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Optimal service channel reconfiguration based on multi-agent simulation

Published: 07 December 2008 Publication History

Abstract

Solving the problem of long queues becomes increasingly urgent in many on-site service outlets. For example, in a bank branch or a government agency, customers may have to wait for a very long time to be served. How to optimally reconfigure the capability of service channels in such outlets is a key for the service providers to improve efficiency and quality of service. In this paper, we propose a method to optimally configure service channels at a given service outlet. We integrate customer experience metrics with cost and profit into a unified objective function for optimization. Multi-agent simulation technique is employed to model the stochastic service processes and customer behavior, and to evaluate the objective function in optimizing service channel capacity. Some real-life data collected from bank branches provide significant empirical support to the method and demonstrate that the presented method is both effective and efficient.

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Published In

cover image ACM Conferences
WSC '08: Proceedings of the 40th Conference on Winter Simulation
December 2008
3189 pages
ISBN:9781424427086

Sponsors

  • IIE: Institute of Industrial Engineers
  • INFORMS-SIM: Institute for Operations Research and the Management Sciences: Simulation Society
  • ASA: American Statistical Association
  • IEEE/SMC: Institute of Electrical and Electronics Engineers: Systems, Man, and Cybernetics Society
  • SIGSIM: ACM Special Interest Group on Simulation and Modeling
  • NIST: National Institute of Standards and Technology
  • (SCS): The Society for Modeling and Simulation International

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Winter Simulation Conference

Publication History

Published: 07 December 2008

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  • Research-article

Conference

WSC08
Sponsor:
  • IIE
  • INFORMS-SIM
  • ASA
  • IEEE/SMC
  • SIGSIM
  • NIST
  • (SCS)
WSC08: Winter Simulation Conference
December 7 - 10, 2008
Florida, Miami

Acceptance Rates

WSC '08 Paper Acceptance Rate 249 of 304 submissions, 82%;
Overall Acceptance Rate 3,413 of 5,075 submissions, 67%

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