مدل حل مبتنی بر جستجوگر محلی ژنتیک برای مساله زمان بندی استقرار کارگاهی تعمیم یافته با زمانهای عملیات قابل کنترل

Although incorporating complexities and flexibilities of real world manufacturing systems into classic scheduling problems results in problems with greater complexity, it has immense theoretical and practical importance due to its impressive effect on system performance. In this research, three basic assumptions of a job shop scheduling problem have been revised to develop a model with three types of flexibility which fits into many cases of real manufacturing environments sequencing flexibility, machine flexibility, and processing time flexibility. In this paper, after describing the problem and formulating it as a mathematical program, we have proposed an efficient genetic local search algorithm to solve the problem. In the proposed algorithm first, problem-specific elements including population initialization, crossover, and mutation are designed for genetic algorithm. Then, a novel local search procedure has been developed and incorporated into genetic algorithm to enhance its search ability. In experimental study, the performance of the proposed algorithm to find optimal and near-optimal solutions has been demonstrated based on some test problems which are built by incorporating processing time flexibility into a number of problems adopted from the literature.