Simulation-supported framework for job shop scheduling with genetic algorithm

Abstract

The Job Shop Scheduling Problem (JSSP) is recognized to be one of the most difficult scheduling problems, being NP-complete. During years, many different solving techniques were developed: some techniques are focused on the development of optimization algorithms, whilst others are based on simulation models. Since the 80s, it was recognized that a combination of the two could be of big advantage, matching advantages from both sides. However, this research stream has not been followed to a great extent. The goal of this study is to propose a novel scheduling tool able to match these two really different techniques in one common framework in order to fill this gap in literature. The base of the framework is composed by a genetic algorithm (GA) and a simulation model is introduced into the evaluation of the fitness function, due to the inability of GAs in taking into account the real performances of a production system. An additional purpose of this research is to improve the collaboration between academic and industrial worlds on the topic, through an application of the novel scheduling framework to an industrial case. The implementation to the industrial case also suggested an improvement of the tool: The introduction of the stochasticity into the proposed scheduling framework in order to consider the variable nature of the production systems