Selection of logistics network nodes based on cloud warehousing under uncertain demand
Article Text (Baidu Translation)
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摘要: 为了降低需求不确定对城市配送网络效率和稳定性的影响，研究了基于云仓储的物流网络节点选择问题；考虑云仓、车辆的容量及缺货保证率等现实因素，构建以云仓租赁与配送总成本最小为目标的混合整数非线性随机优化模型；设计了融合模拟植物生长算法、邻域搜索算法、动态规划与遗传算法的三层混合算法求解模型；在外层框架中，基于自适应模拟植物生长算法，对云仓的数量和选址进行优化；在中层框架中，基于嵌入量-距-成本积聚类算子的改进邻域搜索算法，对云仓与需求点的服务关系进行优化；在内层框架中，基于动态规划节约算法，对配送车辆路径进行优化，并基于遗传算法，对干线车辆路径、各云仓租用面积和补货周期进行优化。研究结果表明：设计的算法相比于现有算法所获得方案的总成本最高由367.34万元降低至350.82万元，相比已有的算法成本降低幅度为2.48%~4.50%；在云仓和需求点数量相同的情况下，产销地距离与干线运输成本呈负相关，与租仓面积以及补货周期呈正相关；云仓单位租金与干线运输成本呈正相关，与租仓面积以及补货周期呈负相关；云仓与需求点分布越聚集，云仓储系统的配送成本越小；在同一分布下，需求点数量与被选择的云仓数量以及租仓面积呈正相关。研究结论可为云仓物流网络设计提供决策参考。Abstract: In order to reduce the effects of uncertain logistics demands on the efficiency and stability of urban delivery network, the problem of selecting logistics network nodes based on cloud warehousing was investigated. Realistic factors such as the capacities of cloud warehouses and vehicles, as well as the stock-out guarantee rate, were taken into account, and a mixed-integer nonlinear stochastic optimization model aiming to minimize the total cost of cloud warehouse leasing and distribution was constructed. A three-layer hybrid algorithm model was designed by integrating plant growth simulated algorithm, neighborhood search algorithm, dynamic programming, and genetic algorithm. In the outer framework, the number and location selection of cloud warehouses were optimized based on an adaptive plant growth simulated algorithm. In the middle framework, the service relationships between cloud warehouses and demand points were optimized based on the improved neighborhood search algorithm incorporating the volume-distance-cost accumulation clustering operator. In the inner framework, the delivery vehicle routes were optimized based on the dynamic programming savings algorithm, and the trunk vehicle routes, the rental areas of each cloud warehouse, and the replenishment cycles were optimized based on the genetic algorithm. Research results show that the total cost of the solution obtained by the algorithm designed reduce from a maximum of 3.673 4 million yuan to 3.508 2 million yuan compared with existing algorithms, and the reduction range is between 2.48% and 4.50% compared with different algorithms. When the number of cloud warehouses and demand points is the same, the distance between production and sales areas is negatively correlated with the trunk transportation cost, and positively correlated with the rented area of cloud warehouses and the replenishment cycle. The unit rent of cloud warehouses is positively correlated with the trunk transportation cost, and negatively correlated with the rented area of cloud warehouses and the replenishment cycle. The more concentrated the distribution of cloud warehouses and demand points, the lower the distribution cost of the cloud warehousing system. Under the same distribution, the number of demand points is positively correlated with the number of selected cloud warehouses and the rented area of cloud warehouses. The research conclusions can provide decision-making references for the design of cloud warehouse logistics networks.
