采用双档案协同进化离散多目标烟花算法的低碳疫苗冷链优化配送

计算机工程与科学 ›› 2022, Vol. 44 ›› Issue (12): 2255-2265.

采用双档案协同进化离散多目标烟花算法的低碳疫苗冷链优化配送

申晓宁1，2，3，游璇1，陈庆洲1，潘红丽1，黄遥1

（1.南京信息工程大学自动化学院,江苏南京 210044；2.江苏省大气环境与装备技术协同创新中心,江苏南京 210044；
3.江苏省大数据分析技术重点实验室，江苏南京 210044）

收稿日期:2021-05-12 修回日期:2021-06-28 接受日期:2022-12-25 出版日期:2022-12-25 发布日期:2023-01-05
基金资助:
国家自然科学基金(61502239)；江苏省自然科学基金(BK20150924)

A multi-objective fireworks algorithm with two-archive coevolution for low-carbon cold chain distribution optimization of vaccines

SHEN Xiao-ning1,2,3,YOU Xuan1,CHEN Qing-zhou1,PAN Hong-li1,HUANG Yao1

(1.School of Automation,Nanjing University of Information Science and Technology,Nanjing 210044;
2.Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology,Nanjing 210044;
3.Jiangsu Key Laboratory of Big Data Analysis Technology,Nanjing 210044,China)

Received:2021-05-12 Revised:2021-06-28 Accepted:2022-12-25 Online:2022-12-25 Published:2023-01-05

摘要/Abstract

摘要： 建立低碳疫苗冷链配送问题的约束多目标优化模型，在满足可用车数量、车辆容量约束和时间窗约束的条件下，考虑最小化碳排放的企业运输成本和客户不满意度。提出一种双档案协同进化的离散多目标烟花算法，采用消除车辆数量和容量约束的解码方式，设计了部分映射爆炸算子，设置可行解档案和不可行解档案协同进化，并对不可行解档案实施可行性搜索。实验结果表明，与已有算法相比，所提算法在低碳疫苗冷链配送问题上能高效地搜索到一组收敛精度和分布性能更优的Pareto非支配解。

关键词: 低碳, 疫苗配送, 多目标优化, 烟花算法, 约束处理, 协同进化

Abstract: A constrained multi-objective optimization model for the low-carbon-cold chain distribution of vaccines is established to minimize the corporate transportation costs including the cost of carbon emissions and customer dissatisfaction, satisfying the constraints of the number of available vehicles, vehicle capacity and time window. A discrete two-archive-based multi-objective fireworks algorithm is proposed. The decoding method that can meet the constraints of the number of available vehicles and vehicle capacity is adopted. The partial mapping explosion operator is designed. Feasible solution archive and infeasible solution archive are set for coevolution. Feasibility search is performed on the infeasible solution archive. Experimental results show that, compared with the existing algorithms, the proposed algorithm can effectively obtain a group of Pareto non-dominated solutions with better convergence and distribution.

Key words: low-carbon, vaccine distribution, multi-objective optimization, fireworks algorithm, constraint handling, coevolution

申晓宁, 游璇, 陈庆洲, 潘红丽, 黄遥. 采用双档案协同进化离散多目标烟花算法的低碳疫苗冷链优化配送[J]. 计算机工程与科学, 2022, 44(12): 2255-2265.

SHEN Xiao-ning, YOU Xuan, CHEN Qing-zhou, PAN Hong-li, HUANG Yao. A multi-objective fireworks algorithm with two-archive coevolution for low-carbon cold chain distribution optimization of vaccines[J]. Computer Engineering & Science, 2022, 44(12): 2255-2265.

