基于多目标生物地理学优化算法的模糊节能分布式流水车间调度  

Fuzzy Energy-Efficient Distributed Flow Shop Scheduling Based on Multi-Objective Biogeography Optimization Algorithm

作　　者：张振[1] 张丫丫 孙美玲 顾幸生[1] ZHANG Zhen;ZHANG Yaya;SUN Meiling;GU Xingsheng(Key Laboratory of Smart Manufacturing in Energy Chemical Process,Ministry of Education,East China University of Science and Technology,Shanghai 200237,China)

机构地区：[1]华东理工大学能源化工过程智能制造教育部重点实验室,上海200237

出　　处：《华东理工大学学报（自然科学版）》2024年第6期869-877,共9页Journal of East China University of Science and Technology

基　　金：国家自然科学基金(61973120)。

摘　　要：研究了模糊节能分布式置换流水车间多目标调度问题(Fuzzy Energy Efficient Distributed Permutation Flow Shop Problem,FEEDPFSP),针对最小化模糊完工时间和模糊能耗两个优化目标,提出了一种基于生物地理学的多目标优化算法(Multiple-Objective Biogeography-Based Optimization,MOBBO)。在MOBBO中,设计了一种有效的初始解生成规则,并根据两个优化目标之间的关系,设计了4种速度操作方法用于迁移过程和变异过程,并且引入快速非支配排序以及拥挤距离方法,保证每次迭代的种群质量。对比两个先进算法在480个不同规模实例下的表现,验证了所提出的MOBBO算法在解决FEEDPFSP问题的有效性。In the face of increasingly severe ecological issues,sustainable development and green manufacturing have garnered significant attention.Meanwhile,with the development of globalization,distributed manufacturing is becoming more prevalent.In this paper,the Fuzzy Energy Efficient Distributed Permutation Flow Shop Problem(FEEDPFSP)is studied,with the objective to minimize fuzzy makespan and fuzzy energy consumption.A Multiple-Objective Biogeography-Based Optimization(MOBBO)is proposed for FEEDPFSP.In MOBBO,an effective initial solution generation rule is designed,and according to the relationship between the two optimization objectives,four speed operation methods are designed for the migration process and the mutation process,and fast non-dominated sorting and crowding distance methods are introduced to ensure the population quality of each iteration.A comparison of the performance of the proposed MOBBO algorithm with two state-of-the-art algorithms across 480 instances of different scales demonstrates the effectiveness of the algorithm in addressing the FEEDPFSP.

关 键 词：分布式流水车间调度 生物地理学优化 模糊规划 节能 多目标优化 

分 类 号：TB497[一般工业技术] TP18[自动化与计算机技术—控制理论与控制工程]