Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/246371 
Year of Publication: 
2019
Citation: 
[Journal:] Operations Research Perspectives [ISSN:] 2214-7160 [Volume:] 6 [Publisher:] Elsevier [Place:] Amsterdam [Year:] 2019 [Pages:] 1-14
Publisher: 
Elsevier, Amsterdam
Abstract: 
Optimization via Simulation (OvS) is an useful optimization tool to find a solution to an optimization problem that is difficult to model analytically. OvS consists in evaluating potential solutions through simulation executions; however, its high computational cost is a factor that can make its implementation infeasible. This issue also occurs in multi-objective problems, which tend to be expensive to solve. In this work, we present a new hybrid multi-objective OvS algorithm, which uses Kriging-type metamodels to estimate the simulations results and a multi-objective evolutionary algorithm to manage the optimization process. Our proposal succeeds in reducing the computational cost significantly without affecting the quality of the results obtained. The evolutionary part of the hybrid algorithm is based on the popular NSGA-II. The hybrid method is compared to the canonical NSGA-II and other hybrid approaches, showing a good performance not only in the quality of the solutions but also as computational cost saving.
Subjects: 
Optimization via simulation
Metamodel
Multi-Objective optimization
Kriging
NSGA-II
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article

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