参考文献［17］

［1］	Li Y,Lim M K,Tseng M L.A green vehicle routing model based on modified particle swarm optimization for cold chain logistics［J］.Industrial Management & Data Systems,2019,119(3):473-494.
［2］	de Carvalho M I, Ribeiro D,Barbosa-Povoa A P.Design and planning of sustainable vaccine supply chain［M］∥Pharmaceutical Supply Chains—Medicines Shortages.Switzerland:Springer,2019.
［3］	Qi L,Qza C,Bl D.Cold chain transportation decision in the vaccine supply chain［J］.European Journal of Operational Research,2020,283(1):182-195.
［4］	Zheng Hao.Application research on the strategy for the vaccine refrigerated transport［D］.Shanghai:Shanghai Jiao Tong University,2014.(in Chinese)
［5］	Chen Yong,Lü Wen-jin,Jiang Jing-tao.Research on cold-chain delivery model of multi-vaccine based on time constraint［J］.Logistics Sci-Tech,2017,40(9):24-26.(in Chinese)
［6］	Zheng Chun-jiao.Research on the construction of vaccine cold chain distribution logistics system of Taifeng biological products Co.,Ltd.［D］.Changchun:Jilin University,2018.(in Chinese)
［7］	Tan Ying,Ding Jin-liang.Fireworks algorithm［M］.Beijing:Science Press,2015.(in Chinese)
［8］	Liu F,Xiao B,Li H.Finding key node sets in complex networks based on improved discrete fireworks algorithm［J］.Journal of Systems Science and Complexity,2021,34(3):1014-1027.
［9］	Zare M,Narimani M R,Malekpour M,et al.Reserve constrained dynamic economic dispatch in multi-area power systems:An improved fireworks algorithm［J］.International Journal of Electrical Power & Energy Systems,2021,126:106579.
［10］	Zhang Shi-bin,Li Jing-guang,Chen Wei-hong,et al.Day- ahead energy transaction for commercial buildings based on improved bare bones fireworks algorithm［J］.Power System and Clean Energy,2020,36(4):14-22.(in Chinese)
［11］	Tian Ye.Research on distribution path optimization of electric refrigerated vehicle with fuzzy time window［D］.Hangzhou:Zhejiang University of Technology,2020.(in Chinese)
［12］	Tang Hui-ling,Tang Heng-shu,Zhu Xing-liang.Research on low-carbon vehicle routing problem based on modified ant colony algorithm［J］.Chinese Journal of Management Science,2021,29(7):118-127.(in Chinese)
［13］	Deb K,Mohan M,Mishra S.Towards a quick computation of well-spread pareto-optimal solutions［C］∥Proc of International Conference on Evolutionary Multi-Criterion Optimization,2003:222-236.
［14］	Sun Y,Yen G G,Yi Z.IGD indicator-based evolutionary algorithm for many-objective optimization problems［J］.IEEE Transactions on Evolutionary Computation,2019,23(2):173-187.
［15］	Singh H K.Understanding hypervolume behavior theoretically for benchmarking in evolutionary multi/many- objective optimization［J］.IEEE Transactions on Evolutionary Computation,2020,24(3):603-610.
［16］	Li Yan-ming.Research on VRP optimization in cold chain logistics in the vision of low-carbon［D］.Qinhuangdao:Yanshan University,2017.(in Chinese)
［17］	Xu Wen-hao.Research on multi-objective distribution path of urban cold chain considering distribution plan［D］.Beijing:Beijing Jiaotong University,2019.(in Chinese)
［18］	Chen J,Dan B,Shi J.A variable neighborhood search approach for the multi-compartment vehicle routing problem with time windows considering carbon emission［J］.Journal of Cleaner Production,2020,277:123932.
［19］	He L,Li W,Zhang Y,et al.A discrete multi-objective fireworks algorithm for flowshop scheduling with sequence- dependent setup times［J］.Swarm and Evolutionary Computation,2019,51:100575.
［20］	Tan K C,Chew Y H,Lee L H.A hybrid multiobjective evolutionary algorithm for solving vehicle routing problem with time windows［J］.Computational Optimization & Applications,2006,34(1):115-151.
［21］	Song X,Zheng K,Yan W U.Multi-objective hybrid DE algorithm for solving VRPTW［C］∥Proc of 2017 International Conference on Mathematics,Modelling and Simulation Technologies and Applications,2017:447-452.
［22］	Zhang W,Hou W,Yang D,et al.Multiobjective evolutionary algorithm with fast convergence strategy for clustered vehicle routing problem with time window［C］∥Proc of 2019 IEEE International Conference on Smart Manufacturing,Industrial & Logistics Engineering,2019:210-214.
［23］	Liu L,Zheng S,Tan Y.S-metric based multi-objective fireworks algorithm［C］∥Proc of 2015 IEEE Congress on Evolutionary Computation,2015:1257-1264.
	附中文参考文献：
［4］	郑浩.疫苗冷藏运输策略的应用研究［D］.上海:上海交通大学,2014.
［5］	陈勇,吕文锦,蒋景涛.基于时间约束的多品种疫苗冷链配送模型研究［J］.物流科技,2017,40(9):24-26.
［6］	郑春姣.泰丰生物制品有限公司疫苗冷链配送物流体系构建研究［D］.长春:吉林大学,2018.
［7］	谭营,丁进良.烟花算法引论［M］.北京:科技出版社,2015.
［10］	张世斌,李敬光,陈威洪,等.基于改进精简爆炸烟花的区域商业楼宇日前电能交易［J］.电网与清洁能源,2020,36(4):14-22.
［11］	田野.带模糊时间窗的纯电动冷藏车配送路径优化研究［D］.杭州:浙江工业大学,2020.
［12］	唐慧玲,唐恒书,朱兴亮.基于改进蚁群算法的低碳车辆路径问题研究［J］.中国管理科学,2021,29(7):118-127.
［16］	李艳明.低碳视阈下冷链物流VRP优化研究［D］.秦皇岛:燕山大学,2017.
［17］	徐文豪.考虑配货计划的城市冷链多目标配送路径研究［D］.北京:北京交通大学,2019.

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