Unequal-area stochastic facility layout problems: solutions using improved covariance matrix adaptation evolution strategy, particle swarm optimisation, and genetic algorithm

Abstract

Determining the locations of departments or machines in a shop floor is classified as a facility layout problem. This article studies unequal-area stochastic facility layout problems where the shapes of departments are fixed during the iteration of an algorithm and the product demands are stochastic with a known variance and expected value. These problems are non-deterministic polynomial-time hard and very complex, thus meta-heuristic algorithms and evolution strategies are needed to solve them. In this paper, an improved covariance matrix adaptation evolution strategy (CMA ES) was developed and its results were compared with those of two improved meta-heuristic algorithms (i.e. improved particle swarm optimisation [PSO] and genetic algorithm [GA]). In the three proposed algorithms, the swapping method and two local search techniques which altered the positions of departments were used to avoid local optima and to improve the quality of solutions for the problems. A real case and two problem instances were introduced to test the proposed algorithms. The results showed that the proposed CMA ES has found better layouts in contrast to the proposed PSO and GA.

Using eye-tracking into decision makers evaluation in evolutionary interactive UA-FLP algorithms

“…In recent years, meta-heuristic techniques have been extensively used for solving FLPs and, among them, genetic algorithms [2,19,23,35,44,49], simulated annealing [5,36,42], ant colony optimization [24,28,40,52], tabu search [8,30,33,54], particle swarm optimization [13,34], and coral reefs optimization [20].…”

Quality-Oriented Study on Mapping Island Model Genetic Algorithm onto CUDA GPU

“…UA-FLP was proven to be an NP-hard combinatorial optimization problem, meaning that when the number of facilities increases, the calculation time is exponentially increased [24]. More and more metaheuristic algorithms [25][26][27][28][29][30] are proposed to solve UA-FLP for finding better near-optimal solutions. Among them, GA is the most popular algorithm due to its effectiveness in enhancing the opportunity to achieve global optimal solutions.…”