Abstract:With the rapid development of the cold chain logistics industry, enterprises are paying more attention to the cost, quality of logistics services and carbon emissions generated by transportation. In order to solve this problem, a multi- depot half-open cold chain logistics routing optimization model is constructed, and various dynamic changes are integrated into the model. An improved NSGA-II algorithm is designed and the standard test function is used to prove that the algorithm has good convergence and diversity. The results show that, compared with the scheme without considering carbon emission, the distribution cost of the scheme with considering carbon emission increases by 19.00%, the transportation distance decreases by 5.16%, and the carbon emission cost decreases by 13.03%. Compared with the initial distribution scheme, the distribution cost of the real-time optimization scheme decreased by 29.78%, the carbon emission cost decreased by 23.19%, and the customer satisfaction increased by 14.49%. Compared with the single distribution center mode, the distribution cost of the multi-depot mode is reduced by 27.30%, the carbon emission cost is reduced by 48.89%, and the customer satisfaction is increased by 13.11%, which brings certain management enlightenment for cold chain logistics enterprises.
[1]LENG L, ZHANG C, ZHAO Y, et al. Biobjective Low-carbon location-routing problem for cold chain logistics:formulation and heuristic approaches[DB/OL].[2023-01-28].https://doi.org/10.1016/j.jclepro.2020.122801. [2]LIU G, HU J, YANG Y, et al. Vehicle routing problem in cold chain logistics: a joint distribution model with carbon trading mechanisms[DB/OL].[2023-01-28]. https://doi.org/10.1016/j.resconrec.2020.104715. [3]LIAN J. An optimization model of cross-docking scheduling of cold chain logistics based on fuzzy time window[J]. Journal of Intelligent and Fuzzy Systems, 2021, 41(1):1901-1915. [4] 李倩,蒋丽,梁昌勇. 基于模糊时间窗的多目标冷链配送优化[J]. 计算机工程与应用, 2021, 57(23): 255-262. LI Qian, JIANG Li, LIANG Changyong. Multi-objective cold chain distribution optimization based on fuzzy t-ime window [J]. Computer Engineering and Applicati-ons, 2021, 57(23): 255-262. [5]任腾,罗天羽,李姝萱,等. 面向冷链物流配送路径优化的知识型蚁群算法[J]. 控制与决策, 2022, 37(3): 545-554. REN Teng, LUO Tianyu, LI Shuxuan, et al. Knowledge based ant colony algorithm for distribution route optimization of cold chain logistics [J]. Control and Decision, 2022, 37(3): 545-554. [6]白秦洋,尹小庆,林云. 考虑路网中实时交通的冷链物流路径优化[J]. 工业工程与管理, 2021, 26(6): 56-65. BAI Qinyang, YIN Xiaoqing, LIN Yun. Cold chain logistics routing optimization considering real-time traffic in road network [J]. Industrial Engineering and Management, 2021, 26(6): 56-65. [7]方文婷,艾时钟,王晴,等. 基于混合蚁群算法的冷链物流配送路径优化研究[J]. 中国管理科学, 2019, 27(11): 107-115. FANG Wenting, AI Shizhong, WANG Qing, et al. Research on distribution route optimization of cold chain logistics based on hybrid ant colony algorithm [J]. Chinese Journal of Management Science, 2019, 27(11): 107-115. [8]BRANDO J. A memory-based iterated local search algorithm for the multi-depot open vehicle routing problem[J]. European Journal of Operational Research, 2020, 284(2):559-571. [9]WANG Y, LI Q, GUAN X, et al. Two-echelon collaborative multi-depot multi-period vehicle routing problem[DB/OL].[2023-01-28].https://doi.org/10.1016/j.eswa.2020.114201. [10] FAN H, ZHANG Y, TIAN P, et al. Time-dependent multi-depot green vehicle routing problem with time windows considering temporal-spatial distance[DB/OL].[2023-01-28].https://doi.org/10.1016/j.cor.2021.105211. [11] 廖列法,张幸平. 考虑客户满意度的多配送站低碳物流路径规划[J]. 信息与控制, 2020, 49(4): 420-428. LIAO Liefa, ZHANG Xingping. Low-carbon logistics route planning for multi-distribution stations considering customer satisfaction [J]. Information and Control, 2020, 49(4): 420-428. [12] 范厚明,张轩,任晓雪,等. 多中心开放且需求可拆分的VRPSDP问题优化[J]. 系统工程理论与实践, 2021, 41(6): 1521-1534. FAN Houming, ZHANG Xuan, REN Xiaoxue, et al. Optimization of multi-center open VRPSDP problem with separable requirements [J]. Systems Engineering Theory & Practice, 2021, 41(6): 1521-1534. [13] 王万良,陈浩立,李国庆,等. 基于深度强化学习的多配送中心车辆路径规划[J]. 控制与决策, 2022, 37(8): 2101-2109. WANG Wanliang, CHEN Haoli, LI Guoqing, et al. Vehicle routing planning in multi-distribution centers based on deep reinforcement learning [J]. Control and Decision, 2022, 37(8): 2101-2109. [14] 张颖钰,吴立云.多中心半开放式送取需求可拆分的车辆路径优化[J].计算机应用研究,2022,39(8):2316-2321. ZHANG Yingyu, WU Liyun. Optimization of multi-center semi-open vehicle routing with split delivery demand [J]. Application Research of Computers, 2012,39(8):2316-2321. [15] XIAO Y, ZHAO Q, KAKU I, et al. Development of a fuel consumption optimization model for the capacitated vehicle routing problem[J]. Computers and Operations Research, 2012, 39(7):1419-1431. [16] 张建勇,郭耀煌,李军. 基于顾客满意度的多目标模糊车辆优化调度问题研究[J]. 铁道学报, 2003(2): 15-17. ZHANG Jianyong, GUO Yaohuang, LI Jun.Research on multi-objective fuzzy vehicle optimization scheduling problem based on customer satisfaction [J]. Journal of the China Railway Society, 2003(2): 15-17. [17] 潘立军,符卓. 求解带时间窗取送货问题的遗传算法[J]. 系统工程理论与实践, 2012, 32(1): 120-126. PAN Lijun, FU Zhuo. Genetic algorithm for pickup and delivery problem with time window[J]. Systems Engineering Theory & P-ractice, 2012, 32(1): 120-126.