Research on Partner Selection in Green Supply Chain Based on Angle Penalty Distance Elite Selection Strategy
GUO Hai-dong1,4,WANG Li-ping2,ZHANG Ming-lei3
1(College of Business Administration,Zhejiang University of Technology,Hangzhou 310023,China)2(College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China)3(College of Business,Zhejiang Industry Polytechnic College,Shaoxing 312000,China)4(College of Educational Science & Technology,Zhejiang University of Technology,Hangzhou 310023,China)
Abstract：Aiming at the problem of the typical many-objective optimization in the partner selection of green supply chain,the three stage green supply chain network optimization model was established by taking operation cost,distribution time,product quality and green degree as the optimization goal,and the many-objective optimization algorithm based on angle penalty distance(APD)elite strategy was proposed to solve this problem.The APD mechanism only uses decomposition strategy to optimize the population,ignoring the Pareto relationship between individuals,and easier leading to population deficiencies.This paper proposed a manyobjective optimization algorithm improved by introducing non-dominated sorting methods to first perform Pareto non-dominated sorting of individuals within subpopulations.Then the remaining individuals were screened through the APD mechanism,which increased selection pressure and convergence speed while maintaining population diversity.The simulation results revealed that the proposed algorithm had the advantages of strong convergence,good overall performance and low computational complexity.The set of solutions was superior to the MOGA,NSGA-II,MOEA/D and RVEA in many aspects such as the IGD* index,the average number of the Pareto optimal solutions and the running time.
郭海东,王丽萍,章鸣雷. 角度惩罚精英策略的绿色供应链伙伴选择研究[J]. 小型微型计算机系统, 2019, 40(7): 1442-1448.
GUO Hai-dong,WANG Li-ping,ZHANG Ming-lei. Research on Partner Selection in Green Supply Chain Based on Angle Penalty Distance Elite Selection Strategy. Journal of Chinese Computer Systems, 2019, 40(7): 1442-1448.
2019, Vol. 40 
