混合离散蝙蝠算法求解多目标柔性作业车间调度

针对以最大完工时间、生产成本和生产质量为目标的柔性作业车间调度问题,在研究和分析蝙蝠算法的基础上,提出一种混合离散蝙蝠算法。为了提高求解多目标柔性作业车间调度问题的混合离散蝙蝠算法的初始种群质量,在通过分析初始选择的机器与每道工序调度完工时间两者关系的基础上,提出一种优先指派规则策略产生初始种群,提高了算法的全局搜索能力。同时采用位置变异策略来使得算法在较短的时间内尽可能多地搜索到最优位置,有效地避免了算法早熟收敛。在计算问题的目标值上面,首次提出时钟算法。针对具体实例进行测试,试验数据表明,该算法在求解柔性作业车间调度问题上有很好的性能,是一种有效的调度算法,从而为解决这类问题提供了新的途径和方法。

Hybrid Discrete Bat Algorithm for Solving the Multi-objective Flexible Job Shop Scheduling Problem

Aiming at the flexible job shop scheduling problem with the goal of the maximum completion time, production cost and production quality, a hybrid discrete bat algorithm is proposed based on studying and analyzing the bat algorithm. In order to improve the quality of initial population of the hybrid discrete bat algorithm for solving the multi-objective flexible job shop scheduling problem, a priority assignment rule is proposed to produce initial population which improves the global searching ability of the algorithm based on the analysis of both initial machine selection and scheduling completion time of each process. At the same time, use position mutation strategy to search the optimal location as much as possible in a relatively short time, which can avoid the premature convergence effectively. The clock algorithm is proposed for the first time in the target value of the calculation problem. Commence from the concrete examples, the experimental results show that the algorithm has good performance in solving the flexible job shop scheduling problem, and it is an effective scheduling algorithm, which provides a new way to solve this kind of problems.