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图 1 不确定需求下三级云仓供应链网络
Figure 1. Three-level cloud warehouse supply chain network under uncertain demands
图 13 模拟山岭地区需求点分布
Figure 13. Demand point distributions of simutation mountainous areas
表 1 云仓的位置、仓储容量、单位仓储费
Table 1. Location, storage capacity, and unit storage costs of cloud warehouse
云仓编号 横坐标/km 纵坐标/km 仓储容量/m2 每天每平米存储费/元 ① 260 60 2 200 23 ② 294 59 1 900 16 ③ 199 79 1 050 18 ④ 250 16 2 870 15 ⑤ 271 95 1 700 21 ⑥ 232 77 1 150 24 ⑦ 220 40 2 040 20 ⑧ 240 120 940 15 ⑨ 300 30 2 300 16 ⑩ 312 106 1 900 17 
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表 2 需求点的位置和需求分布
Table 2. Locations of demand points and distribution of demand
需求点编号 横坐标/km 纵坐标/km μ/t σ2/t2 1 271 50 0.96 0.14 2 238 88 1.08 0.24 3 232 55 0.91 0.16 4 243 11 0.87 0.09 5 265 120 1.31 0.27 6 290 77 0.93 0.15 7 199 24 1.20 0.25 8 250 55 0.95 0.18 9 254 33 1.38 0.28 10 318 99 1.22 0.23 11 311 66 0.90 0.11 12 220 80 1.07 0.23 13 232 110 1.05 0.21 14 278 106 0.91 0.17 15 212 44 0.89 0.09 16 318 33 0.83 0.08 17 199 117 1.27 0.30 18 285 20 0.77 0.15 19 200 73 0.82 0.13 20 312 130 1.08 0.21 21 225 131 1.21 0.23 22 226 86 1.06 0.21 23 228 29 0.84 0.11 24 206 103 1.29 0.26 25 298 105 1.21 0.22 26 265 7 0.71 0.05 27 293 42 0.83 0.11 28 190 45 0.82 0.18 29 316 12 0.72 0.07 30 212 6 0.73 0.08
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表 3 案例运行100次计算结果和最优结果
Table 3. Calculation results and optimal results of 100 runs of case
成本 平均值 方差 最优值 干线运输成本/万元 121.94 0.00 121.94 配送成本/万元 211.39 0.53 207.83 云仓租赁成本/万元 24.11 1.02 21.04 总成本/万元 357.44 0.35 350.82
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表 4 运输路径及补货周期
Table 4. Transportation routes and replenishment cycles
编号 运输路径 补货周期/d 干线路径 ⅰ O→⑦→③→O 2 ⅱ O→⑤→O 4 ⅲ O→⑨→O 6 配送路径 Ⅰ ③→17→21→13→③ 1 Ⅱ ③→19→12→22→24→③ Ⅲ ⑤→2→5→14→⑤ Ⅳ ⑤→6→10→20→25→⑤ Ⅴ ⑦→15→28→7→30→⑦ Ⅵ ⑦→3→8→9→4→23→⑦ Ⅶ ⑨→18→26→1→11→⑨ Ⅷ ⑨→27→16→29→⑨
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表 5 算法计算结果对比
Table 5. Comparison of calculation results of algorithms
参数 本文算法 PGSA GA 本文算法相较于PGSA的优化幅度/% 本文算法相较于GA的优化幅度/% 干线运输成本/万元 121.94 124.92 125.36 2.39 2.73 配送成本/万元 207.83 211.08 218.43 1.54 4.85 云仓租赁成本/万元 21.04 23.74 23.55 11.37 10.66 总成本/万元 350.82 359.74 367.34 2.48 4.50 计算时间/s 287 176 135 -63.07 -112.59
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表 6 产销地距离对总成本的影响
Table 6. Impact of production-sales location distance on total cost
成本 η=0.5 η=0.75 η=1 η=1.25 η=1.5 干线运输成本/万元 75.98 97.41 121.94 158.98 178.32 配送成本/万元 207.63 207.83 207.83 207.83 217.53 云仓租赁成本/万元 25.68 21.04 21.04 20.16 13.99 总成本/万元 307.91 326.28 350.82 375.35 409.83
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表 7 云仓单位租金对总成本的影响
Table 7. Impact of cloud warehouse unit rental on total cost
成本 ϕ=0.5 ϕ=0.75 ϕ=1 ϕ=1.25 ϕ=1.5 干线运输成本/万元 125.81 126.66 121.94 133.35 145.55 配送成本/万元 208.35 207.77 207.83 207.83 209.91 云仓租赁成本/万元 26.11 22.06 21.04 20.16 14.02 总成本/万元 360.28 351.71 350.82 361.34 369.24
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表 8 云仓与需求点分布对总成本的影响
Table 8. Impact of cloud warehouses and demand point distribution on total costs
成本 m1=20 m1=30 m1=40 m2=30 m2=40 干线运输成本/万元 105.67 121.94 134.02 121.94 134.02 配送成本/万元 208.11 207.83 205.46 199.35 191.85 云仓租赁成本/万元 20.09 21.04 25.11 21.04 25.11 总成本/万元 333.87 350.82 364.59 342.33 350.98
