A Framework to Incorporate Decision-Maker Preferences Into Simulation Optimization to Support Collaborative Design
| Autor: | Henri Pierreval, Ahlem Baccouche, Selcuk Goren |
|---|---|
| Přispěvatelé: | Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS), Ecole Nationale Supérieure des Mines de St Etienne-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Mathematical optimization
021103 operations research Optimization problem Linear programming Computer science Stochastic process Supply chain Probabilistic-based design optimization Analytic network process 0211 other engineering and technologies Analytic hierarchy process 02 engineering and technology Computer Science Applications Human-Computer Interaction [SPI]Engineering Sciences [physics] Control and Systems Engineering Genetic algorithm 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing [INFO]Computer Science [cs] Electrical and Electronic Engineering Social choice theory Performance metric Software |
| Zdroj: | IEEE Transactions on Systems IEEE Transactions on Systems, 2017, 47(2), pp.1-9. ⟨10.1109/TSMC.2016.253164⟩ |
| DOI: | 10.1109/TSMC.2016.253164⟩ |
| Popis: | In this paper, we are concerned with the use of simulation optimization to handle collaborative design problems where more than one decision-maker is involved. We assume that the designers cannot enumerate all their considerations in closed-form, precise mathematical expressions but they can examine the merits of solutions with respect to their preferences and can compare candidate solutions with one another. We propose a three-stage framework to take the decision-makers’ such considerations into account. The first step is to obtain a diverse set of designs that can all be considered efficient in terms of a performance metric (i.e., the objective function values of the simulation optimization model). These solutions are then passed on to the decision-makers to be analyzed in terms of their preferences that could not have been previously considered. Finally, the most appropriate solution is chosen. We address the problem encountered in the first step as a multimodal optimization problem. We address the second and the third subproblems as a preference aggregation problem in the social choice theory. We also illustrate the effectiveness of the proposed approach through a supply chain design problem inspired from the literature. We use the crowding clustering genetic algorithm as an example to demonstrate the first step. We use a multiplicative variant of the popular analytic hierarchy process to illustrate how the second and the third steps can be handled. |
| Databáze: | OpenAIRE |
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