自适应Jaya算法求解多目标柔性车间绿色调度问题
作者:
作者单位:

1.江苏大学 管理学院 工业工程系;2.江苏大学 管理学院

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中图分类号:

TP301

基金项目:

国家自然科学基金面向项目71673118


Multi-objective flexible job shop green scheduling problem with self-adaptive Jaya algorithm
Author:
Affiliation:

College of management, Jiangsu University

Fund Project:

Project supported by the National Natural Science Foundation, China (N0.716731118).

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    摘要:

    针对多目标柔性作业车间绿色调度问题(MO-FJGSP),建立优化目标为最大完工时间、机器总负荷和能耗最小的多目标数学模型,并设计了一种基于Pareto最优解的自适应多目标Jaya算法(SAMO-Jaya)对该问题进行优化求解.该算法采用两级实数编码方式实现工序排序与机器分配的编码表示,并设计一种转换机制实现将Jaya连续解空间映射至FJSP离散解空间;然后设计了一种混沌序列与均匀分布相结合的混合策略以提高初始种群的质量与全局分散性;此外,在Jaya算法中嵌入了自适应调整种群规模的方法以提高算法求解速度.通过10个单目标与3个多目标基准算例测试,并与7个已有算法进行对比分析,结果表明SAMO-Jaya算法能够对MO-FJGSP进行有效求解.

    Abstract:

    A mathematical model aiming at minimizing the makespan, total machine utilization and energy consumption is established according to the multi-objective flexible job shop green scheduling problem. A self-adaptive multi-objective Jaya algorithm (SAMO-Jaya) based on Pareto optimal is designed to optimize the model. And a two-level real number encoding is adopted to implement the coding scheme of processes’ sequences and machines’ assignment, then a transformation mechanism is designed to create a mapping between the continuous solution space of Jaya and the discrete solution space of FJSP. And then a hybrid strategy combining chaotic sequence and uniform distribution is raised to improve the quality and diversity of the initial populations. In addition, a self-adaptive population size adjusting method is embedded to improve the optimizing speed of the algorithm. By analyzing the solutions of 10 single-objective benchmarks and 3 multi-objective benchmarks solved by SAMO-Jaya and other 7 existing algorithms, the results show that SAMO-Jaya can solve the MO-FJGSP effectively.

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历史
  • 收稿日期:2019-12-19
  • 最后修改日期:2021-01-21
  • 录用日期:2020-03-18
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