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

Abstract

The development of various heuristics has enabled optimization in simulation environments. Nevertheless, this research area remains underexplored, primarily with respect to the time required for convergence of these heuristics. In this sense, simulation optimization is influenced by the complexity of the simulation model, the number of variables, and by their ranges of variation. Within this context, this paper proposes a method capable of identifying the best ranges for each integer decision variable within the simulation optimization problem, thereby providing a reduction in computational cost without loss of the quality in the response. The proposed method combines experimental design techniques, Discrete Event Simulation, and Data Envelopment Analysis. The experimental designs called orthogonal arrays are used to generate the input scenarios to be simulated, and super-efficiency analysis is applied in a Data Envelopment Analysis model with variable returns to scale to rank the input scenarios. The use of the super-efficiency concept enables to distinguish the most efficient input scenarios, which allows for the ranking of all the orthogonal array scenarios used. The values of the variables of the two input scenarios that present the highest values of super-efficiency are adopted as the new range of the optimization problem. To illustrate this method's use and advantages, it was applied to real cases associated with integer simulation optimization problems. Based on the results, the effectiveness of this approach is verified because it delivered considerable reductions in the search space and in the computational time required to obtain a solution without affecting the quality.

Goal programming and multiple criteria data envelopment analysis combined with optimization and Monte Carlo simulation: An application in railway components

“…Generally, when not much is known about the distribution of an outcome (say, only its smallest and largest values), it is possible to use uniform distribution. But, if the most likely outcome is also known, then the outcome can be simulated by a triangular distribution (Mendas & Lorenzoni, 2018; Miranda et al, 2017).…”

Metamodel-Based Simulation Optimization: A Systematic Literature Review

“…Figure 1 shows this process. According to Miranda et al (2017), several SO techniques have been developed in recent years, e.g., heuristics and metaheuristics, that have still been little explored by scientific literature. SO methods can be classified into two categories, model-based and metamodelbased methods.…”

“…Problems may also contain other constraints (represented by h) that do not involve random variables, or contain constraints linked to decision variables. By contrast, Miranda et al (2017), states that long computational times are required for the optimization algorithm to converge to a good result for SO problems addressing complex systems with very large solution spaces. Barton (2009), states that this has led researchers to develop specialized SO methods, which include:…”

Tema 7 - Pesquisa Operacional

“…A Simulação a Eventos Discretos (SED) vem sendo utilizada de forma crescente, há várias décadas, sempre apoiando o processo de tomada de decisões (BANKS et al, 2009;SARGENT, 2013;LAW, 2015;MIRANDA et al, 2017) SIEBERS et al, 2010, principalmente, devido à sua versatilidade, flexibilidade e poder de análise (JAHANGIRIAN et al, 2010;RYAN e HEAVEY, 2006;XU et al, 2015). Esta já é apontada como uma das técnicas de pesquisa mais utilizadas em vários setores, permitindo o estudo de sistemas complexos, de forma mais econômica, rápida e flexível que a experimentação direta em sistemas reais, o que consume enormes recursos (SHEN e WAN, 2009; SHARDA e BURY, 2011).…”