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A multi-agent approach for solving optimization problems involving expensive resources

Published: 13 March 2005 Publication History

Abstract

In this paper, we propose a multi-agent approach for solving a class of optimization problems involving expensive resources, where monolithic local search schemes perform miserably. More specifically, we study the class of bin-packing problems. Under our proposed Fine-Grained Agent System scheme, rational agents work both collaboratively and selfishly based on local search and mimic physics-motivated systems. We apply our approach to a generalization of bin-packing - the Inventory Routing Problem with Time Windows - which is an important logistics problem, and demonstrate the efficiency and effectiveness of our approach.

References

[1]
Yokoo M., Durfee E. H., Ishida T., and Kuwabara K., 1998 Distributed constraint satisfaction problem: Formalization and algorithms. IEEE Transactions on Data and Knowledge Engineering, 673--685
[2]
Modi P. J., Shen W., Tambe M., and Yokoo M., 2003 An asynchronous complete method for distributed constraint optimization. Proc. Autonomous Agents and Multiagent Systems, 161--168
[3]
Armstrong, A. and E. Durfee, 1997 Dynamic Prioritization of Complex Agents in Distributed Constraint Satisfaction Problems. Proc. 15th International Joint Conference on Artificial Intelligence, 620--625
[4]
Jung H. and Tambe M., 2003 Performance Models for Large Scale Multiagent Systems: Using Distributed POMDP Building Blocks, Proc. Autonomous Agents and Multiagent Systems, 297--304
[5]
Tsui K. C. and Liu J., 2003 Multiagent Diffusion and Distributed Optimization. Proc. Autonomous Agents and Multiagent Systems, 169--176
[6]
Shehory O., Kraus S, and Yadgar O., 1999 Emergent cooperative goal satisfaction in large-scale automated-agent systems. Artificial Intelligence, 110(1): 1--55
[7]
Lau H. C., Ono H., and Liu Q. Z., 2002 Integrating Local Search and Network Flow to Solve the Inventory Routing Problem. Proc. 18th National Conf. on Artificial Intelligence (AAAI), 9--14
[8]
De Backer B., and Furnon V., 1997 Meta-heuristics in Constraint Programming Experiments with Tabu Search on the Vehicle Routing Problem, Proc. 2nd Metaheuristics International Conference
[9]
Lau H. C., Lim A., and Liu Q. Z., 2000 Solving a Supply Chain Optimization Problem Collaboratively. Proc. 17th National Conf. on Artificial Intelligence (AAAI), 780--785
[10]
Lau H. C., Lim M. K., Wan W. C., Wang H. and Wu X., 2003 Solving Multi-Objective Multi-Constrained Optimization Problems using Hybrid Ants System and Tabu Search. Proc. 5th Metaheuristics International Conference
[11]
Lerman K. and Shehory O., 2000 Coalition formation for largescale electronic markets. Proc. International Conference on Multi-Agent Systems (ICMAS)

Cited By

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  • (2017)Decision Support through Intelligent Agent Based Simulation and Multiple Goal Based Evolutionary OptimizationIntelligent Information Management10.4236/iim.2017.9300509:03(97-113)Online publication date: 2017
  • (2013)Multiagent cooperation for solving global optimization problemsJournal of Global Optimization10.1007/s10898-012-0012-357:2(499-519)Online publication date: 1-Oct-2013

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  1. A multi-agent approach for solving optimization problems involving expensive resources

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    cover image ACM Conferences
    SAC '05: Proceedings of the 2005 ACM symposium on Applied computing
    March 2005
    1814 pages
    ISBN:1581139640
    DOI:10.1145/1066677
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 13 March 2005

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    Author Tags

    1. expensive resources
    2. multi-agent system
    3. optimization problems

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    SAC05
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    SAC05: The 2005 ACM Symposium on Applied Computing
    March 13 - 17, 2005
    New Mexico, Santa Fe

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    Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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    View all
    • (2017)Decision Support through Intelligent Agent Based Simulation and Multiple Goal Based Evolutionary OptimizationIntelligent Information Management10.4236/iim.2017.9300509:03(97-113)Online publication date: 2017
    • (2013)Multiagent cooperation for solving global optimization problemsJournal of Global Optimization10.1007/s10898-012-0012-357:2(499-519)Online publication date: 1-Oct-2013

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