A survey of the optimization of the flexible job shop problem

Abstract

The flexible job shop problem is an important problem in modern manufacturing systems. It is known to be an NP-hard problem. The optimization of this problem can bring in considerable improvements in the manufacturing efficiency. In recent studies, it has attracted the attention of most researchers in this field. Several metaheuristic methods were proposed to solve this problem. These methods started with exact algorithms and later approximate methods, which include heuristic methods, evolutionary algorithms, swarm intelligence, local search and hybrid algorithms, were introduced to cope with the development and the growing scale of the flexible job shop problem. In this paper we explore the algorithms that are most commonly used to solve this problem. This paper also aims to evaluate and compare the performance of these algorithms.