A New Approach to Reducing Search Space and Increasing Efficiency in Simulation Optimization Problems via the Fuzzy-DEA-BCC

Abstract

The development of discrete-event simulation software was one of the most successful interfaces in operational research with computation. As a result, research has been focused on the development of new methods and algorithms with the purpose of increasing simulation optimization efficiency and reliability. This study aims to define optimum variation intervals for each decision variable through a proposed approach which combines the data envelopment analysis with the Fuzzy logic (Fuzzy-DEA-BCC), seeking to improve the decision-making units’ distinction in the face of uncertainty. In this study, Taguchi’s orthogonal arrays were used to generate the necessary quantity of DMUs, and the output variables were generated by the simulation. Two study objects were utilized as examples of mono- and multiobjective problems. Results confirmed the reliability and applicability of the proposed method, as it enabled a significant reduction in search space and computational demand when compared to conventional simulation optimization techniques.

Increasing the efficiency in integer simulation optimization: Reducing the search space through data envelopment analysis and orthogonal arrays

“…(4) Simulation optimization is used to obtain the solution. Miranda et al, 2014 ) presents the proposed step-by-step approach for optimization and assumes that the simulation model has been built, programmed, verified, and validated. Furthermore, all decision variables are integers.…”

“…Recently, Miranda, Montevechi, Silva, and Marins (2014 ) proposed a new Fuzzy-DEA-BCC model to obtain optimum variation intervals for decision variables, to improve DEA's discrimination power under occurrence of uncertainty and seeking for reduction in search space and computational solution times when compared to conventional simulation optimization techniques. They adopted orthogonal array to generate the necessary quantity of DMUs, and the output variables were generated by simulation.…”

“…Here, we proposed the classical DEA BCC model, also using orthogonal array, as simpler and innovative alternative than the Fuzzy DEA BCC model ( Miranda et al, 2014 ) to analyze the superefficiency. The new procedure reduces search space in the optimization problems without compromising the solution quality, and it allows solving real, complex, and large-scale problems, with substantial decrease in the associated computational time.…”

Strategy for Improving Efficiency of Supply Chain Quality Management in Buyer-Supplier Dyads: The Suppliers’ Perspective

“…The BCC model compensates for the shortcomings of the CCR model by adding constraints. Consequently, it calculates a higher DMU score than the CCR model [36]. The BCC model is defined as follows:…”

Boas práticas administrativas e pedagógicas que contribuem para o desempenho em Matemática dos alunos do Ensino Fundamental II de escolas públicas municipais: análise de dados do Índice de Desenvolvimento da Educação Básica - IDEB e pesquisa exploratória em municípios de nível socioeconômico elevado

“…O modelo BCC calcula um escore de eficiência denominado Variable Returns To Scale From Technical Efficiency (retornos variáveis em escala de eficiência técnica, em tradução livre) -VRSTE (ISHIZAWA, 2013). O Gráfico 3 mostra a fronteira eficiente para um modelo DEA BCC, que é formada pela curva que liga os pontos correspondentes às DMUs eficientes dentro da amostra estudada (ou seja, as DMUs A, B, E e G), desta maneira o modelo DEA BCC assume que o máximo de eficiência nesta amostra é delimitado por essa fronteira (NACIF;MEZA, 2008;OPRICOVIC;TZENG, 2008;BENÍCIO;MELLO, 2014;MONTEVECHI;MARINS, 2014). Isso implica que o modelo DEA BCC calcula escores de eficiência relativos e não absolutos, assim as DMUs eficientes poderiam não sê-lo caso suas eficiências relativas fossem calculadas em outra amostra.…”