### Abstract

Original language | English |
---|---|

Place of Publication | Tilburg |

Publisher | Operations research |

Number of pages | 24 |

Volume | 2001-10 |

Publication status | Published - 2001 |

### Publication series

Name | CentER Discussion Paper |
---|---|

Volume | 2001-10 |

### Fingerprint

### Keywords

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

### Cite this

*Optimization Versus Robustness in Simulation: A Practical Methodology, With a Production-Management Case-Study*. (CentER Discussion Paper; Vol. 2001-10). Tilburg: Operations research.

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**Optimization Versus Robustness in Simulation : A Practical Methodology, With a Production-Management Case-Study.** / Kleijnen, J.P.C.; Gaury, E.G.A.

Research output: Working paper › Discussion paper › Other research output

TY - UNPB

T1 - Optimization Versus Robustness in Simulation

T2 - A Practical Methodology, With a Production-Management Case-Study

AU - Kleijnen, J.P.C.

AU - Gaury, E.G.A.

N1 - Pagination: 24

PY - 2001

Y1 - 2001

N2 - 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.

AB - 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.

KW - simulation

KW - experimental design

KW - statistical methods

KW - optimization

KW - risk analysis

KW - bootstrap

KW - production control

KW - robustness

M3 - Discussion paper

VL - 2001-10

T3 - CentER Discussion Paper

BT - Optimization Versus Robustness in Simulation

PB - Operations research

CY - Tilburg

ER -