data pemsis
DESCRIPTION
Pemodelan SistemTRANSCRIPT
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Tahun Judul Nama
2006 Xin Li
2008
2007
2002
Multi-agent based modeling and analysis of collaboration strategies in supply chain
Agent.Hospital – Health Care Applications of Intelligent Agents1
Stefan Kirn, Christian Anhalt, Helmut Krcmar, Andreas Schweiger
Collaborative Agent-Based Negotiation in Supply Chain Planning Using Multi Behavior Agents
Pascal Forget, thibaud monteiro, sophie D'amours dan jean-marc frayret
agent-based simulation for distributed supply chain planning: conceptual modeling, analysis and illustration
Luis-Antonio Santa-Eulalia, Jean-Marc Frayret, Sophie D'Amours
Agent-based supply chain management*/1: framework
Nirupam Julka 1, Rajagopalan Srinivasan *, I. Karimi
2006
2003
2004
AGENT-BASED MODELING OF SUPPLY CHAINS FOR DISTRIBUTEDSCHEDULING
Jason LAU, George Q Huang (Corresponding Author) and K L Mak
Emergent Structures in Supply Chains—A Study Integrating Agent-Based and System Dynamics Modeling
Nadine Schieritz and Andreas Größler
Agent-Based Framework for Dynamic Supply ChainConfiguration
Denise Emerson, Selwyn Piramuthu
2011
2011 AGENT BASED SIMULATION OUTPUT ANALYSIS
2003 Agent-Based Applications in Health Care
AGENT BASED SIMULATION DESIGN FOR AGGREGATION AND DISAGGREGATION
Tiffany J. HarperJohn O. MillerRaymond R. Hill, J. Robert Wirthlin
Lee SchrubenDashi Singham
John Nealon1 and Antonio Moreno2
2012 Agent-based Model of Parcel Logistics Jirí Starý
2010
AGENT-BASED SUPPLY CHAIN SIMULATION: TOWARDS ANORGANIZATION-ORIENTED METHODOLOGICAL FRAMEWORK
Karam MUSTAPHA, Erwan TRANVOUEZ, Bernard ESPINASSE, Alain FERRARINI
An Agent-based framework for cooperation in SupplyChain
Benaissa Ezzeddine, Benabdelhafid Abdellatif, Benaissa Mounir
2012
2009 R. Brent Ross, PhD
Sharing Breakdown Information in Supply Chain Systems:An Agent-Based Modelling Approach
Yasser Ibrahim1, 2 Ghada Deghedi2*
Entrepreneurial Behavior in Agri-Food Supply Chains:The Role of Supply Chain Partners
2012 Dimitris Kremmydas
Agent based modeling foragricultural policy evaluation: Areview
An Agent Based Model for the Evolving Supply Chain of Jatropha Biofuel
Ahu Soylu, Derek Bunn, William McKenzie
An Agent-based Simulation System for Concert-event Emergency Management Support
2010
An Agent-Based Formal Framework forModeling and Simulating Supply Chains
Li Tan1, Shenghan Xu2, Benjamin Meyer1, and Brock Erwin1
Agent-based simulation of competitive and collaborative mechanismsfor mobile service chains
Guoyin Jiang a,b, Bin Hu a,*, Youtian Wang a
2008
2001 Dr. Thomas Berger
2008
Agent-based Discrete Event Simulation Modeling forDisaster Responses
Shengnan Wu, Larry Shuman, Bopaya Bidanda, Matthew Kelley, Ken Sochats, Carey Balaban
Dissecting and Understanding Supply Chainsthrough Simulation: an Agent-based Approach
Gian Paolo Jesi1 and Guido Fioretti1
Agent-based Spatial Models Applied to Agriculture:A Simulation Tool for Technology Diffusion, Resource Use Changes and Policy Analysis
Toward a conceptual agent-based framework for modeling and simulation of distributed healthcare delivery systems
Moez Charfeddine, Benoit Montreuil
2009
2012
2005
An agent-based retail location model on a supply chainnetwork
Arthur Huang and David Levinson†
Agent-based simuation to anticipate impacts of tactical supply chain decision-Making in the lumber industry
Sebastirn Lamieux, Sophie D'amours, Jonatham Gaudreault, Jean-Marc Frayret
MODELING FOOD SUPPLY CHAINS USING MULTI-AGENT SIMULATION
Caroline C. KrejciBenita M. Beamon
multi-agent modelling for simulation of customer-centric supply chain
Oliver Labarthe, Alain Ferrarini, Bernard Espinasse dan Benoit Montreuil
2008
Design and Optimization of Agent-Based Emergency SupplyChains
Ozlem Ergun, Pinar Keskinocak, and Julie L. Swann
Exploring policy interventions for rural sustainabledevelopment
Ruud Kempener, Peter Kaufmann, Sigrid Stagl
2012 Jakub Dyntar1, Jan Škvor2
Integrated Multi-agent-based Supply Chain Management
This paper appears in:Enabling Technologies: Infrastructure for Collaborative Enterprises, 2003. WET ICE 2003. Proceedings. Twelfth IEEE International Workshops onDate of Conference: 9-11 June 2003Author(s): Frey, D. Inst. of Process Control & Robotics, Univ. Karlsruhe, Germany Woelk, P.-O.; Stockheim, T.; Zimmermann, R. Page(s): 24 - 29
AN AGENT-BASED COLLABORATIVE MODEL FOR SUPPLY CHAINMANAGEMENT SIMULATION
C. M. Vieira, A. P. Barbosa-Póvoa, C. Martinho
Oil Refinery Supply Chain Modelling Using Pipe TransportationSimulator
2012 Akansha Mishra, Ramaa.A
2008 Güven Demirel
2007
Agent Based Simulation for Measuring Performance of Supply chain network – A case study of Cable wires Manufacturing Company
SUPPLY CHAIN MODELING AND ANALYSIS AT ALTERNATIVE LEVELS OF AGGREGATION
An agent-based approach for supply chain retrofitting under uncertain
Fernando D. Mele, Gonzalo Guill´en, Antonio Espu˜na, Luis Puigjaner ∗
Modeling of supply chain: a multi-agent approach
2005
Surya Dev Pathak, Greg Nordstrom, Susumu Kurokawa
Robust Agent-based Optimisation for Supply Chain Configuration to Copewith Risks and Uncertainties
Zheng Ren and David Z Zhang
WILLINGNESS TO COOPERATE IN THE SUPPLY CHAINA PRELIMINARY AGENT-BASED MODELING APPROACH
Timea TörökJon H. Hanf
A novel combined approach for supply chain modeling and analysis
Fernando D. Mele, Carlos A.Méndez, Antonio Espuña, Luis Puigjaner
2010 M. S. Uppin
Distributed Agent-Based Air Traffic Flow Management
Multi Agent System Model of SupplyChain for Information Sharing
SCAMM-CPA: A supply chain agent-basedmodelling methodology that supports acollaborative planning process
Jorge E. Hernández*, M.M.E. Alemany, Francisco C. Lario & Raúl Poler
Kagan Tumer, Adrian Agogino
2009
A Multi-Agent Simulation of Collaborative Air Traffic FlowManagement
Shawn R. Wolfe Peter A. JarvisFrancis Y. EnomotoMaarten SierhuisBart-Jan van PuttenKapil S. Sheth
Agent-Based Modeling and Simulation of Collaborative Air Traffic FlowManagement Using Brahms
Peter A. Jarvis1, Shawn R. Wolfe, Maarten Sierhuis2, Robert A. Nado3, Francis Y. EnomotoNASA Ames Research Center, Moffett Field, CA 94035
ANALYZING AIR TRAFFIC MANAGEMENT SYSTEMS USINGAGENT-BASED MODELING AND SIMULATION
A.P. Shah, A.R. Pritchett, K.M. Feigh and S.A. Kalaver, A. Jadhav and K.M. Corker, D.M. Holl and R.C. Bea,
Agent-Based Modeling of Culture’sConsequences for Trade
2007 Adaptive Planning for Supply Chain Networks
2010 Yee Ming Chen
Michael Andreev , George Rzevski , Petr Skobelev , Peter Shveykin , Er Tsarev , Andrew Tugashev
Improving Supply Chain Coordination by Linking Dynamic ProcurementDecision to Multi-Agent System
2007What differentiates a winning agent: An information gain based analysis of TAC-SCM
James Andrews , Michael Benisch , Alberto Sardinha , Norman Sadeh
Using Information Gain to Analyze and Fine Tune the Performance of Supply Chain Trading Agents
James Andrews , Michael Benisch , Alberto Sardinha , Norman Sadeh
2006
2005
The CrocodileAgent: research for efficient agent-based cross-enterprise processes”
Vedran Podobnik , Ana Petric , Gordan Jezic
An analysis of the 2004 supply chain management trading agent competition
Christopher Kiekintveld , Yevgeniy Vorobeychik , Michael P. Wellman
Keterangan
Cirrelt-54
Cirrelt-11
share information, pake 4 skenario investigasi untuk mengukur inventory cost dan cust servide levels
Multiagent EngineeringInternational Handbooks on Information Systems 2006, pp 199-220\
In SPP 1083 the Hospital Logistics group studies the applicability of agent-based information systemsin health care business scenarios by identifying problems, analyzing requirements, elaborating the state of the artof conventional and agent-based systems, specifying and designing multiagent applications, and evaluating theirapplication. This chapter includes a survey of both the projects forming the group and their collaboration in orderto integrate the systems designed by them into the agent testbed named Agent.Hospital.
automated negotiation, simulation, lumber industry
advanced planning and scheduling, lumber suplly chain
elsvier, Computers and Chemical Engineering 26 (2002) 1755 /1769
Software agents; Grafcets, entreprise supply chain
This paper appears in:Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions onDate of Publication: Sept. 2006Author(s): Lau, J.S.K. Dept. of Ind. & Manuf. Syst. Eng., Univ. of Hong Kong Huang, G.Q.; Mak, K.L.; Liang, L. Volume: 36 , Issue: 5 Page(s): 847 - 861
Project scheduling, Agent-based modeling, Distributed scheduling,Information sharing,
Proceedings of the 36th Hawaii International Conference on System Sciences
An integration of systemdynamics and discrete agent-based modeling is apromising combination of methods for reducing the apriori complexity of the model. The paper discusses thestrengths and weaknesses of system dynamics anddiscrete agent-based modeling. An approach forintegration of the two modeling methodologies ispresented.
