Lanzhou University of Technology Institutional Repository (LUT_IR)
A new approach based on Ant Colony Optimization (ACO) to determine the supply chain (SC) design for a product mix | |
Zhao, FuQing1,2![]() ![]() ![]() | |
2012 | |
发表期刊 | Journal of Computers
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ISSN | 1796203X |
卷号 | 7期号:3页码:736-742 |
摘要 | Manufacturing supply chain(SC) faces changing business environment and various customer demands. Pareto Ant Colony Optimisation (P-ACO) in order to obtain the non-dominated set of different SC designs was utilized as the guidance for designing manufacturing SC. PACO explores the solution space on the basis of applying the Ant Colony Optimisation algorithm and implementing more than one pheromone matrix, one for every objective. The SC design problem has been addressed by using Pareto Ant Colony Optimisation in which two objectives are minimised simultaneously. There were tested two ways in which the quantity of pheromones in the PM is incremented. In the SPM, the pheromone increment is a function of the two objectives, cost and time, while in MPM the pheromone matrix is divided into two pheromones, one for the cost and another one for the time. It could be concluded that the number of solutions do not depend on if the pheromone is split or is a function of the two variables because both method explore the same solution space. Although both methods explore the same solution space, the POS generated by every one is different. The POS that is generated when the pheromone matrix is split got solutions with lower time and cost than SMP because in the probabilistic decision rule a value of λ = 0.2 is used. It means that the ants preferred solution with a low cost instead of solutions with low time. The strategy of letting the best-so-far ant deposit pheromone over the PM accelerates the algorithm to get the optimal POS although the number of ants in the colony is small. An experimental example is used to test the algorithm and show the benefits of utilising two pheromone matrices and multiple ant colonies in SC optimisation problem. © 2012 ACADEMY PUBLISHER. |
关键词 | Artificial intelligence Hormones Manufacture Matrix algebra Multiobjective optimization Product design Supply chains Ant colonies Ant Colony Optimization (ACO) Changing business environment Meta heuristics Optimisation problems Preferred solutions Probabilistic decisions Supply chain design |
DOI | 10.4304/jcp.7.3.736-742 |
收录类别 | EI ; ESCI |
语种 | 英语 |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Interdisciplinary Applications |
WOS记录号 | WOS:000218096100021 |
出版者 | Academy Publisher |
EI入藏号 | 20121414925773 |
EI主题词 | Ant colony optimization |
EI分类号 | 461.2 Biological Materials and Tissue Engineering - 912 Industrial Engineering and Management - 913 Production Planning and Control ; Manufacturing - 921.1 Algebra - 921.5 Optimization Techniques |
来源库 | Compendex |
分类代码 | 461.2 Biological Materials and Tissue Engineering - 723.4 Artificial Intelligence - 912 Industrial Engineering and Management - 913 Production Planning and Control; Manufacturing - 921.1 Algebra - 921.5 Optimization Techniques |
引用统计 | 无
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文献类型 | 期刊论文 |
条目标识符 | https://ir.lut.edu.cn/handle/2XXMBERH/111496 |
专题 | 国际合作处(港澳台办) 计算机与通信学院 土木工程学院 |
通讯作者 | Zhao, FuQing |
作者单位 | 1.Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou, Gansu, Peoples R China; 2.Key Lab Gansu Adv Control Ind Proc, Lanzhou, Gansu, Peoples R China; 3.LanZhou Univ Technol, Coll Civil Engn, Lanzhou, Gansu, Peoples R China |
第一作者单位 | 兰州理工大学 |
通讯作者单位 | 兰州理工大学 |
第一作者的第一单位 | 兰州理工大学 |
推荐引用方式 GB/T 7714 | Zhao, FuQing,Tang, JianXin,Yang, YaHong. A new approach based on Ant Colony Optimization (ACO) to determine the supply chain (SC) design for a product mix[J]. Journal of Computers,2012,7(3):736-742. |
APA | Zhao, FuQing,Tang, JianXin,&Yang, YaHong.(2012).A new approach based on Ant Colony Optimization (ACO) to determine the supply chain (SC) design for a product mix.Journal of Computers,7(3),736-742. |
MLA | Zhao, FuQing,et al."A new approach based on Ant Colony Optimization (ACO) to determine the supply chain (SC) design for a product mix".Journal of Computers 7.3(2012):736-742. |
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