@techreport{5b506682ac584dd68c4ccbed29f4e6ef,

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

abstract = "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.",

keywords = "simulation, experimental design, statistical methods, optimization, risk analysis, bootstrap, production control, robustness",

author = "J.P.C. Kleijnen and E.G.A. Gaury",

note = "Pagination: 24",

year = "2001",

language = "English",

volume = "2001-10",

series = "CentER Discussion Paper",

publisher = "Operations research",

type = "WorkingPaper",

institution = "Operations research",

}