Proceedings of the 37th Hawaii International Conference on System Sciences
In this paper we develop a framework,with machine learning, for automated supply chain configuration
Proceedings of the 2011 Winter Simulation ConferenceS. Jain, R.R. Creasey, J. Himmelspach, K.P. White, and M.
This work ties together a general framework for using ABMS for supply chain risk management, which includes the use of software agents, for data mining, integrated with agent-based simulation platforms.
Proceedings of the 2011 Winter Simulation Conference
In most realistic simulations there are multiple outputs of interest and the overall performance of the sys-tem can only be estimated in terms of these multiple outputs. We propose a method that uses agent-based modeling to determine a truncation point to remove significant initialization bias. Mapping the output of multiple replications into agent paths that traverse the sample space helps determine when a near steady state has been reached.
in Applications of Software Agent Technology in the Health Care Domain , Whitestein Series in Software Agent Technologies, Birkhäuser Verlag
A distributed decision support system based onthe multi-agent paradigm can monitor the status of a hospitalised patientand help to diagnose the state of the patient [28], or support co-operativemedical decision- making [29], [30].
Master’s Thesis, Czech Technical University in PragueFaculty of Electrical EngineeringDepartment of Cybernetics
Inside,the vehicles move on a graph representation of the streets. Using the A* planner andwith the estimated distance as a cost, they compete for the deliveries. Later, on the cityof Prague, several allocation strategies are attempted, while an insight into the interactionsbetween agents is gained and the results discussed. Two variants of the allocation algorithmwere measured, each performing better under different conditions and metrics.
8th International Conference of Modeling and Simulation - MOSIM’10
a new methodologicalframework, organizationally oriented, which permits modeling and simulation of such SC organizational aspects, allowingobservables of different levels of details while reproducing the SC behavior according to desired observables.
Multi-Agent System, Cooperation, Ontologies,Supply Chain, Semantic Web Services, intelligent agents.
Information and Knowledge Management www.iiste.orgISSN 2224-5758 (Paper) ISSN 2224-896X (Online)Vol 2, No.4, 2012
Reverse Information Sharing, Beer Game, Agent-Based Modeling, Supply Chain Risk.
Submitted for consideration to the 19th Annual World Forum and Symposium of theInternational Food and Agribusiness Management Association in Budapest, Hungary
AUA Working Paper Series No.2012-3
Agent based modeling, Agricultural policy evaluation, Agripolis, Reg-MAS,MP-MAS, SWISSland
Plantations of Jatropha and production of biofuel can create new job opportunities and an economic resource for people living in subsistence areas and these places can greatly benefit from further development of Jatropha. However, its success will depend on construction of a successful infrastructure for its supply chain.
Modeling and Simulation, Agent-based System, Emergency Management, Disaster Mitigation
This paper appears in:Information Reuse & Integration, 2009. IRI '09. IEEE International Conference onDate of Conference: 10-12 Aug. 2009Author(s): Li Tan Sch. of Electr. Eng. & Comput. Sci., Washington State Univ., Richland, WA, USA Shenghan Xu; Meyer, B.; Erwin, B. Page(s): 224 - 229 Product Type: Conference Publications
elsevier, Information Sciences 180 (2010) 225–240
cirrelt-09
Proceedings of the 2008 Industrial Engineering Research Conference
Emergency response, agent-based simulation, discrete event simulation, geographical information system, rulebasedsystem
In this ongoing work, we focus on supply chain; in particular, as ourultimate goal, we focus on food mass purchasing consortium (MPC)market because of the availability of literature and data on this speci ctopic.
Agricultural Economics 25 (2/3),245-260.
spatial multi-agent programming model which has been developed forassessing policy options in the diffusion of innovations and resource use changes
patient flow modelling, health service, multi-agent systems, simulation, modelling framework and methodology, distributed healtcare delivery systems, logistics
Cirrelt-51
clustering, supply chain network, location choice, distribution pattern
distributed simulation, value creation network, advanced planning system, multi agent, lumber
Proceedings of the 2012 Winter Simulation Conference
In light of the pressures of increasing demands on earth’s resources, society faces serious challenges infood production and distribution. Food supply chain (FSC) models are critically important, providing decision-makers with tools that allow for the evaluation and design of FSCs, en route to ensuring sustainableFSC productivity. Multi-agent simulation (MAS) is well-suited to modeling FSCs for this purpose,enabling capture of decision-making, interactions, and adaptations of autonomous FSC actors.
International Journal of Simulation and Process Modelling
customer-centric supply chain, multi agent, agents-oriented simulation
http://nexus.umn.edu/Papers/Cluster.pdf Paper provided by University of Minnesota: Nexus Research Group in its series Working Papers with number 000037.
Emergency Management (EM)organizations need to have highly functioning logistics networks as key towards supporting andaiding a®ected populations in disasters or con°icts.
rural development, agent-based modeling, scenario analysis, supplychains, diversity
The aim of the methodology is to contribute to theanalysis, evaluation and development of robust policy instruments thatimprove and stimulate rural sustainable development over a range ofpotential future scenarios.
Daniel Frey, Peer-Oliver Woelk, Tim Stockheim, Roland Zimmermann
Their structure inherently meets the requirementsof decentralised supply chains, whereas conventionalSCM systems are often restricted in terms of dynamicbehaviour, handling severe disturbances at supplier sitesas well as dealing with highly customised or complexproducts.
Supply Chain Management, Multi-Agent System,Simulation.
IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 2, No 1,
Discrete Event Simulation, Supply ChainManagement, Oil Refinery Supply Chain, Agent-Based Modelling.
ISCIAgent Based Modeling, Supply chain, cable manufacturing
Master of Science, Industrial Engineering, Boğaziçi University
The analysis focuses especially on inventory/order variability and the bullwhip effect, which is the increase in variability as one moves up in a supply chain. On the other hand, demand forecasting, order batching, and dynamic pricing are shown to be the causes of variability and bullwhip effect in aggregate orders and inventories.
elsevier, Computers and Chemical Engineering 31 (2007) 722–735
Supply chain management; Uncertainty; Multi-agent system
Elsevier
development of an agent-based software system for assisting in decision-making regarding supply chain management and the efficient and effective use of Electronic Data Interchange (EDI) in the automobile industry.
Supply chain configuration optimisation, supply chain risks and uncertainties, multi-objectiveoptimisation, robustness in supply chains
Leibniz Institute for Agricultural Development in Central and Eastern EuropeTheodor-Lieser-Str. 206120 Halle (Saale)Germany
Supply Chain Management, software agents, mixed-integer linear programming.
innovar
Contemporary Engineering Sciences, Vol. 3, 2010, no. 1, 1 - 16
Supply chain, Agent Technology, Multi Agent System and Informationsharing
multi-agent system (MAS), collaborative planning (CP), collaborative operational planning (COP), modelling methodology, supplychain management (SCM), distribution and supply chains and networks (DSC-N), literature review
Air Traffic Control, Multiagent Systems, Reinforcement Learning, Optimization
SAE International
NASA Ames Research CenterUnited States of America
agent-based modeling and simulation, air traffic flow management,collaboration, competition
In this paper, wedescribe an agent-based simulation of the newconcept of operations and our plannedexperimentation to determine if the newconcept of operations will lead to betterutilization of the national airspace.
As a result, this paper proposesagent-based simulation as a method of predicting theimpact of revolutionary changes to an airtransportation system. Agent based simulation canintegrate cognitive models of human performance,physical models of technology behavior anddescription of their operating environment.
Abstract. This paper describes methodology, toolset and case studies of adaptive planning for supply chain networks based on the holistic approach, multi-agent technology and ontological modeling. The set of tools for the development of adaptive planners can be used for a wide range of applications. Case studies are included describing applications inr adaptive airport logistics, factory planning, laundry scheduling and pharmaceutical logistics.
JOURNAL OF COMPUTING, VOLUME 2, ISSUE 4, APRIL 2010, ISSN 2151-9617
—Multi-Agent System、Bullwhip effect、 Fill rate 、NYOP(Name Your Own Price)
In Proceedings of Trading Agent Design and Analysis Workshop
During the course of each year’s competition historical data is logged describing more than 800 games played by different agents from around the world. In this paper, we present analysis that is focused on determining which features of agent behavior, such as the average lead time requested for supplies or the average selling price offered on finished products, tend to differentiate agents that win from those that do not. We present a visual inspection of data from 16 games played in one bracket of the 2006 TAC SCM semi-final rounds. Plots of data from these games help isolate behavioral features that distinguish top performing agents in this bracket.
