A new approach based on Ant Colony Optimization (ACO) to determine the supply chain (SC) design for a product mix

doi:10.4304/jcp.7.3.736-742

	A new approach based on Ant Colony Optimization (ACO) to determine the supply chain (SC) design for a product mix
	Zhao, FuQing1,2 ; Tang, JianXin1 ; Yang, YaHong3
	2012
发表期刊	Journal of Computers
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
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.