument: – Explicit scheduling rules

• Qualitative analysis

• Security intrusions: – Similar to fault management •1999 Allerton Conference

•IEEE TAC 2001•Proposals


Operations & Logistics: Scheduling Army Training Resources

• LTC M. McGinnis: Ph.D. UA

• Thousands of recruits/year

• Many installations/bases

• Decisions:– Company size

– Length of training period

– Number of companies to activate/retire each week.

• Model: Inventory-type

• Solution: Heuristic Policy Iteration Algorithm

• Decision support software (in use by Army).

Journal Military Op. Res. 1996(Winner of David Rist Prize)


Operations & Logistics: Scheduling Army Training Resources


Logistics: Workforce Management

• Recruit-retain-dismiss individuals

• Intrinsic individual’s potential– Unobservable state

• Random productivity– Bayesian stochastic model

• The firm’s lifetime is long:– Average cost criterion

• Adaptive control through Bayesian learning

• Qualitative analysis of case studies

Fdez, Jain, Lee, Rao, Rao: Management Science 1995.

emmanuel fernandez associate professor [email protected]

Documents