Lecture Notes in Computer Science
The Trading Agent Competition (TAC) is an international forum which promotes high-quality research regarding the trading agent problem. One of the TAC competitive scenarios is Supply Chain Management (SCM) where six agents compete by buying components, assembling PCs from these components and selling the manufactured PCs to customers. The idea of TAC SCM is the development of agent-based architectures that implement wideapplicable business strategies which efficiently manage cross-enterprise processes. In this paper, we analyze the TAC SCM environment and describe the main features of the CrocodileAgent, our entry in the TAC SCM Competition.
In IJCAI 2005 Workshop on Trading Agent Design and Analysis
Goal Area
Healthcare
mengetahui behavior negosiasi manufacturing
manufaktur
manufacturing
investigate information sharing as a basic supply chain collaboration strategy through the application of the multi agent approach to model and simulate the supply chain sehingga dapat mereduce bullwhip effect dan menurunkan inventory
The main goal is the construction of an agent-based simulation system that simulates processesrelevant to the implementation of clinical trials [HHPA2003].
investigating this aspect of supply chain planning through the use of theoretical contributions found in the field of simulation, system theory and distributed decision making
The framework helps to analyze the business policies with respect to differentsituations arising in the supply chain. We illustrate the framework by means of two case studies
automated supply chain
In order to maketheoretical investigations of supply chains feasible andto support decision-making in real world supply chains,simulation models are used.
This is primarily due tothe availability of timely information across the various stages ofthe supply chain, and therefore the need to effectively utilize theinformation for improved performance -----
Aircraft Supply Chain Example
This paper proposes a framework for designing an agent based simulation to allow for easy aggregation and/or disaggregation of agent characteristics, behaviors, and interactions using a supply chain modeling context.
By viewing these paths in reversed time, qualitative and quantitative methods can be used to determine when the multivariate output is leaving its near-steady state regime as the paths coa-lesce back towards their common initialization state. The methodology is more efficient and general than typical approaches for finding a truncation point for scalar outputs of individual replicates. Artificial bootstrap-like re-sampling of simulation runs is proposed for expensive simulations to estimate system performance sensitivity.
The knowledge required to solve the problem is spatially distributed in differentlocations.
Vehicle
manufacturing
The purpose of this work is to leverage the agent-based modeling onto the process of parceldelivery. F
Our current work aims to take into account the impact anSC’s organizational structure has on its performances byproviding a methodological framework which supportranges from the domain model analysis to running thesimulation.
We propose in particular solutionsthat focus on cooperation between actors in the Supply Chain.
beergame
agri-food system
agent-based representation of the Beer Game Model (BGM) is used to demonstratehow disruptions, occurring to the factory of a SC, can negatively affect its overall performance, and how sharing thefactory disruption information can effectively help blocking the evolution of risk in the SC and improve itsperformance. The BGM is extended in this research to include two factories. The concept of Reverse InformationSharing (RIS) is introduced as a mechanism for sharing the breakdown information. The results show a significantreduction in the cost of the SC and each of its agents due to the RIS. In addition, the analysis shows that the RISsignificance is getting larger with the increase of the disruption frequency.
The objective of this study is to investigate the role of supply chain partners inthe entrepreneurial process. In particular, we will examine the differences inentrepreneurial performance between firms that discover and exploit new wealthcreation opportunities within existing supply chains as opposed to those that decide toestablish the supply chain themselves. In doing so, we introduce an agent-basedmodel that explicitly simulates entrepreneurial discovery, rent appropriation and thedissipation of those rents via competition and resource depletion in the presence ofsupply chain partners.
agriculture
energy
In this paper we initially do a short presentation of the principles of modeling economicsystems with the ABM approach quoting its features, the advantages and disadvantages.
The main aim in supply chain management is to satisfy production requirements, while optimizing the economic objectives. In traditional fossil fuel supply chains, huge amounts of fossil fuels are transported via pipelines or tankers with very small costs. These fuels can be transformed into other sources of energy or transportation fuels at their destination points. This supply chain structure results in creation of global energy markets and has made the fossil fuel based energy systems the dominating energy technologies in the world. Unfortunately, the consumption of fossil fuels now represents the major cause of climate change, and as a consequence, the viability of the fossil fuel supply chain is becoming increasingly questioned.
This paper presents aprototype of a computer simulation system that uses agent-based modeling to simulate an emergencyenvironment with crowd evacuation and provides for testing of multiple disaster scenarios at virtually nocost. The prototype is unique in the current literature as it is designed to simulate a concert-event settingsuch as a stadium or auditorium and is highly configurable allowing for user definition of twodimensionalenclosed areas with any arrangement of seats, pathways, stages, exits, and people as well asthe definition of multiple fire/bombs with fire and smoke dynamics included.
To improveextensibility, a distinctive feature of our approach is thatit separates the functionalities of an element from its roleand handles interactions among elements in an agent-basedframework: elements are modeled as agents and their interactionsdecide the behavior of a supply chain. Our frameworkprovides formal definitions for the syntax and semanticsof an element. The framework separates internal behaviorsof an element from its interface. These features make iteasier to define new types of elements and customize theirbehaviors for a variety of supply-chain applications.
A new paradigm for a mobile service chain’s competitive and collaborative mechanism isproposed in this study.
Byanalyzing the expectations and variances (or risks) of each player’s profit, the interactionbetween and among entities in the chain is well understood. It is found that in the situationwhere a collaborative mechanism is applied, the performance of players is better as comparedto the other two situations where a competitive mechanism is implemented. If someconstraints are applied, the risk will be kept at a low level.
food
We develop a comprehensive simulation model for civilian emergency/disaster responses. In order to incorporate thedetailed interactions among various responders and victims, we integrate the agent-based modeling ideas into thediscrete-event simulation framework and hybrid several components such as GIS.
simulasi disaster di pitterseiburg
We propose an agent based modeling (ABM) approach to study andidentify the supply chain weaknesses by modeling its structural evolu-tion over time. Our approach can provide answers to the previous ques-tions. We believe this approach is promising since it could represent avaluable tool in (i) understanding supply chain processes and (ii) in test-ing social, economical and political strategies in order to achieve bettersustainability models.
Simulationresults show that agent-based spatial modelling constitutes a powerful approach to betterunderstanding processes of innovation and resource use change.
softwood lumber industry
golf club industry
This paper investigates the emergence of clusters of business locations ona supply chain network comprised of suppliers, retailers, and, consumers.
This research thus finds that the centripetalforce attracts retailers to supplier locations; with even more retailers entering the market, thecentrifugal force disperses them. The sensitivity results on model parameters and consumers’demand elasticity are also discussed.
making the integaration of an advanced planning and scheduling system in multi-agent based simulation in lumber industry
However,certain characteristics of FSCs are particularly difficult to model in detail, as data requirements can be intensive.In this paper we highlight some of the challenges modelers face in deciding the most appropriatemethods for representing the elements of an FSC in an MAS model. We provide examples from the literaturethat show how other modelers have chosen to address these challenges. Finally, we discuss benefitsand limitations of each example’s approach, in terms of realism and data requirements.
estimasi demand per market per personalization so allows a satisfactory representation of a structure and behavior of SC
Optimization has made immense strides towards improving logistics costs and services. How-ever, most optimization has been designed to operate in a centralized environment, while manysystems actually operate in a decentralized way, with individuals making decisions that impactthe entire system, especially in emergency management. EM operations can be linked with mobilecomputing, but new methods are needed to operate across the decentralized network.
The Home Depot and Wa²e House for hurricanes and other disasters; supplychain design and interventions for in°uenza pandemic in Georgia and with the American Red Cross;and debris collection post-disaster with FEMA and USACE.
Common AgriculturalPolicy (CAP) increasingly place agriculture in a wider multifunctionalcontext taking into account their role in the rural economy, the qualityof the environment and food safety. This expanded perspective requiresan understanding of agricultural activities beyond farming, includingfood and energy processing, transport, retail and other land use relatedactivities like tourism.
MANUFACTURING
Since necessary data are not available withinthe whole supply chain, an integrated approach forproduction planning and control taking into account allthe partners involved is not feasible. In this paper a MASarchitecture integrating various intelligent agent systemsis presented to address the problem.
COMPARING EXISTING SCM DENGAN MAS IN manufacturer of agricultural equipment
The proposed model allows modellingdifferent SCs with multi-products and differentoperational policies considering information asymmetryand distributed/decentralized mode of control.
The aim of this paper is to describe an application of PipeTransportation Simulator (PTS) in oil refinery supplychain modelling.
manufacturing
MANUFACTURING
manufacturing
This paper proposes a supply chain management model allowing performance measurement in a cable wire manufacturing company. This paper specifically highlights the components and key parameters involved in the same and define the performance indicators. The proposed work focuses in showcasing the multiple agent interactions in the cable wire making industry and benefits of operating supply chains as an integral part of the manufacturing enterprise. It also discusses the importance of information sharing for the effective functioning of supply chains and multi- agent interactions.
The proposed methodology allows to address the design of complex SCs which are hard to be modelled otherwise, for example by means ofstandard mathematical programming tools. Specifically, the multi-agent system is suitable for SCs that are either driven by pull strategies or operateunder uncertain environments, in which the mathematical programming approaches are likely to be inferior due to the high computational effortrequired. The advantages of our approach are highlighted through a case study comprising several plants, warehouses and retailers.
manufacturing
agri-food business
manufacturing
We are developing a MIC-based supply chain management-modeling environment. This environment will allow domain experts to create models of the software agents to simulate, and control, the actual on-line negotiation processes. The modeling environment will allow modeling of agent behavior, as well as defining agent-to-agent interaction scenarios.
