Robust simulation with a base environmental scenario

Abstract In the design of a system, the comparison of possible solutions using simulation is generally performed with fixed environmental conditions. In practice, however, unexpected changes can occur for example in the part mix of a manufacturing facility or in the customer demand. Such changes, which are considered as modifications in environmental factors, can impact the system response. As a consequence, a solution A that is better than B for a given environment, can yield poorer performance than B for another environment. Therefore, we are interested in robust simulation studies, which aim at taking into account several possible environments. In methods based on Taguchi’s principles, no distinction is made between these environments in the robustness computation. In the suggested heuristic approach, we focus on problems where a particular environment is expected when the system will be in operation (the others being unexpected environments). This particular environment will be considered in the study as a “base environmental scenario”. The robustness of a solution of the design problem is computed as an approximate measure of what will be saved or lost if the environment becomes the unexpected. Reference curves are suggested to allow these solutions to be empirically compared in accordance with the decision-maker’s requirements. A simplified example is provided. The results are different from those obtained using a signal to noise ratio, which is typically used in Taguchian approaches.

[1]  Henri Pierreval,et al.  Distributed evolutionary algorithms for simulation optimization , 2000, IEEE Trans. Syst. Man Cybern. Part A.

[2]  Joseph J. Pignatiello,et al.  An experimental design strategy for designing robust systems using discrete-event simulation , 1991, Simul..

[3]  Henri Pierreval,et al.  A two-stage simulation optimization heuristic for robust design , 2002, IEEE International Conference on Systems, Man and Cybernetics.

[4]  C. F. Jeff Wu,et al.  Experiments: Planning, Analysis, and Parameter Design Optimization , 2000 .

[5]  E.G.A. Gaury Designing pull production control systems: Customization and robustness , 2000 .

[6]  Thomas J. Santner,et al.  The Design and Analysis of Computer Experiments , 2003, Springer Series in Statistics.

[7]  Jack P. C. Kleijnen,et al.  Simulation: A Statistical Perspective , 1992 .

[8]  J. Banks,et al.  Handbook of Simulation , 1998 .

[9]  Susan M. Sanchez,et al.  Robust design: seeking the best of all possible worlds , 2000, 2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165).

[10]  Urmila M. Diwekar,et al.  An efficient sampling technique for off-line quality control , 1997 .

[11]  Liang-Hsuan Chen,et al.  An intelligent interface between symbolic and numeric analysis tools required for the development of an integrated CAD system , 1996 .

[12]  R. Al-Aomar,et al.  A robust simulation-based multicriteria optimization methodology , 2002, Proceedings of the Winter Simulation Conference.

[13]  Susan M. Sanchez,et al.  Designing simulation experiments: Taguchi methods and response surface metamodels , 1991, 1991 Winter Simulation Conference Proceedings..

[14]  Chi-Sheng Tsai,et al.  Evaluation and optimisation of integrated manufacturing system operations using Taguch's experiment design in computer simulation , 2002 .

[15]  John Stufken,et al.  Taguchi Methods: A Hands-On Approach , 1992 .

[16]  G. Geoffrey Vining,et al.  Taguchi's parameter design: a panel discussion , 1992 .

[17]  László Monostori,et al.  Design and real-time reconfiguration of robust manufacturing systems by using design of experiments and artificial neural networks , 1997 .

[18]  Jennifer Shang Robust design and optimization of material handling in an FMS , 1995 .

[19]  Averill M. Law,et al.  Simulation Modeling and Analysis , 1982 .

[20]  Christian N. Madu,et al.  Design optimization using signal-to-noise ratio , 1999, Simul. Pract. Theory.

[21]  Hark Hwang,et al.  Determination of an optimal configuration of operating policies for direct-input-output manufacturing systems using the Taguchi method , 1996 .

[22]  Max Henrion,et al.  Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis , 1990 .

[23]  William Y. Fowlkes,et al.  Engineering Methods for Robust Product Design: Using Taguchi Methods in Technology and Product Development , 1995 .

[24]  Susan M. Sanchez,et al.  Effective Engineering Design through Simulation , 1996 .

[25]  Kwok-Leung Tsui,et al.  AN OVERVIEW OF TAGUCHI METHOD AND NEWLY DEVELOPED STATISTICAL METHODS FOR ROBUST DESIGN , 1992 .

[26]  Madhav Erraguntla,et al.  Using Simulation for Robust System Design , 1995, Simul..

[27]  Barry L. Nelson,et al.  Comparing Systems via Simulation , 2007 .

[28]  Jack P. C. Kleijnen,et al.  Short-term robustness of production management systems: A case study , 2003, Eur. J. Oper. Res..