退役产品性能损耗分级评价的拆解车间调度节能优化研究
Energy-saving Consumption Optimization of End-of-Life Product Disassembly Job Shop Scheduling Problem Based on Performance Loss Graded Evaluation
匡增彧 1谢晖 1任莹晖 1卓晓军 2刘洋 2易峦2
作者信息
- 1. 湖南大学机械与运载工程学院 长沙 410082
- 2. 长沙矿冶研究院有限责任公司 长沙 410012
- 折叠
摘要
为优化回收拆解车间调度方案和车间能耗,考虑退役产品不确定的性能损耗,开展回收拆解车间调度节能优化问题研究.首先,讨论退役产品性能损耗对拆解工艺规划和车间作业调度方案的影响,分析回收拆解车间的能耗构成,构建拆解车间调度节能优化模型.其次,设计一种性能损耗权重分级评价算子,求解退役产品拆解初始方案.在此基础上,设计基于拆解方式和产品工序结构的变邻域算子改进NSGA-Ⅱ算法求解车间调度节能优化模型.最后,以某企业退役动力电池回收拆解车间为例,选取三种近似元启发算法对比,验证所提车间调度节能优化模型和改进算法的正确性和有效性.结果表明:所提算法的解空间均匀且有效性,优于所选对比算法;优化模型制定的车间调度方案的最大完工时长(降低48.4％)和车间总能耗(降低35％)均优于其他调度方案.
Abstract
In order to optimize the disassembly job shop scheduling problem and energy consumption,considering the influence of uncertain performance loss of end-of-life(EOF)products,a study on the energy-saving optimization of shop scheduling problem is carried out.Firstly,the effect of product performance loss on the energy-saving optimization of disassembly plan and shop scheduling problem is analyzed.The energy consumption composition of the disassembly job shop is analyzed.An energy-saving optimization model of the disassembly job shop scheduling problem is constructed.Secondly,a performance loss graded evaluation methodology is designed to solve the initial process scheme.Then,a variable neighborhood operator is designed to improve the efficiency of solving the improved NSGA-Ⅱ algorithm.Finally,a case of the EOF automotive battery disassembly job shop is analyzed.Three heuristic algorithms are selected to verify the validity of proposed model and algorithm.The results show that the proposed algorithm has an uniform solution space.So,the efficiency of the proposed algorithm is better than that of comparison algorithm.The maximum completion time(48.4％reduction)and total energy consumption(35％reduction)of the proposed optimized process scheme are better than others.
关键词
退役产品/车间调度/性能损耗/分级评价/节能Key words
end-of-life products/shop scheduling/performance loss/graded evaluation/energy-saving引用本文复制引用
基金项目
国家重点研发计划资助项目(2020YFB1713002)
出版年
2024
NETL
NSTL
万方数据