This paper examines the risks and uncertainties in supplychains and identifies the key factors included in supply chain optimisation. The paper also proposes amethodology to introduce the key risks and uncertainties into modelling in an agent-based supply chainoptimisation algorithm, with the aim of minimising cost and maximising reliability and robustness.
The aim of the paperis to introduce ideas regarding the ABMS implementation and to build a model which capturesthe change of willingness to cooperate within a first simplified supply chain model of anagricultural market.
The hybrid approach proposed in this work offers the advantages of the multi-agent system to model the SC together with the optimization capabilities of local not so large mathematical programming models to solve in an efficient manner the decision problems that the central agent faces along one simulation run. The results so far obtained are very promising.
The proposed work focus on the significance and benefits of operating supply chainsas an integral part of the modern manufacturing enterprises and also the importanceof information sharing as the major requirement for the effective functioning ofsupply chains.
Finding reliable and adaptivesolutions to the flow management problem is of paramountimportance if the Next Generation Air Transportation Systemsare to achieve the stated goal of accommodating threetimes the current traffic volume. This problem is particularlycomplex as it requires the integration and/or coordinationof many factors including: new data (e.g., changingweather info), potentially conflicting priorities (e.g., differentairlines), limited resources (e.g., air traffic controllers)and very heavy traffic volume (e.g., over 40,000 flights overthe US airspace).
Our FACET based results show that agents receivingpersonalized rewards reduce congestion by up to 45%over agents receiving a global reward and by up to 67% overa current industry approach (Monte Carlo estimation).
Wesummarize a new concept that has been proposed for collaborative air traffic flowmanagement, the problems it is meant to address, and our approach to evaluating theconcept. We present our initial simulation design and experimental results, using severalsimple route selection strategies and traffic flow management approaches.
mengantar penumpang dalam pesawat
Case studies are included describing applications inr adaptive airport logistics, factory planning, laundry scheduling and pharmaceutical logistics.
Using NYOP(Name Your Own Price) to be the core of dynamic procurement negotiation algorithm sets up multi-agent dynamic supply chain system, to present the DSINs(Dynamic Supply Chain Information Networks) by JADE, and to present the dynamic supply chain logistic simulation by eM-Plant. Finally, evaluating supply chain performance with supply chain performance metrics (such as bullwhip, fill rate), to be the reference of enterprise making deciding in the future.
The Supply Chain Trading Agent Competition (TAC SCM) was designed to explore approaches to dynamic supply chain trading. During the course of each year’s competition historical data is logged describing more than 800 games played by different agents from around the world. In this paper, we present analysis that is focused on determining which features of agent behavior, such as average lead time or selling price, tend to differentiate agents that win from those that don’t. We begin with a visual inspection of games from one bracket of the 2006 semi-final rounds. Plots from these games allow us to isolate behavioral features which do, in fact, distinguish top performing agents in this bracket. We introduce an information gain based metric that we use to provide a more complete analysis of all the games from the 2006 quarter-final, semi-final and final rounds.
Using this metric we find that, in the final rounds of the 2006 competition, winning agents distinguished themselves by their procurement decisions, rather than their customer bidding decisions. We also discuss how we used the analysis presented in this paper to improve our entry for the 2007 competition, which was one of the six finalists that year.
pake Future
skenario
simulasi scm
prototype model dari supply chain menggunakan java --> simulasi skenaio
efek stokastik harus dianalisis lebih lanjut
simulationmodels were implemented, which map necessary and participating actors. These models were based onan actor-centered view and were implemented by an agent-oriented approach. The developed prototypesupports medical personnel and other staff of participating
Software agents are used to emulate the entities i.e. various enterprises and their internal departments. Flows*/material and information*/are modeled as objects. .
This paper considers a supply chain which comprises multiple independent and autonomousenterprises (project managers) which seek and select various contractors to complete operationsof their projects. Both the project managers and contractors jointly determine the schedules oftheir operations while no single enterprise has complete information of other enterprises. Thecentralized scheduling approach that can usually obtain good global performance but mustshare nearly complete information sharing is difficult or even impractical due to the distributednature of real- life supply chains. This paper proposes an agent-based supply chain model tosupport distributed scheduling.
Vensim® andeM-Plant® in a hierarchical model.
Automated Supply Chain Configurer (ASCC) Framework
The design methodology is based on combining hierarchical model-ing with data-driven modeling.
The next model we consider is a simple tool bank with M machines and N workers. This example is a simplified version of the model in Schruben (1981), that still illustrates an unconventional initial transient and has what might be viewed as a conventional steady-state in the scalar output of total work in the pro-cess queue. The system being modeled is a set of parts presses with workers who do degating (trimming the flange) of finished parts. When the parts queue is too long a “start” light signals the workers, who as soon as they are free, degate the parts in the queue until they reach a “free” limit. Afterwards, they can take a break if there are no parts above their “start” limit.
An allocation algorithm based on the definition isformed. The model of the problem is built and described. The implementation is constructedover an event-based agent simulation platform for the urban environment, AgentPolis.
Methodological for the modeling andsimulation oriented agents, The software designer details the CAOM by associating aconceptual agent with a software agent architecture (forexample BDI (Believe, Desire, Intention) [Rao et al.1991]) and specifying their behaviors (for example aUML2, state chart for a reactive agent) and interactions(AUML3 sequence diagram [Odell et al., 2001]), resultingin an Operational Agent Model (OPAM). T
We covet to propose solutions related to informationsystems and Supply Chains through an intelligentplatform (i-SEEC) that allows different companies tohave the ability to best meet customer demands.
The proposed topology addresses manyissues where future research could yield interestingresults.
skenario
Agripolis, Reg-MAS, MP-MAS,SWISSland
Theframework also gives rigid simulation-based semantics fora supply-chain model. The formalism it introduced helps ananalyst understand and validate simulation results preciselyand rigorously. The formal framework also facilitates automatedformal analysis of a supply chain [7]. We discuss theimplementation of our framework in context of SIMRISK, asupply chain simulation and analysis tool we developed.
The main idea of the proposed approach is based on a multi-agentsystem with optimal profit of the pull, push, and collaborative models among the portalaccess service provider (PASP), the product service provider (PSP), and the mobile serviceprovider (MSP). To address the running mechanism for the multi-agent system, an integratedsystem framework is proposed based on the agent evolution algorithm (AEA), whichcould resolve all these modes. To examine the feasibility of the framework, a prototype systembased on Java-Repast is implemented. The simulation experiments show that this systemcan help decision makers take the appropriate strategies with higher profits.
D4S2
AOE2 framework
The individual choice ofthe farm-household among available production, consumption, investment and marketingalternatives is represented in recursive linear programming models.
We introduce research that explicitly integrates the decentralized decision-makers into the opti-mization of the entire supply chain by incorporating their behavior with mathematical models so asto improve the overall system outcomes.
The methodology is developed to assist DGAgriculture in implementing the newly orientated CAP, RuralDevelopment Policy and the Lisbon Strategy over a period of 15 years1
Witness simulation softwareenvironment using MS Excel for input data loading and outputsupgrading. PTS is particularly suitable for “what-if” analysis inthe crude oil, fuels or gas supply chains where the products aretransported among warehouses and refineries through the pipelines
Model matematis
The scheduling systems generally have different objectives and constraints, and operate in an environment where there is enough Information related to production failures, supplier information, and order processing and customer requests. Each process is assigned to an agent who works according to the information received from other agents. Henceforth, we discuss here the various aspects of agent interactions in supply chain management.
Model matematis
Model Integrated Computing dan ZEUS Agent Building Toolkit
Deliberative agents take into account not only the present situations but also the history of its past interactions. All such interactions can be stored and can be considered as a very large history space, theoretically infinite, limited by the hardware resources.
With the increased importance of an efficient and robust supply chain in business success, most firmsneed to optimise their supply chain configurations not only to meet customer demands with minimum cost butalso to satisfy customer requirements in a dynamically changing operational environment. As such risks anduncertainties should be included in supply chain configuration design and optimisation.
model matematis menggunakan Matlab®: Stateflow and Simulink
air trafic
In this paper we use FACET – an air traffic flow simulatordeveloped at NASA and used extensively by the FAA andindustry – to test a multi-agent algorithm for traffic flowmanagement.
Experiment on a Local Traffic Scenario - Though our modelis still in an early stage of development, these results have revealed interesting properties ofthe proposed concept that will guide our continued development, refinement of the model,and possibly influence other studies of traffic management elsewhere. Finally, we concludewith the challenges of validating the proposed concept through simulation and future work.
Simulation of these individual models acting togethercan predict the result of completely newtransformations in procedures and technologies.While agent-based simulations cannot include everyaspect of system behavior, they can provide quick,cost-effective insights that can supplement otherforms of analysis.
. The technique involves calculating the amount of information gained about an agent’s performance by knowing its value for each of 20 different features. Our analysis helps identify features that differentiated winning agents. In particular we find that, in the final rounds of the 2006 competition, winning agents distinguished themselves by their procurement decisions, rather than their customer bidding decisions. We also discuss how the information gain analysis could be extended by agent developers to identify potential weaknesses in their entry.
