Integration of genetic algorithm and Monte Carlo to analyze the effect of routing flexibility

Abstract

Flexibility is an important task for effectively utilizing resources in a manufacturing system and responding demands rapidly. In manufacturing systems, there exist different types of flexibility levels. In this study, the stochastic flexible job shop scheduling problem is considered to measure the impact of routing flexibility on shop performance. Thus, an integrated genetic algorithm-Monte Carlo method is proposed to analyze the effect of routing flexibility. To make the problem more realistic, system parameters (processing times, operation sequences, etc.) are generated randomly via Monte Carlo. An experimental design is utilized to analyze main and interaction effects of the factors considered (i.e., number of parts, number of machines, number of operations, and flexibility levels) by using a genetic algorithm which is specifically designed for the stochastic flexible job shop scheduling problem. In developed genetic algorithm, different initial strategies which not only improve solution quality but also decrease solution time are used. Makespan is specified as the objective function to be minimized. Results are analyzed with a full factorial analysis of variance. Comprehensive discussions of results are given case by case.