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研究了带恶化工件的置换流水车间调度问题,其中工件的加工时间是与开始时间有关的线性函数,考虑不同工件在不同机器上具有不同的恶化率,以最小化最大完工时间为目标,建立数学规划模型,进而提出了一种混合遗传算法来求解。该算法引入一种启发式规则以产生m-1条染色体改进初始种群的40%,结合遗传算法的初始种群产生方法共同生成种群,设计遗传参数自适应调节。仿真实验测试和对比了启发式法、遗传算法和混合遗传算法三种求解方法,实验结果表明所提出的混合遗传算法能更有效地求解这类NP-hard问题。
In this paper, the scheduling problem of displacement flow shop with deteriorating workpieces is studied. The machining time of workpieces is a linear function related to the start time. Considering different workpieces have different deteriorating rates on different machines, aiming to minimize the maximum completion time, Planning model, and then put forward a hybrid genetic algorithm to solve. The algorithm introduced a heuristic rule to generate m-1 chromosomes to improve 40% of the initial population, combined with the initial population generation method of genetic algorithm to generate the population, and the genetic parameters were designed and adjusted adaptively. The simulation experiment tests and compares heuristic method, genetic algorithm and hybrid genetic algorithm. The experimental results show that the proposed hybrid genetic algorithm can solve these NP-hard problems more effectively.