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Area Pengarang
1 Umum
2 Umum
3 Umum Lau et al. (2006)
4 Umum
5 Umum(automated SC)
6 Umum Xin Li (2006)
7 Umum
8 Umum Lau et al. (2007)
9 Umum Jetly et al. (2012)
Santa-Eulalia et al. (2011)
Akanle dan Zhang (2008)
Schieritz dan Größler (2003)
Emerson dan Piramuthu (2004)
Andrews et al. (2007)
11 Umum
12 Umum
13 Umum
14 Umum Karimi et al. (2007)
15 Umum Kovalchuk (2008)
16 Umum Zhang dan
Wuhan (2008)17 Umum Kumar et al. (2009)
18 Umum Tan et al. (2009)
19 Umum Chen (2010)
20 Umum Mustapha (2010)
21 Umum Jahani et al. (2011)
22 Umum Harper et al. (2011)
Carvalho dan Custódio (2005)
Sardinha et al. (2006)
Huang dan Levinson (2006)
23 Umum (automated SC)
24 Manufaktur Forget et al. (2008)
10 Manufaktur
25 Manufaktur Aslam et al. (2010)
26 Manufaktur Goel et al. (2011)
27 Manufaktur
28 Manufaktur (inddstri kayu)
29
30 Manufaktur Al-zu’bi (2010)
31 Manufaktur
32 Manufaktur Verdicchio danColombetti (2001)
33 Manufaktur
34 Manufaktur
Ameri dan McArthur (2013)
Santa-Eulalia et al. (2002)
Kaur dan Pandey (2012)
Santa-Eulalia et al (2008)
Manufaktur (industri otomotif dan semi konduktor)
Azevedo et al. (2007)
Souza dan Khong (1999)
Ulieru dan Cobzaru (2005)
Monostori et al. (2006)
34 Manufaktur
35 Manufaktur
36 Manufaktur
37 Manufaktur (kayu)
38 Manufaktur(Golf)
39 Manufaktur Frey et al. (2003)
40 Manufaktur Vieira et al. (2012)
41 Manufaktur Demirel (2008)
42 Manufaktur Mele et al. (2007)
43 Manufaktur Pathak et al. (
44 Manufaktur Mele et al. (2005)
45 Bisnis Lin et al. (2002)
Monostori et al. (2006)
Frayret et al. (2011)
Ibrahim dan Deghedi (2012)
Lamieux et al. (2009)
Labarthe et al. (2005)
45 Bisnis Lin et al. (2002)
46 Bisnis Jiang et al. (2010)
47 Bisnis (Koran)
48 Bisnis (logistic)
49 Bisnis Starý (2012)(Logistik parcel)
50 Kesehatan
51 Kesehatan Kirn et al. (2006)
52 Kesehatan
53 Kesehatan Gasmelseid (2012)
54 Agrikultur Ross (2009)
Böhnlein et al. (2011)
Rani dan Srinivasan (2012)
Nealon dan Moreno (2003)
Charfeddine dan Montreuil (2008)
55 Agrikultural Chen (2011)
56 Agrikultur
57 Energi Kremmydas (2012)
58 Energi
59 Bencana Wu et al. (2008)
60 Bencana Ergun et al. (2008)
1999 1 umum2000 0 manufaktur2001 1 Bisnis2002 2 Kesehatan2003 3 Agrikultural2004 1 Energi2005 4 Bencana2006 62007 52008 92009 42010 52011 72012 102013 1
1 Co-evolutionary 12 Ggeographical information system (GIS) 13 Desain e-Supply Chain Management 14 Procurement auctation 15 Desain pricing 1
Krejci dan Beamon (2012)
Dyntar dan Škvor (2012)
19992000
20012002
20032004
20052006
20072008
20092010
20112012
20130
2
4
6
8
10
12
10
12
3
1
4
65
9
45
7
10
1
umum
manufak
turBisn
is
Kesehata
n
Agriku
ltural
Energ
i
Bencan
a0
5
10
15
20
25
22 22
5 4 3 2 2
Chart Title
Column E
6 Pemilihan distributor 17 Resiko SC 19 Pola distribusi 1
10 Lokasi setiap agen 111 Pergerakan dan rute kendaraaan 212 Perbandingan kontraktor 213 Bullwhip effect 214 Pengendalian sumber daya 215 Peranan partner 216 Emergency Management (EM) 217 Ketidakpastian 318 Automatisasi konfigurasi SC 319 SC competition 320 Pengaturan harga jual 321 Biaya operasi 322 Penjadwalan 523 Alat pengambilan keputusan 524 Kebijakan dan kontrol inventory 625 Pengendalian dan desain order 6
Kerangka kerja SC 8Planning SC 9Desain jaringan SC 9Negosiasi 10Information sharing 12
Kata Kuci dalam Konten
-FAMASS
- Kombinasi sumber daya untuk order individual.
Sentralisasi penjadwalan proyekMelihat penyebaran infromasi-Perbandingan Kontraktor
-Model integrasi
-Pembahasan 2 model
- Konfigurasi SC yang terautomasi
- Information Sharing
- Pendekatan multi agent Untuk memodelkan dan mensimulasikan SC.- Supply Chain Trading Agent Competition (TAC SCM)
-sharing information
- biaya operasi,
-tingkat persediaan
obat dalam lingkungan yang tidak pasti.
-Integrasi SC dengan Advanced Planning and Scheduling (APS)
Model analitis yang unik, yaitu dengan framework FAMASS (FORAC Architecture for Modelling Agent-based Simulations for SC planning)
- Konfigurasi optimisasi dari penawaran iteratif agen dalam
Melihat dan memilih beragam kontrektor yang dapat menyelesaikan proyek.
Untuk menurunkan kompleksitas dari model dengan integrasi antara system dynamic dan diskrit agent based.
Menganalisis kekuatan dan kelemahan 2 model dalam Emergent Structures dalam SC.
Menginvestigasi penyebaran informasi sebagai sebagai dasar dari strategi kolaborasi SC
-tingkat backlog dari retailer, distributor, produsen dan seluruh komponen SC
-SC dalam beberapa perusahaan farmasi yang berinteraksi untuk memproduksi dan mendistribusikan
-Simulasi dari jumlah agen yang hampir tak terbatas
heuristik untuk pengambilan keputusan,
-evaluasi yang berbeda terhadap kriteria dan fungsi-Urutan order yang berbeda-beda, -Prilaku stokastik atau deterministik-intelligent agent-SCM competitionpenerapan desain Trading Agent Competition.-Mengklusterkan- Pemilihan lokasi retail-Pola dari distribusi-jaringan SC- procurement auction
-Selain SCM, solusinya adalah forecasting financial markets dan berpartisipasi dalam online auctions.
-kinerja, dan kebijakan sharing information-negosiasi bilateral, -Pemantauan sistem order dan produksi-Perencanaan dan penjadwalan sistem multiagen.
-Pemecahan masalah komunikasi- MAS-Bullwhip effect-Fill rate-NYOP(Name Your Own Price)-Dinamika negosiasi-Dinamika keputusan pembelian
- intelligent platform (i-SEEC)untuk mencari permintaan konsumen- Pawaran bahan
- agregasi atau disagregasi
-kemungkinan untuk memilih strategi keputusan diantara alternatif dan taktik,
-Jenis informasi dengan dampaknya terhadap rantai pasokan
-MAS merupakan pemecah masalah melampaui kapasitas individu dari setiap problem solver.
- Sebuah metodologi kerangka kerja yang baru dan berorientasi organisasi
-Negosiasi dengan pemasok dan pelanggan pada harga, volume, kualitas, dan tanggal pengiriman bahan supplier.
- data mining yang terintegrasi dengan landasan simulasi agent-based
-Negosiasi automasi
-Framework SC perusahaan-Kebijakan perusahaan-Optimisasi dalam SCM- Intelligent agentspemasok, produsen, distributor dan retailer.
-Pengembangan MAS-Negosiasi antar komponen SC-Pemilihan yang intelegen pada distributor- Negosiasi antara makelar dan distributor- Perancangan, kofigurasi dan desain SC
-Jaringan perusahaanJaringan pasok yang terintegrasi-DSS (Decision support systems)-Production planning and control-E-business-E-Supply Chain Management- Teknanan ekonomi SC klasik
- information sharing sebagai kunci dalam penentuan level dalam jaringan SC- inter-agent stateantara 2 perusahaan
-Studi kasus Industri Manufaktur Telepon-Negosiasiadalah kegiatan interaksi agen akan sistem harga.
-Perkenalan software agents dan sistem multi-agent-ketidakpastian-sharing information
- Menyelesaikan masalah SC dengan kerangka kerja Digital Manufacturing Market (DMM).
-Aplikasi pertama dari teknologi agen didasarkan pada ontologi formal yang mengkodekan kemampuan manufaktur dari supplier.
Negosiasi dalam perencanaan SC menggunakan multi-behavior-agents
-Model perusahaan holonic dengan Foundation for Intelligent Physical Agents (FIPA)
- customer-centric SC
-Perusahaan Canadian forest product-Reverse Information Sharing
-Beer Game-SC risk-Nilai penciptaan jaringan -Advanced planning system- perencanaan lanjutan -sistem penjadwalan-customer-centric supply chain, -MAS dengan simulasi agents-oriented -Desentralisasi SC- Penanganan gangguan yang parah pada supplier- pendekatan terpadu -perencanaan dan pengendalian produksi -Pertimbangan retailer-MAS- information asymmetry
-Mode kontrol yang terdistribusi atau terdesentralisasi.-Variabilitas dari inventory atau order variability -Bullwhip effect-Forecasting-Order batching-Dynamic pricing-MAS-Ketidakpastian-Pengambilan keputusan
-Industri otomotif-Negosiasi online- SCM-Mixed-integer linear programming- Pendekatan hybrid-MAS-Sharing information-tingkat pemenuhan in-time order
-Cara mengaplikasikan advanced planning dan scheduling tool.
