A new approach using fuzzy DEA models to reduce search space and eliminate replications in simulation optimization problems. (15th April 2020)

• Record Type: Journal Article
• Authors: Marins, Fernando Augusto Silva; da Silva, Aneirson Francisco; Miranda, Rafael de Carvalho; Montevechi, José Arnaldo Barra
• Is Part Of: Expert systems with applications. Volume 144(2020)
• Publication Date: 2020-04-15
• Subjects: Simulation optimization -- Discrete event simulation -- Fuzzy-Data Envelopment Analysis; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2019.113137
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A method was developed to reduce the search space in simulation optimization. The method combines orthogonal Arrays, data envelopment analysis and optimization. The method reduces the search space in various case studies by roughly 97%. The method defines the best range of variation for each decision variable. The method is applied to simulation optimization with a single objective. 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.