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[1] NGUYEN M D, YEON K T, RUDZKI K, et al. Strategies for developing logistics centres: technological trends and policy implications[J]. Polish Maritime Research, 2023, 30(4): 129-147. doi: 10.2478/pomr-2023-0066 [2] 慕艳平, 周文凤. 我国云仓储物流模式发展探析[J]. 电子商务, 2019(9): 1-2.MU Yan-ping, ZHOU Wen-feng. Analysis on the development of cloud storage logistics mode in China[J]. E-Business Journal, 2019(9): 1-2. [3] 李媛, 伍星华, 李思寰, 等. 基于云物流的湘西地区农产品物流降损保鲜提质研究[J]. 农村经济与科技, 2022, 33(17): 245-247, 260.LI Yuan, WU Xing-hua, LI Si-huan, et al. Research on loss reduction, preservation and quality improvement of agricultural product logistics in Xiangxi region based on cloud logistics[J]. Rural Economy and Science-Technology, 2022, 33(17): 245-247, 260. [4] 王志国. 云物流下生鲜农产品物流模式优化及资源整合研究[J]. 物流科技, 2020, 43(12): 144-146.WANG Zhi-guo. Research on fresh agricultural products logistics mode optimization and resource integration under cloud logistics[J]. Logistics Sci-Tech, 2020, 43(12): 144-146. [5] 杨侃. 基于"云仓储"的国民经济动员中心物资储备模式研究[J]. 现代商贸工业, 2021, 42(13): 25, 38.YANG Kan. Research on material reserve mode of national economic mobilization center based on "cloud warehousing"[J]. Modern Business Trade Industry, 2021, 42(13): 25, 38. [6] 杨从平, 黄素心, 杨丽英. 基于云仓储的两阶段快递配送分析[J]. 物流工程与管理, 2018, 40(1): 95-97, 49.YANG Cong-ping, HUANG Su-xin, YANG Li-ying. Two-stage express delivery based on cloud warehouse[J]. Logistics Engineering and Management, 2018, 40(1): 95-97, 49. [7] 李振华. 基于量子遗传算法的云仓储选址分配问题研究[J]. 物流工程与管理, 2019, 41(1): 59-62.LI Zhen-hua. Research on cloud storage location allocation problem based on quantum genetic algorithm[J]. Logistics Engineering and Management, 2019, 41(1): 59-62. [8] 王飞, 孟凡超, 郑宏珍. 基于禁忌搜索和遗传算法的云仓储分配优化[J]. 计算机集成制造系统, 2022, 28(1): 208-216.WANG Fei, MENG Fan-chao, ZHENG Hong-zhen. Distribution and optimization of cloud warehousing based on tabu search algorithm[J]. Computer Integrated Manufacturing Systems, 2022, 28(1): 208-216. [9] 王孟君. 考虑预期需求覆盖率的两级云仓选址研究[D]. 大连: 大连海事大学, 2023.WANG Meng-jun. A two-echelon cloud warehouse location in consideration of expected demand coverage[D]. Dalian: Dalian Maritime University, 2023. [10] 盛世杰. 基于云仓的物流网络选址-库存-路径优化研究[D]. 大连: 大连海事大学, 2023.SHENG Shi-jie. Cloud warehouse-based logistics network site-inventory-route optimization research[D]. Dalian: Dalian Maritime University, 2023. [11] KUMARI M, DE P K, CHAKRABORTY A K. Formulation of a multi-period multi-echelon location-inventory-routing problem comparing different nature-inspired algorithms[J]. Sādhanā, 2023, 48(4): 280. doi: 10.1007/s12046-023-02288-9 [12] LYU A, SUN B F. Multi-objective robust optimization for the sustainable location-inventory-routing problem of auto parts supply logistics[J]. Mathematics, 2022, 10(16): 2942. doi: 10.3390/math10162942 [13] LIU L H, HE A N, TIAN T, et al. Bi-objective mixed integer nonlinear programming model for low carbon location-inventory-routing problem with time windows and customer satisfaction[J]. Mathematics, 2024, 12(15): 2367. doi: 10.3390/math12152367 [14] 吴迪, 韩欣丽, 石帅杰, 等. 需求不确定下边远群岛海运物流网络选址-库存-路径优化[J]. 