改进遗传算法求解织造车间并行批调度问题

Improved Genetic Algorithm for Parallel Batch Scheduling in Weaving Workshop

针对织造车间并行批处理调度问题,提出一种改进遗传算法用于最大完工时间最小化求解。首先,采用实数编码方式进行编码操作;然后,引入模拟退火算法的Metropolis机制,从而增强遗传算子在该调度问题的可行解集空间中寻优的能力;最后,通过随机生成的150个仿真测试集对算法进行求解性能上的比较分析,并将测试结果与文献中提到的BSNRPSO算法和另外一种差分进化算法进行比较分析。经过实验证明,本文改进遗传算法在求解性能上明显优于对比算法。

Aiming at the parallel batch scheduling problem in weaving workshop,an improved genetic algorithm is proposed to minimize the maximum completion time.Firstly,the real number coding method is used for coding operation;Then,the metropolis of simulated annealing algorithm is introduced to enhanced the optimization ability of genetic operator in the feasible solution set space of the scheduling problem;Finally,the performance of the algorithm is compared and analyzed through 150 randomly generated simulation test sets.Then,the test results are compared with the BSNRPSO algorithm mentioned in the literature and another differential evolution algorithm.Experiments show that the improved genetic algorithm is significantly better than its comparison algorithm in solving performance.