A memetic algorithm for job shop scheduling using a critical-path-based local search heuristic

Abstract

In this article, a new memetic algorithm has been proposed to solve job shop scheduling problems (JSSPs). The proposed method is a genetic-algorithm-based approach combined with a local search heuristic. The proposed local search heuristic is based on critical operations. It removes the critical operations and reassigns them to a new position to improve the fitness value of the schedule. Moreover, in this article, a new fitness function is introduced for JSSPs. The new fitness function called priority-based fitness function is defined in three priority levels to improve the selection procedure. To show the generality of our proposed method, we apply it to three different types of job scheduling problems including classical, flexible and multi-objective flexible JSSPs. The experiment results show the efficiency of the proposed fitness function. In addition, the results show that incorporating local search not only offers better solutions but also improves the convergence rate. Compared to the state-of-the-art algorithms, the proposed method outperforms the existing methods in classical JSSPs and offers competitive solutions in other types of scheduling problems.