Artificial bee colony algorithm for solving a bi-objective job shop scheduling problem

Job shop scheduling is a significant decision problem in modern manufacturing systems. Most existing algorithms can only handle the minimization of makespan (a.k.a. the maximum completion time). However, due date related performances are also very important for the companies in a fiercely competitive market. In this paper, we propose an artificial bee colony (ABC) algorithm for solving the job shop scheduling problem with the objectives of minimizing the makespan as well as the total weighted tardiness. As a swarm-intelligence-based optimizer, ABC systematically incorporates the function of exploration and exploitation. The aggregating functions method has been adopted for adapting ABC to the multi-objective optimization problem. Finally, the computational experiments on a set of test instances show that the proposed ABC is effective and efficient for solving the scheduling problem.