A hybrid intelligent algorithm and rescheduling technique for job shop scheduling problems with disruptions

Abstract

In most real manufacturing environment, schedules are usually inevitable with the presence of various unexpected disruptions. In this study, a new rescheduling technique based on a hybrid intelligent algorithm is developed for solving job shop scheduling problems with random job arrivals and machine breakdowns. According to the dynamic feature of this problem, a new initialization method is proposed to improve the performance of the hybrid intelligent algorithm, which combines the advantage of genetic algorithm and tabu search. In order to solve the difficulty of using the mathematical model to express the unexpected disruptions, a simulator is designed to generate the disruptions. The performance measures investigated respectively are: mean flow time, maximum flow time, mean tardiness, maximum tardiness, and the number of tardy jobs. Moreover, many experiments have been designed to test and evaluate the effect of different initializations in several disruption scenarios. Finally, the performance of the new rescheduling technique is compared with other rescheduling technologies in various shop floor conditions. The experimental results show that the proposed rescheduling technique is superior to other rescheduling techniques with respect to five objectives, different shop load level, and different due date tightness. The results also illustrate that the proposed rescheduling technique has a good robustness in the dynamic manufacturing environment.