The application of Adaptive Genetic Algorithms in FMS dynamic rescheduling
The application of Adaptive Genetic Algorithms in FMS dynamic rescheduling
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DOI：
10.1080/0951192031000077870
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发表时间：
2003-01
影响因子：
4.1
通讯作者：
Honghong Yang;Zhi-ming Wu
中科院分区：
文献类型：
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作者：
Honghong Yang;Zhi-ming Wu
在柔性制造系统（FMS）中，生产过程中的不确定性将不可避免地导致偏离现有计划的偏差。本文试图研究在现实环境中的中断和时间限制的响应要求在重新调度的问题。在本文中，重新调度系统是基于动态数据库（DDB）的记录。它能够通过精确地总结剩余资源和在制品（WIPs）来改革受干扰系统的最新状态。以其为新的初始状态，结合现有调度顺利配置新调度，提高柔性制造系统在这一关键时刻的效率。在兼顾速度和经济效益的基础上，提出了一种自适应遗传算法（阿加），用于求解大型复杂作业车间FMS在发生故障后短期内的次优调度问题。阿加算法的设计是为了防止早熟收敛和改善遗传算法在重调度中的性能。在阿加遗传算法的进化过程中，交叉和变异的概率是变化的，这取决于适应值和规范化的适应值之间的解决方案的距离。在DDB、FMS调度模型和阿加算法的帮助下，在调度实例中得到了令人满意的结果。
Uncertainties in the production process would inevitably result in deviations from the existing schedule in flexible manufacturing systems (FMSs). This paper tries to study the problem in an environment with realistic interruptions and a requirement of time-restricted response in rescheduling. In our paper, the rescheduling system is based on the records of a dynamic database (DDB). It is able to reform the up-to-date status of a disturbed system via summarizing the remaining resources and works in process (WIPs) precisely. By using it as the new initial state, the new schedule is configured smoothly in conjunction with the existing schedule to improve the efficiency of FMS at this critical instant. Considering both speed and economic benefit, an adaptive genetic algorithm (AGA) is proposed for finding the new sub-optimal schedule of a large and complicated job shop FMS shortly after the interruption occurred. The AGA is designed to prevent the premature convergence and refine the performance of genetic algorithms in re-scheduling. During the AGA evolution process, the probabilities of crossover and mutation are varied, depending on both the fitness value and the normalized fitness distances between solutions. With help from DDB, the FMS scheduling model and AGA, the results obtained in the test examples of rescheduling are quite satisfactory.