A rule-based estimation of distribution algorithm for solving job shop scheduling problems

In this paper, we aim at solving job shop scheduling problems with the total weighted tardiness objective. In order to save computational time, several effective dispatching rules are adopted for constructing feasible schedules. Meanwhile, it is noticeable that the rule selections for each machine are not mutually independent but actually interrelated. Under such circumstances, an estimation of distribution algorithm (EDA) is proposed to optimize the combination of the rules. The superiority of this approach is verified by extensive computational experiments and comparisons.