考虑双资源约束的分布式柔性作业车间调度

Distributed flexible job shop scheduling considering dual resource constraints

随着全球制造业的发展,分布式柔性作业车间调度问题(distributed flexible job shop scheduling problem, DFJSP)引起了学者们的关注.DFJSP的研究中常常忽略工人资源,作为生产的关键因素,有效利用工人资源可以提高生产率.研究了考虑双资源约束的分布式柔性作业车间调度问题(distributed flexible job shop scheduling problem with dual resource constraints, DFJSP-DRC),建立以最小化最大完工时间和总能耗为目标的数学模型,并提出一种改进的非支配排序遗传算法(improved non-dominated sorting genetic algorithm, INSGA-Ⅱ)去求解.在INSGA-Ⅱ中,通过混合初始化策略生成高质量的初始解,并设计了一种基于加工机器和工人公共空闲时间的主动解码策略来获得调度方案.为增强INSGA-Ⅱ的全局搜索能力,提出了改进的交叉变异策略和自适应交叉变异率.通过在45个算例与三种算法的比较,验证了INSGA-Ⅱ解决DFJSP-DRC的有效性.

The distributed flexible job shop scheduling problem(DFJSP)garnered significant attention in line with the expansion of the global manufacturing industry.However,the previous DFJSP research ignored worker constraints.As one critical factor of production,the effective utilization of worker resources increased productivity.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption was studied in this paper.To solve the problem,a multi-objective mathematical model for DFJSP-DRC and an improved non-dominated sorting genetic algorithm(INSGA-Ⅱ)were proposed.In INSGA-Ⅱ,high-quality initial solutions were generated using a hybrid initialization strategy,and an active decoding strategy based on the public idle time of processing machines and workers was designed to derive the scheduling scheme.To enhance the global search capability of INSGA-Ⅱ,an improved cross-mutation strategy and an adaptive cross-mutation rate were proposed.The effectiveness of INSGA-Ⅱ in addressing DFJSP-DRC was verified through 45 comprehensive experiment instances compared with three algorithms.