基于混合遗传算法车间多工艺路线批量调度

Hybrid genetic algorithm for multiple process and batch scheduling in job-shop

结合启发式分派规则和模拟退火算法,给出了改进的遗传算法——遗传退火算法(GASA,Genetic Algorithm-Simulated Annealing Algorithm).该算法采用新型POX交叉算子,通过结合模拟退火算法,有效地避免了基本遗传算法解决车间调度早熟的问题,并通过实验验证了该算法的有效性.基于GASA研究了航空复杂产品制造车间中,考虑生产批量、生产转换时间、允许多设备加工路线的车间静态与动态调度问题,分析并验证了不同分批方法对考虑以上因素的车间生产调度结果的影响.该算法已应用到某航空车间生产计划与控制系统中.

Combined with the heuristic rule and the simulated annealing algorithm, an improved genetic algorithm GASA （genetic algorithm-simulated annealing algorithm） was put forward. This algorithm adopts the new type of POX cross operators and takes advantage of the simulated annealing algorithm which could effectively avoid the earliness problems based on the basic genetic algorithm. The availability of this algorithm was also validated through experiments. Grounded on GASA, considering the production batch, production transition time and multi-equipment process path, the problems of static and and dynamic scheduling in aeronautic workshop were researched, and then the effects brought by the different batching methods were discussed and validated as well. The algorithm now has been used in some production planning and control system in large scale enterprise.