-Kombinasi dari teknologi agent-based dan operations research-based tools.
berbagi informasi mengenai breakdown (kerusakan)
-Penggunaan yang efektif dan efisiern dari Electronic Data Interchange (EDI)
-waktu siklus agar lebih pendek
provider (MSP).- Interaksi antara entitas dalam rantai
- Menyelesaiakn permasalahan rute kendaraan.-Memperhitungkan pungutan dalam SC
-Bisnis logistik logistik-shipping- Pergerakan kendaraan dalam bentuk grafik
- Agent.Hospital
Didesain sebagai ntegrasi supply chain dari sistem
-Distribusi dari bahan medis
-Model aliran pasein-Pelayanan kesehatan-MAS-Sistem delivery healthcare yang terdistribusi
-Mengadopsi konsep co-evolutionary
- Agri-Food SC-Peranan partner dalam SC
-Paradigma baru dalam untuk mekanisme kompetitif dan kolaboratif dari rantai layanan mobile
- laba yang optimal dari portal access service provider (PASP), product service provider (PSP), dan mobile service
-Pendekatan Multi-agent dalam sistem produksi dan distribusi industri Koran.
- Kontrol persediaan dan Minimasi total cost dari SC dengan sharing information dan forecasting, menggunakan mekanisme negosiasi.
- DSS (Decision Support system) berlandaskan pada paradigma multi-agent paradigm
dengan permintaan yang tinggi dalam sistem informasi yang dijadikan automasi.
- pengembangan referensi model dari multi-agen Pharmacoinformatics.
-SC dari Produk Agrikultural-ASCTS-SIstem keamanan produk Agrikultural
produksi dan distribusi makanan.-Pembangunan ifrastruktur untuk SC
- Oil refinery SCM
-Discrete Event Simulation, -Agent-Based Modelling.- Respon darurat -Ggeographical information system (GIS)-Emergency Management (EM)Fungsi jaringan logistic yang kuat adalah kunci utama
2222
54322
-Food supply chain (FSC)
-Pengangkutan hasil produksi biofuel pada Perkebunan Jatropha dengan kapal tanker dan pipa
SC pada crude oil, cairan atau gas yang ditransportasikan menggunakan pipa.
Co-evolutionaryGgeographical information system (GIS)
Desain e-Supply Chain ManagementProcurement auctation
Desain pricingPemilihan distributor
Resiko SCPola distribusi
Lokasi setiap agenPergerakan dan rute kendaraaan
Perbandingan kontraktorBullwhip effect
Pengendalian sumber dayaPeranan partner
Emergency Management (EM)Ketidakpastian
Automatisasi konfigurasi SCSC competition
Pengaturan harga jualBiaya operasiPenjadwalan
Alat pengambilan keputusanKebijakan dan kontrol inventoryPengendalian dan desain order
Kerangka kerja SCPlanning SC
Desain jaringan SCNegosiasi
Information sharing
0 2 4 6 8 10 12 14111111111
222222
33333
55
66
899
1012
umum
manufak
turBisn
is
Kesehata
n
Agriku
ltural
Energ
i
Bencan
a0
5
10
15
20
25
22 22
5 4 3 2 2
Chart Title
Column E
Co-evolutionaryGgeographical information system (GIS)
Desain e-Supply Chain ManagementProcurement auctation
Desain pricingPemilihan distributor
Resiko SCPola distribusi
Lokasi setiap agenPergerakan dan rute kendaraaan
Perbandingan kontraktorBullwhip effect
Pengendalian sumber dayaPeranan partner
Emergency Management (EM)Ketidakpastian
Automatisasi konfigurasi SCSC competition
Pengaturan harga jualBiaya operasiPenjadwalan
Alat pengambilan keputusanKebijakan dan kontrol inventoryPengendalian dan desain order
Kerangka kerja SCPlanning SC
Desain jaringan SCNegosiasi
Information sharing
0 2 4 6 8 10 12 14111111111
222222
33333
55
66
899
1012
Goal Metodologi
Framework
Seluruh siklus simulasi.
Mengoptimasi konfigurasi SCM Metode Optimasi
Vensim® daneM-Plant®
-Menurunkan bullwhip effect-Menurunkan inventory
-
Simulasi multi-agents
Memberikan kerangka terpadu untuk menganalisis, menentukan,
merancang dan mengimplementasikan percobaan simulasi meliputi
Mempertimbangkan SC sehingga dapat memilih kontraktor.
model agent-based SC model untuk membantu penjadwalan distribusi.
Menciptakan integrasi model dengan menurunnya kompleksitas dari model antara system dynamic dan diskrit agent based.
Mengetahui availability dari informasi dalam beragam tingkatan SC dan mengefektifkan utitiitas dari informasi sehingga meningkatkan
Kombinasi model hierarchical dengan driven modeling.Penggunaan model prototype dari supply chain sehingga menghasilkan beberapa skenario.
Menentukan fitur dari perilaku agen, seperti rata-rata lead time atau harga jual untuk membedakan agen yang menang dan tidak.
-Mengkaji dampak dari berbagai tingkat penyebaran informasi tentang penambahan inventory perusahaan dan tingkat backlog.
Skenario dalam simulasi eksperimental.
-Mempelajari tingkat penyebaran informasi mana yang paling optimal.
Mengembangkan dan memvalidasi simulasi SC dengan multi-agen,sehingga dapat akurat diwakili untuk menciptakan model yang spesifik.
Simulasi
-
Simulasi
-
-
informasi operasional SCJava
SIMRISK (simulasi)
JADE
-
-
Software agents
Manajemen masalah SC, sehingga memperoleh hasil dengan
tingkat stok stok untuk setiap produk dari material.
untuk menangani sub masalah dari dinamiika SCM
Meneliti munculnya kelompok lokasi bisnis pada jaringan SC.
Mengendalikan kendala dan mendefinisikan procurement auction yang baru.
Model matematis dan simulasi
Memprediksi harga penawaran dalam konteks dinamika lingkungan yang kompetitif.
Memberikan review koordinasi
Konstruksi, arsitektur, koordinasi dan perancangan agen.
Menciptakan sistem yang dikembangkan secara terpisah interkoneksi memebangun agen, jadi memungkinkan harmonisasi yang berfungsi di luar kemampuan dari agen Mengevaluasi kinerja rantai pasokan dengan metrik kinerja rantai pasok, sehingga menjadi acuan perusahaan untuk membuat keputusan di masa depan.
Menghasilkan solusi yang fokus pada kooperasi antara agen-agen dalam SC.
Menyajikan kerangka kerja berbasis agen yang sistematis untuk pemilihan supplier berdasarkan pendekatan kasus.
Menciptakan kerangka kerja untuk merancang sebuah simulasi agent-based untuk memungkinkan adannya agregasi atau disagregasi pada karakteristik agen, perilaku,
skenario
Software agents
Simulasi
Software agent-based
-
Model yang kompleks
Software Agents
JADE
seluruh holarchy SC.
software agents
-Membuat kerangka agent-based untuk menciptakan konfigurasi CS yang tangkas.
Java Agent DEvelopment Framework (JADE).
-Hasilnya buka SC yang optimal, namun kelayakan teknologi SC yang dihasilkan.
mengetahui behavior negosiasi
Framework digunakan untuk membantu menganalisis kebijakan bisnis dengan situasi yang berbeda-beda.Untuk menyajikan kerangka kerja baru dalam penerapan ABS dan teknik optimasi berbasis simulasi.
Rancangan kerangka sistem dapat menghilangkan masalah komunikasi dan pengambilan keputusan untuk sistem Untuk pemilihan distributor yang cocok untuk manufaktur.
Java Application Development Environment (Jade)-Mengusulkan suatu erangka simulasi yang
berbasis agen untuk pemodelan sistem SC dalam tahap analisis.
-Mengusulkan suatu metode formal untuk mengubah model analisis menjadi spesifikasi dan desain dari model.
Meningkatkan visibilitas informasi, peringatan dini gangguan, disinkronisasi produksi dan perencanaan kolaboratif dalam SC.
Menciptakan alat yang dapat menghadapi globalisasi, inovasi produk, hambatan organisasi, dan penyebaran informasi yang
MAS+SCM dengan MAS untuk mendukung Electronic SCMUntuk menghadapi tekanan dalam industri
disk drive, yaitu nilai, waktu, pasokan, permintaan dan pengembangan teknologi Menyajikan struktur data untuk komitmen yang dapat digunakan dalam kerangka komunikasi berbasis agen untuk pengelolaan rantai pasokan
untuk mengakomodasi diferensiasi kepentingan dan menyediakan alokasi sumber daya
Menangani masalah ketidakpastian dan dinamika temporal, sharing information dan operasi terdistribusi atau koordinasi dan kerjasama entitas yang otonom.
software agents
Software
Beer Game
Simulasi
Simulasi
Memungkinkan pemodelan Simulasi
Model matematis
Mengatasi desain yang kompleks dari SC Model matematis
Mensimulasikan -
Menangani masalah ketidakpastian dan dinamika temporal, sharing information dan operasi terdistribusi atau koordinasi dan kerjasama entitas yang otonom.
Mendukung koordinasi perencanaan dan upaya pengendalian dalam lingkungan yang berpusat pada pelanggan dengan cara perencanaan lanjutan dan sistem penjadwalan.
