An ANN-based optimization model for facility layout problem using simulation technique

Abstract

A real manufacturing system faces lots of real-world situations, such as stochastic behaviors; the lack of attention to this issue is noticeable in the previous research. The aim of this paper is to find the optimum layout and the most appropriate handling transporters for the problem by a novel solving algorithm. The new model contains two objective functions including the Material Handling Costs (MHC) and the complication time of jobs (makespan). Real-world situations such as stochastic processing times, random breakdowns, and cross traffics among transporters are considered in this paper. Several experiment designs have been produced using DOE technique in simulation software and an Artificial Neural Network (ANN) as a meta-model is used to estimate the objective functions in the metaheuristic algorithms. A hybrid non-dominated sorting genetic algorithm (H-NSGA-II) is applied for the optimization task. The proposed methodology is evaluated through a real case study. First, simulation model is validated by comparing it with a real data set. Then, the prediction performance of ANN is investigated. Finally, the ability of H-NSGA-II in searching the solution space is compared with the traditional NSGA-II. The results show that the proposed approach, combing simulation, ANN, and H-NSGA-II, provides promising solutions for practical applications.