A hybrid genetic algorithm and tabu search for multi-objective dynamic job shop scheduling problem

摘要

In most real manufacturing environments, schedules are usually inevitable with the presence of various unexpected disruptions. In this paper, a rescheduling method based on the hybrid genetic algorithm and tabu search is introduced to address the dynamic job shop scheduling problem with random job arrivals and machine breakdowns. Because the realtime events are difficult to be expressed and taken into account by the mathematical model, a simulator is proposed to tackle the complexity of the problem. A hybrid policy is selected to deal with the dynamic feature of the problem. Two objectives, which are the schedule efficiency and the schedule stability, are considered simultaneously to improve the robustness and the performance of the schedule system. Numerical experiments have been designed to test and evaluate the performance of the proposed method. This proposed method has compared with some common dispatching rules and meta-heuristic algorithms which have widely been used in the literature. The experimental results illustrate that the proposed method is very effective in various shop floor conditions.