Mengetahui bagaimana gangguan dapat mempengaruhi kinerja secara keseluruhan dan cara penyebaran informasi pabrik secara efektif dapat membantu menghalangi evolusi risiko di SC dan meningkatkan kinerja.Membuat integrasi dari perencanaan lanjutan dan sistem penjadwalan dalam simulasi berbasis multi-agent dalam industri kayu
Mengestimasi permintaan per market per personalisasi, sehingga merepresentasikan kepuasan dari so struktur dan perilaku dalam Mengintegrasikan berbagai sistem intelegent agents untuk mengatasi masalah yang ada
Perbandingan SCM lama dan MAS
SC yang berbeda-beda dengan multi-produk dan kebijakan operasional yang berbeda-beda.
Mengagregatkan jumlah pesanan dan persediaan dengan menurunkan variabilitas bullwhip effect.
Menciptakan model dari agen perangkat lunak untuk mensimulasikan dan mengontrol proses negosiasi online.
Simulasi dengan Model Integrated Computing dan ZEUS Agent Building Toolkit
Memperoleh keuntungan dari sistem multi-agen untuk model SC bersama-sama dengan kemampuan optimasi untuk memecahkan masalah secara efisien.
Model matematis dan simulasi
dan menganalisa korelasi pembeli dan penjual dalam sharing information antara para mitra bisnis SC.
-
Java-Repast
Metode Optimasi
Software agents
-
-Menganalisis kebutuhan
Simulasi
software agents
menyelidiki peran partner di SC. -
Untuk menguji kelayakan kerangka kerja suatu pengimplementasian sistem prototype berbasis Java-Repast yang dapat membantu pengambil keputusan mengambil strategi yang tepat dengan keuntungan yang lebih tinggi.
Optimasi jadwal transportasi yang berhubungan erat dengan jadwal produksi
Merancang biaya subsistem menggunakan teknologi multi-agen yang berkaitan dengan perhitungan pada barang dalam logistic SC.
Memperkirakan jarak sebagai biaya dengan persaingan untuk pengiriman parcel.
Simulasi bernama AgentPolis.
Mengetahui persyarata yang diperlukan untuk memecahkan masalah yang secara spasial terdistribusi dalam berbagai lokasi.
-Mencari proses yang relevan untuk dapat diterapkan pada klinik.
Sistem informasi dengan agent-based indalam skenario-skenario bisnis kesehatan.
-Mengidentifikasikan masalah dalam bisnis healthcare
Membantu memodelkan sistem delivery bahan kesehatan dan meminimasi resiko dari melewatkan elemen dan interaksi yang penting.
Untuk meningkatkan komunikasi,, melaporkan reaksi obat yang merugikan dan memfasilitasinya
berbasis skenario, sehingga meningkatkan kualitas pelayanan farmasi.
-
Simulasi (MS. Excel)
D4S2 (Software Simulasi)
Model matematis
2048
32
Untuk mengatasi kecacatan pada sistem ASCTS (Agricultural Supply Chain Traceability System).
Metode kualitatif dan analisis algoritma pada model fisik.
Memungkinkan penangkapan interaksi dari pengambilan keputusan, dan adaptasi agen FSC yang bersifat otonomMemenuhi kebutuhan produksi dan mengoptimalkan tujuan ekonomi.
Menggambarkan penerapan Pipe Transportation Simulator (PTS) dalam model rantai pasok kilang minyak.
Membuat Model simulasi komprehensif untuk darurat atau bencana tanggapan sipil, sehingga mengintegrasikan ide-ide pemodelan Merancang ME yang paling optimasi dengan dihubungkan dengan mobile komputasi dan metode baru yang diperlukan untuk beroperasi
Co-evolutionaryGgeographical information system (GIS)
Desain e-Supply Chain ManagementProcurement auctation
Desain pricingPemilihan distributor
Resiko SCPola distribusi
Lokasi setiap agenPergerakan dan rute kendaraaan
Perbandingan kontraktorBullwhip effect
Pengendalian sumber dayaPeranan partner
Emergency Management (EM)Ketidakpastian
Automatisasi konfigurasi SCSC competition
Pengaturan harga jualBiaya operasiPenjadwalan
Alat pengambilan keputusanKebijakan dan kontrol inventoryPengendalian dan desain order
Kerangka kerja SCPlanning SC
Desain jaringan SCNegosiasi
Information sharing
0 2 4 6 8 10 12 14111111111
222222
33333
55
66
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umum
manufak
turBisn
is
Kesehata
n
Agriku
ltural
Energ
i
Bencan
a0
5
10
15
20
25
22 22
5 4 3 2 2
Chart Title
Column E
Co-evolutionaryGgeographical information system (GIS)
Desain e-Supply Chain ManagementProcurement auctation
Desain pricingPemilihan distributor
Resiko SCPola distribusi
Lokasi setiap agenPergerakan dan rute kendaraaan
Perbandingan kontraktorBullwhip effect
Pengendalian sumber dayaPeranan partner
Emergency Management (EM)Ketidakpastian
Automatisasi konfigurasi SCSC competition
Pengaturan harga jualBiaya operasiPenjadwalan
Alat pengambilan keputusanKebijakan dan kontrol inventoryPengendalian dan desain order
Kerangka kerja SCPlanning SC
Desain jaringan SCNegosiasi
Information sharing
0 2 4 6 8 10 12 14111111111
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33333
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66
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Software 9 D4S2 1
UmumManufaktur
Bisnis Lin et al. (2002)
Bisnis Jiang et al. (2010)
Bisnis
Bisnis (Koran) Böhnlein et al. (2011)
Bisnis (logistic) Rani dan Srinivasan (2012)
Bisnis Starý (2012)(Logistik parcel)
Kesehatan Nealon dan Moreno (2003)Kesehatan Kirn et al. (2006)
Kesehatan
19992000
20012002
20032004
20052006
20072008
20092010
20112012
20130
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Umum
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 20130
0.5
1
1.5
2
2.5
3
3.5
Manufaktur
Column E
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 20130
0.5
1
1.5
2
2.5
Bisnis
Kesehatan Kirn et al. (2006)
Kesehatan Charfeddine dan Montreuil (2008)
Kesehatan Gasmelseid (2012)
Agrikultur Ross (2009)
Agrikultural Chen (2011) Agrikultur
Agrikultur Krejci dan Beamon (2012)
Energi Kremmydas (2012)Energi
Energi Dyntar dan Škvor (2012)
Bencana Wu et al. (2008)
Bencana Ergun et al. (2008)
Bencana
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 20130
0.5
1
1.5
2
2.5
Bisnis
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 20130
0.2
0.4
0.6
0.8
1
1.2
Kesehatan
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 20130
0.2
0.4
0.6
0.8
1
1.2
Agrikultur
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 20130
0.5
1
1.5
2
2.5
Chart Title
Column E
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 20130
0.5
1
1.5
2
2.5
Chart Title
Column E
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 20130
0.5
1
1.5
2
2.5
Bencana
Column E
199920002001 12002 12003 220042005 12006 22007 42008 32009 22010 22011 32012 12013 11999 120002001 12002 12003 120042005 32006 12007 22008 32009 12010 22011 22012 320131999200020012002 120032004200520062007200820092010 12011 12012 220131999
2000200120022003 1200420052006 120072008 12009201020112012 1201319992000200120022003200420052006200720082009 120102011 12012 1201319992000200120022003200420052006200720082009201020112012 2201319992000
20012002200320042005200620072008 220092010201120122013
Pengarang Kata Kuci dalam Konten
-FAMASS
- Kombinasi sumber daya untuk order individual.
Lau et al. (2006) Sentralisasi penjadwalan proyekMelihat penyebaran infromasi-Perbandingan Kontraktor
-Model integrasi
-Pembahasan 2 model
- Konfigurasi SC yang terautomasi
Xin Li (2006) - Information Sharing
- Pendekatan multi agent Untuk memodelkan dan mensimulasikan SC.
Lau et al. (2007) -sharing information
-tingkat persediaan
Jetly et al. (2012)
obat dalam lingkungan yang tidak pasti.
heuristik untuk pengambilan keputusan,
-evaluasi yang berbeda terhadap kriteria dan fungsi-Urutan order yang berbeda-beda,
Santa-Eulalia et al. (2011)
-Integrasi SC dengan Advanced Planning and Scheduling (APS)
Model analitis yang unik, yaitu dengan framework FAMASS (FORAC Architecture for Modelling Agent-based Simulations for SC planning)
Akanle dan Zhang (2008) - Konfigurasi optimisasi dari penawaran iteratif agen
dalam
Melihat dan memilih beragam kontrektor yang dapat menyelesaikan proyek.
Schieritz dan Größler (2003) Untuk menurunkan kompleksitas dari model dengan
integrasi antara system dynamic dan diskrit agent based.
Menganalisis kekuatan dan kelemahan 2 model dalam Emergent Structures dalam SC.
Emerson dan Piramuthu (2004)
Menginvestigasi penyebaran informasi sebagai sebagai dasar dari strategi kolaborasi SC
Andrews et al. (2007)
- Supply Chain Trading Agent Competition (TAC SCM)
- biaya operasi,
-tingkat backlog dari retailer, distributor, produsen dan seluruh komponen SC
-SC dalam beberapa perusahaan farmasi yang berinteraksi untuk memproduksi dan mendistribusikan
Carvalho dan Custódio (2005)
-Simulasi dari jumlah agen yang hampir tak terbatas
-kemungkinan untuk memilih strategi keputusan diantara alternatif dan taktik,
2005 2007 2008 2009 2010 2011 20120
0.5
1
1.5
2
2.5
Multi Agent-based Simulition
-Prilaku stokastik atau deterministik-intelligent agent-SCM competitionpenerapan desain Trading Agent Competition.-Mengklusterkan
-Pola dari distribusi-jaringan SC
Karimi et al. (2007)
Kovalchuk (2008) -Selain SCM, solusinya adalah forecasting financial markets dan berpartisipasi dalam online auctions.
