Optimization Versus Robustness in Simulation: A Practical Methodology, With a Production-Management Case-Study

J.P.C. Kleijnen, E.G.A. Gaury

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Whereas Operations Research has always paid much attention to optimization, practitioners judge the robustness of the 'optimum' solution to be of greater importance.Therefore this paper proposes a practical methodology that is a stagewise combination of the following four proven techniques: (1) discrete-event simulation, (2) heuristic optimization, (3) risk or uncertainty analysis, and (4) bootstrapping.This methodology is illustrated through a case study on production control systems.That study defines robustness as the system's capability to maintain a short-term service measure, in a variety of environments (scenarios).More precisely, this measure is the probability of the short-term fill rate remaining within a prespecified range.Besides satisfying this probabilistic constraint, the system should minimize long-term work-in-process.Actually, the case study compares four systems: Kanban, Conwip, Hybrid, and Generic.These systems are studied for a well-known example, namely a production line with four stations and a single product.The conclusion of this case study is that Hybrid is best when risk is not ignored, but otherwise Generic is best: risk considerations do make a difference.
Original languageEnglish
Place of PublicationTilburg
PublisherOperations research
Number of pages24
Publication statusPublished - 2001

Publication series

NameCentER Discussion Paper


  • simulation
  • experimental design
  • statistical methods
  • optimization
  • risk analysis
  • bootstrap
  • production control
  • robustness


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