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  • 中国标准连:ISSN1005-2895
  • 续出版物号: CN 33-1180/TH
  • 主管单位:轻工业杭州机电设计研究院有限公司
  • 主办单位:轻工业杭州机电设计研究院有限公司、中国轻工机械协会、中国轻工业机械总公司
  • 社  长:刘安江
  • 主  编:黄丽珍
  • 地  址:杭州市余杭区高教路970号西溪联合科技广场4-711
  • 电子邮件:qgjxzz@126.com
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周姝含, 吉卫喜, 卢璟钰, 崔志鹏.考虑并行制造的云服务外购件供应商组合优选[J].轻工机械,2023,41(4):99-108
考虑并行制造的云服务外购件供应商组合优选
Combination Optimization of Cloud Service Outsourcing Parts Suppliers Considering Parallel Manufacturing
  
DOI:10.3969/j.issn.1005 2895.2023.04.013
中文关键词:  云制造服务  组合优选  供应商  外购件  非支配排序遗传算法
英文关键词:cloud manufacturing service  combination optimization  supplier  purchased parts  NSGA(Non dominated Sorting Genetic Algorithm)
基金项目:山东省重大科技创新工程基金项目(2019JZZY020111)。
作者单位
周姝含, 吉卫喜, 卢璟钰, 崔志鹏 1.江南大学 机械工程学院 江苏 无锡214122 2.江南大学 江苏省食品制造装备重点实验室 江苏 无锡214122 
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中文摘要:
      云制造环境下,为解决并行结构下外购件供应商组合优选难的问题,在考虑并行制造的前提下,课题组以时间、成本、可靠性和灵活性为优化目标,构建了云服务多目标组合优选模型。提出了一种改进的非支配排序遗传算法(NSGA Ⅲ TS)对模型进行求解,该算法采用反向学习策略提高初始解的质量;而后使用融合邻域搜索(采用交换、反转和插入3种方式)和禁忌搜索算法的策略加强局部搜索,提高算法后期局部寻优能力;对得到的一系列优选供应商组合综合评价,在考虑实际需求的基础上为需求方选出了最优供应商组合及备选方案。通过实际案例验证了该模型的有效性和算法的可行性。
英文摘要:
      〖WT5HZ〗Abstract:〖WT5BZ〗In the cloud manufacturing environment, in order to solve the problem of combination optimization of outsourced parts suppliers under the parallel structure, under the premise of considering parallel manufacturing, a cloud service multi objective combination optimization model was constructed with time, cost, reliability and flexibility as the optimization objectives. An improved non dominated sorting genetic algorithm (NSGA III TS) was proposed to solve the model, the reverse learning strategy was used to improve the quality of the initial solution. A strategy combining neighborhood search (exchange, inversion and insertion) and tabu search to strengthen local search was adopted to enhance the local optimization ability in the later stage of the algorithm. Based on the comprehensive evaluation of the obtained series of preferred supplier combinations, the optimal supplier combinations and alternatives were selected for the demand side on the basis of considering the actual demand. The validity of the model and the feasibility of the algorithm were verified by a practical case.
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