基于改进NSGA-II算法的云制造服务组合优化研究

doi:10.12005/orms.2022.0349

运筹与管理 ›› 2022, Vol. 31 ›› Issue (11): 30-36.DOI: 10.12005/orms.2022.0349

基于改进NSGA-II算法的云制造服务组合优化研究

邵一凡^1,2, 禹春霞¹, 禹嘉诚³

1.中国石油大学 (北京)经济管理学院,北京 102249;
2.厦门大学管理学院,福建厦门 361005;
3.北京体育大学体育商学院,北京 100084

收稿日期:2020-09-25 出版日期:2022-11-25 发布日期:2022-12-14
通讯作者: 禹春霞(1983-),女,山东德州人,副教授,博士生导师,博士,研究方向:物流与供应链管理。
作者简介:邵一凡(1996-),女,山东淄博人,博士研究生,研究方向:物流与供应链管理;禹嘉诚(2001-),男,山东德州人,硕士研究生,研究方向:物流与供应链管理。
基金资助:
国家自然科学基金资助项目(72271244)

Cloud Manufacturing Service Composition Optimization Based on Improved NSGA-II Algorithm

SHAO Yi-fan^1,2, YU Chun-xia¹, YU Jia-cheng³

1. School of Economics and Management, China University of Petroleum, Beijing 102249, China;
2. School of Management, Xiamen University, Xiamen 361005, China;
3. Sport Business School, Beijing Sport University, Beijing 100084, China

Received:2020-09-25 Online:2022-11-25 Published:2022-12-14

摘要/Abstract

摘要： 云制造任务日趋复杂,与基于云制造的云服务组合优化问题相关的指标日益增多,需要综合考虑各个评价指标,从海量备选云服务中筛选出最优服务组合。本文针对云制造的特点,从线上、线下两方面构建了云制造服务评价指标体系;为了更好地处理高维多目标优化问题并消除实际问题中的量纲影响,本文利用改进的α支配策略代替帕累托支配改进NSGA-II算法,提出了基于nα支配的NSGA-II算法。最后,本文通过一个电机制造案例验证了提出算法的可行性,并通过与标准NSGA-II算法、r-NSGA-II算法和基于模糊支配的NSGA-II算法对比,证明了提出算法得到的解集更优、更小,能够大大减小后续组合优选的计算量。

关键词: 云制造, 多目标优化, NSGA-II算法, nα支配

Abstract: The cloud manufacturing task has become more and more complex, and there are more and more criteriarelated to cloud service composition in cloud manufacturing environment. It is necessary to comprehensively consider various relevant criteria and select the optimal service composition from a large number of alternatives. According to the characteristics of cloud manufacturing, this paper formulates the cloud service evaluation criteriasystem from both online and offline aspects.In order to deal with the high-dimensional multi-objective optimization problem better and eliminate the dimensional influence in practice, this paper uses the improved α dominance strategy instead of Pareto dominance to improve the NSGA-II algorithm, and proposes the nα dominance based NSGA-II algorithm. Finally, the feasibility of the proposed algorithm is proved by a motor manufacturing case. Moreover, the solution set obtained by the proposed algorithm is better and smaller by comparing with the standard NSGA-II algorithm, r-NSGA-II algorithm and f-NSGA-II algorithm, which greatly reduces the amount of calculation for subsequent optimal composition selection.

Key words: cloud manufacturing, multi-objective optimization, NSGA-II algorithm, nα dominance

中图分类号:

C931

邵一凡, 禹春霞, 禹嘉诚. 基于改进NSGA-II算法的云制造服务组合优化研究[J]. 运筹与管理, 2022, 31(11): 30-36.

SHAO Yi-fan, YU Chun-xia, YU Jia-cheng. Cloud Manufacturing Service Composition Optimization Based on Improved NSGA-II Algorithm[J]. Operations Research and Management Science, 2022, 31(11): 30-36.

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基于改进NSGA-II算法的云制造服务组合优化研究

Cloud Manufacturing Service Composition Optimization Based on Improved NSGA-II Algorithm

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