Priority rule-based reconstruction for total weighted tardiness minimization of job-shop scheduling problem

Abstract

We propose an efficient heuristic method for job-shop scheduling problems (JSP) with the objective of total weighted tardiness minimization. The proposed method uses schedule reconstructions by priority rules to guide a local search towards promising solutions. Typically priority rules determine the whole schedule and thus in corporating it with local search procedure is difficult. In our proposed method, a priority rule decides a schedule within an arbitrary selected time window and the rest of the schedule is determined by the schedule obtained by a conventional local search method. The priority rule is given by a linear combination of simple priority rules. To improve a schedule efficiently, an appropriate set of time window and gains of the linear combined priority rule is required. Therefore, we optimize the set of time window and rule gains with genetic algorithm. This rule-based reconstruction procedure and the conventional local search procedure are alternately applied to a current solution. In the experiments, the efficiency of rule-based reconstruction procedure is verified and the proposed method is compared with one of the most effective existing methods. The results show that the proposed method outperforms the existing method on large problems with sufficient computational time. The average performance is particularly improved due to the ability of rule-based reconstruction procedure to escape from local optima of the conventional local search procedure efficiently.