| Author Name | Affiliation | | Ceyuan Liang | School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350116, Fujian, China | | Lijun He | School of Logistics Engineering, Wuhan University of Technology, Wuhan 430070, China | | Guangyu Zhu | School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350116, Fujian, China |
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| Abstract: |
| From the perspective of the geographical distribution, considering production fare, supply chain information and quality rating of the manufacturing resource (MR), a manufacturing resource allocation (MRA) model considering the geographical distribution in cloud manufacturing (CM) environment is built. The model includes two stages, preliminary selection stage and optimal selection stage. The membership function is used to select MRs from cloud resource pool (CRP) in the first stage, and then the candidate resource pool is built. In the optimal selection stage, a multi-objective optimization algorithm, particle swarm optimization (PSO) based on the method of relative entropy of fuzzy sets (REFS_PSO), is used to select optimal MRs from the candidate resource pool, and an optimal manufacturing resource supply chain is obtained at last. To verify the performance of REFS_PSO, NSGA-II and PSO based on random weighting (RW_PSO) are selected as the comparison algorithms. They all are used to select optimal MRs at the second stage. The experimental results show solution obtained by REFS_PSO is the best. The model and the method proposed are appropriate for MRA in CM. |
| Key words: cloud manufacturing resource optimization allocation Fuzzy Sets Relative Entropy many-objective optimization supply chain |
| DOI：10.11916/j.issn.1005-9113.17134 |
| Clc Number:TP391 |
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| Descriptions in Chinese: |
| 考虑地理位置分布的制造资源配置优化 梁策远1, 贺利军2, 朱光宇1 （1.福州大学 机械工程及自动化学院 福州 350116； 2武汉理工大学 物流工程学院， 武汉 430070） 创新点说明：1) 建立了云制造环境下跨地理位置制造资源配置的模型，包括初选和优选两个阶段。 2) 优选阶段采用了基于模糊集关联熵的PSO算法解决高维多目标优化，此方法要好于NSGA-II和基于随机权重的PSO算法。 3) 考虑了配置过程中制造资源正常和出错两种情况，突显了该方法所具有的实时性，动态性和容错性。 研究目的： 建立云制造中跨地理位置资源配置模型，优化配置方案，实现配置的实时性，并能在资源出现异常情况时做出动态响应，提高容错率。 结果： 在制造资源正常和出错的情况下，基于模糊集关联熵的PSO算法均能正常得出配置优化结果，适应度值()分别达到了0.745和0.718（此数值越大表示越接近理想解），要好于NSGA-II和基于随机权重的PSO算法所得到的结果。 结论： 建立的资源配置模型是合理的，能有效的应对制造资源出错的情形。基于模糊集关联熵的PSO算法可以有效的得出优化结果。 关键词：云制造; 资源优化配置; 模糊关联熵; 高维目标优化; 供应链 |
