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.
|Place of Publication||Tilburg|
|Number of pages||24|
|Publication status||Published - 2001|
|Name||CentER Discussion Paper|
- experimental design
- statistical methods
- risk analysis
- production control
Kleijnen, J. P. C., & Gaury, E. G. A. (2001). Optimization Versus Robustness in Simulation: A Practical Methodology, With a Production-Management Case-Study. (CentER Discussion Paper; Vol. 2001-10). Operations research.