A new approach using fuzzy DEA models to reduce search space and eliminate replications in simulation optimization problems

Author(s)

Silva Marins, Fernando Augusto ^[1] ; da Silva, Aneirson Francisco ^[1] ; Miranda, Rafael de Carvalho ^[2] ; Barra Montevechi, Jose Arnaldo ^[2]

Affiliation

^[1] Sao Paulo State Univ UNESP, Av Dr Ariberto Pereira da Cunha 333, BR-12516410 Guaratingueta, SP - Brazil

^[2] Fed Univ Itajuba UNIFEI, Av BPS 1303, BR-37500903 Itajuba, MG - Brazil

• Document type: Journal article
• Source: EXPERT SYSTEMS WITH APPLICATIONS; v. 144, APR 15 2020.
• Web of Science Citations: 0

Abstract

This article proposes a new combination of methods to increase optimization simulation efficiency and reliability, utilizing orthogonal arrays, fuzzy-data envelopment analysis (FDEA) with linear membership function, and discrete event simulation (DES). Considering a simulation optimization problem, experimental matrices are generated using orthogonal arrays and which simulation runs (scenarios) will be executed are defined, followed by FDEA to analyze and rank the scenarios in terms of their efficiency (considering occurrence of uncertainty). In this way, it is possible to reduce the search space of scenarios to be simulated, and avoid the need for replications in DES, without impairing the quality of the final solution. Six real cases that were solved by the proposed approach are presented. In order to highlight the efficiency of the proposed method, in Cases 5 and 6, all viable solutions of each of these problems were tested, ie, 100% of the search space was analyzed, and it was found that the solution obtained by the new method was statistically equal to the overall optimal solution. Note that for the other real cases solved, the solutions obtained by the proposed method were also statistically equal to those obtained from the original search space, and that analyzing 100% of the viable solutions space would be computationally impossible or impractical. These results confirmed the reliability and applicability of the proposed method, since it enabled a significant reduction in the search space for the simulation application compared to conventional simulation optimization techniques. (C) 2019 Elsevier Ltd. All rights reserved. (AU)

• FAPESP's process: 18/06858-0 - Optimization via Monte Carlo simulation combined with the response surface method: a proposal for insertion of uncertainty in optimization of experimental problems
• Grantee: Aneirson Francisco da Silva
• Support Opportunities: Regular Research Grants
• FAPESP's process: 18/14433-0 - Application of the multi-objective optimization under uncertainty for paper waste Reclycing systems
• Grantee: Fernando Augusto Silva Marins
• Support Opportunities: Regular Research Grants