Zhang dan
Wuhan (2008) -kinerja, dan kebijakan sharing informationKumar et al. (2009) -negosiasi bilateral,
-Pemantauan sistem order dan produksi-Perencanaan dan penjadwalan sistem multiagen.
Tan et al. (2009)
-Pemecahan masalah komunikasiChen (2010)
-Fill rate-NYOP(Name Your Own Price)-Dinamika negosiasi-Dinamika keputusan pembelian
Mustapha (2010)
untuk mencari permintaan konsumenJahani et al. (2011) - Pawaran bahan
Harper et al. (2011)
- agregasi atau disagregasi
Forget et al. (2008) -Negosiasi automasi
-Framework SC perusahaan
Carvalho dan Custódio (2005)
Sardinha et al. (2006)
Huang dan Levinson (2006) - Pemilihan lokasi retail
- procurement auction
-Jenis informasi dengan dampaknya terhadap rantai pasokan
-MAS merupakan pemecah masalah melampaui kapasitas individu dari setiap problem solver.
- MAS-Bullwhip effect
- Sebuah metodologi kerangka kerja yang baru dan berorientasi organisasi
- intelligent platform (i-SEEC)
-Negosiasi dengan pemasok dan pelanggan pada harga, volume, kualitas, dan tanggal pengiriman bahan supplier.
- data mining yang terintegrasi dengan landasan simulasi agent-based
Ameri dan McArthur (2013)
- Menyelesaikan masalah SC dengan kerangka kerja Digital Manufacturing Market (DMM).
-Aplikasi pertama dari teknologi agen didasarkan pada ontologi formal yang mengkodekan kemampuan manufaktur dari supplier.
Negosiasi dalam perencanaan SC menggunakan multi-behavior-agents
Santa-Eulalia et al. (2002)
-Kebijakan perusahaanAslam et al. (2010) -Optimisasi dalam SCM
pemasok, produsen, distributor dan retailer.
Goel et al. (2011) -Pengembangan MAS-Negosiasi antar komponen SC-Pemilihan yang intelegen pada distributor
-Jaringan perusahaanJaringan pasok yang terintegrasi-DSS (Decision support systems)-Production planning and control
Al-zu’bi (2010) -E-business-E-Supply Chain Management
Verdicchio danColombetti (2001) sebagai kunci dalam penentuan level dalam jaringan SC
antara 2 perusahaan
-Studi kasus Industri Manufaktur Telepon-Negosiasiadalah kegiatan interaksi agen akan sistem harga.
-Perkenalan software agents dan sistem multi-agent-ketidakpastian-sharing information
-Perusahaan Canadian forest product-Reverse Information Sharing
-Beer Game-SC risk-Nilai penciptaan jaringan -Advanced planning system- perencanaan lanjutan -sistem penjadwalan
Santa-Eulalia et al. (2002)
- Intelligent agents
Kaur dan Pandey (2012) - Negosiasi antara makelar dan distributorSanta-Eulalia et al (2008)
- Perancangan, kofigurasi dan desain SC
Azevedo et al. (2007)
Souza dan Khong (1999)
- Teknanan ekonomi SC klasik
- information sharing
- inter-agent state
Ulieru dan Cobzaru (2005)
-Model perusahaan holonic dengan Foundation for Intelligent Physical Agents (FIPA)
Monostori et al. (2006)
- customer-centric SCFrayret et al. (2011)
-Cara mengaplikasikan advanced planning dan scheduling tool.
-Kombinasi dari teknologi agent-based dan operations research-based tools.
Ibrahim dan Deghedi (2012) berbagi informasi mengenai breakdown (kerusakan)
Lamieux et al. (2009)
-customer-centric supply chain, -MAS dengan simulasi agents-oriented
Frey et al. (2003) -Desentralisasi SC
-perencanaan dan pengendalian produksi -Pertimbangan retailer
Vieira et al. (2012) -MAS
-Mode kontrol yang terdistribusi atau terdesentralisasi.Demirel (2008)
-Bullwhip effect-Forecasting-Order batching-Dynamic pricing
Mele et al. (2007) -MAS-Ketidakpastian
Pathak et al. ( -Pengambilan keputusan
-Industri otomotif-Negosiasi online
Mele et al. (2005)-Mixed-integer linear programming
-MASLin et al. (2002) -Sharing information
-tingkat pemenuhan in-time order-waktu siklus agar lebih pendek
Jiang et al. (2010)
provider (MSP).
- Menyelesaiakn permasalahan rute kendaraan.-Memperhitungkan pungutan dalam SC
-Bisnis logistik logistik-shipping
Labarthe et al. (2005)
- Penanganan gangguan yang parah pada supplier- pendekatan terpadu
- information asymmetry
-Variabilitas dari inventory atau order variability
-Penggunaan yang efektif dan efisiern dari Electronic Data Interchange (EDI)
- SCM
- Pendekatan hybrid
-Paradigma baru dalam untuk mekanisme kompetitif dan kolaboratif dari rantai layanan mobile
- laba yang optimal dari portal access service provider (PASP), product service provider (PSP), dan mobile service
- Interaksi antara entitas dalam rantai
Böhnlein et al. (2011)
-Pendekatan Multi-agent dalam sistem produksi dan distribusi industri Koran.
Rani dan Srinivasan (2012) - Kontrol persediaan dan Minimasi total cost dari SC
dengan sharing information dan forecasting, menggunakan mekanisme negosiasi.
Starý (2012)
Kirn et al. (2006) - Agent.HospitalDidesain sebagai ntegrasi supply chain dari sistem-Distribusi dari bahan medis
-Model aliran pasein-Pelayanan kesehatan-MAS-Sistem delivery healthcare yang terdistribusi
Gasmelseid (2012)
-Mengadopsi konsep co-evolutionary
Ross (2009)-Peranan partner dalam SC
Chen (2011) -SC dari Produk Agrikultural-ASCTS-SIstem keamanan produk Agrikultural
produksi dan distribusi makanan.Kremmydas (2012) -Pembangunan ifrastruktur untuk SC
-Discrete Event Simulation, -Agent-Based Modelling.
Wu et al. (2008)-Ggeographical information system (GIS)
Ergun et al. (2008) -Emergency Management (EM)Fungsi jaringan logistic yang kuat adalah kunci utama
umum 22manufaktur 22Bisnis 5Kesehatan 4
- Pergerakan kendaraan dalam bentuk grafik
Nealon dan Moreno (2003)
- DSS (Decision Support system) berlandaskan pada paradigma multi-agent paradigm
dengan permintaan yang tinggi dalam sistem informasi yang dijadikan automasi.
Charfeddine dan Montreuil (2008)
- pengembangan referensi model dari multi-agen Pharmacoinformatics.
- Agri-Food SC
Krejci dan Beamon (2012)
-Food supply chain (FSC)
-Pengangkutan hasil produksi biofuel pada Perkebunan Jatropha dengan kapal tanker dan pipa
Dyntar dan Škvor (2012)
- Oil refinery SCMSC pada crude oil, cairan atau gas yang ditransportasikan menggunakan pipa.
- Respon darurat
Agrikultural 3Energi 2Bencana 2
111111111222222333335566899
1012
Co-evolutionaryGgeographical information system (GIS)
Desain e-Supply Chain ManagementProcurement auctation
Desain pricingPemilihan distributor
Resiko SCPola distribusi
Lokasi setiap agenPergerakan dan rute kendaraaan
Perbandingan kontraktorBullwhip effect
Pengendalian sumber dayaPeranan partner
Emergency Management (EM)Ketidakpastian
Automatisasi konfigurasi SCSC competition
Pengaturan harga jualBiaya operasiPenjadwalan
Alat pengambilan keputusanKebijakan dan kontrol inventoryPengendalian dan desain order
Kerangka kerja SCPlanning SC
Desain jaringan SCNegosiasi
Information sharing
0 2 4 6 8 10 12 14111111111
222222
33333
55
66
899
1012
umum
manufak
turBisn
is
Kesehata
n
Agriku
ltural
Energ
i
Bencan
a0
5
10
15
20
25
22 22
5 4 3 2 2
Chart Title
Column E
Mas 2005 12007 1
2008 12009 1
2010 22011 22012 1
2005 2007 2008 2009 2010 2011 20120
0.5
1
1.5
2
2.5
Multi Agent-based Simulition
Co-evolutionaryGgeographical information system (GIS)
Desain e-Supply Chain ManagementProcurement auctation
Desain pricingPemilihan distributor
Resiko SCPola distribusi
Lokasi setiap agenPergerakan dan rute kendaraaan
Perbandingan kontraktorBullwhip effect
Pengendalian sumber dayaPeranan partner
Emergency Management (EM)Ketidakpastian
Automatisasi konfigurasi SCSC competition
Pengaturan harga jualBiaya operasiPenjadwalan
Alat pengambilan keputusanKebijakan dan kontrol inventoryPengendalian dan desain order
Kerangka kerja SCPlanning SC
Desain jaringan SCNegosiasi
Information sharing
0 2 4 6 8 10 12 14111111111
222222
33333
55
66
899
1012
umum
manufak
turBisn
is
Kesehata
n
Agriku
ltural
Energ
i
Bencan
a0
5
10
15
20
25
22 22
5 4 3 2 2
Chart Title
Column E