交通运输系统工程与信息, 2024, 24(5): 268-282.WU Di, HAN Xin-li, SHI Shuai-jie, et al. Location-inventory-routing optimization of maritime logistics network in remote islands under demand uncertainty[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(5): 268-282. [15] 王梦梦, 韩晓龙. 考虑碳排放的易腐品供应链选址-路径-库存联合优化[J]. 上海海事大学学报, 2019, 40(4): 45-51.WANG Meng-meng, HAN Xiao-long. Location-routing-inventory joint optimization of perishable product supply chain considering carbon emission[J]. Journal of Shanghai Maritime University, 2019, 40(4): 45-51. [16] TAVANA M, TOHIDI H, ALIMOHAMMADI M, et al. A location-inventory-routing model for green supply chains with low-carbon emissions under uncertainty[J]. Environmental Science and Pollution Research, 2021, 28(36): 50636-50648. [17] LIU A J, ZHU Q Y, XU L, et al. Sustainable supply chain management for perishable products in emerging markets: an integrated location-inventory-routing model[J]. Transportation Research Part E: Logistics and Transportation Review, 2021, 150: 102319. [18] 熊浩, 鄢慧丽. 考虑多种安全库存策略的选址-库存问题研究[J]. 中国管理科学, 2021, 29(1): 72-81.XIONG Hao, YAN Hui-li. Six location-inventory models with risk pooling in two-echelon logistics system[J]. Chinese Journal of Management Science, 2021, 29(1): 72-81. [19] 张得志, 潘立红, 李双艳. 考虑供应商选择的选址-库存-路径的联合优化[J]. 计算机应用研究, 2019, 36(8): 2338-2341.ZHANG De-zhi, PAN Li-hong, LI Shuang-yan. Research on joint optimization for location-inventory-routing with supplier selection[J]. Application Research of Computers, 2019, 36(8): 2338-2341. [20] 李慧芳, 胡大伟, 陈希琼, 等. 考虑碳排放的混合轴辐式多式联运网络枢纽扩增选址-路径问题[J]. 交通运输工程学报, 2022, 22(4): 306-321. doi: 10.19818/j.cnki.1671-1637.2022.04.024LI Hui-fang, HU Da-wei, CHEN Xi-qiong, et al. Expanding hub location-routing problem for hybrid hub-and-spoke multimodal transport network considering carbon emissions[J]. Journal of Traffic and Transportation Engineering, 2022, 22(4): 306-321. doi: 10.19818/j.cnki.1671-1637.2022.04.024 [21] 唐金环, 戢守峰, 张杨, 等. 基于顾客低碳行为偏好的选址-路径-库存集成优化模型与算法[J]. 运筹与管理, 2017, 26(1): 35-44.TANG Jin-huan, JI Shou-feng, ZHANG Yang, et al. Research on collaboration of location-routing-inventory optimization model based on the consumers' low carbon behaviors preference[J]. Operations Research and Management Science, 2017, 26(1): 35-44. [22] 尉迟群丽, 何正文, 王能民. 考虑缺货的闭环供应链选址-库存-路径集成优化[J]. 运筹与管理, 2021, 30(2): 53-60.YUCHI Qun-li, HE Zheng-wen, WANG Neng-min. Integration optimization research on location-inventory-routing problem considering out-of-stock strategy in closed-loop supply chain[J]. Operations Research and Management Science, 2021, 30(2): 53-60. [23] NEKOOGHADIRLI N, TAVAKKOLI-MOGHADDAM R, GHEZAVATI V R, et al. Solving a new bi-objective location-routing-inventory problem in a distribution network by meta-heuristics[J]. Computers and Industrial Engineering, 2014, 76: 204-221. [24] RAYAT F, MUSAVI M, BOZORGI-AMIRI A. Bi-objective reliable location-inventory-routing problem with partial backordering under disruption risks: a modified AMOSA approach[J]. Applied Soft Computing, 2017, 59: 622-643. [25] GUERRERO W J, PRODHON C, VELASCO N, et al. Hybrid heuristic for the inventory location-routing problem with deterministic demand[J]. International Journal of Production Economics, 2013, 146(1): 359-370. [26] SARAGIH N I, BAHAGIA S N, SUPRAYOGI, et al. A heuristic method for location-inventory-routing problem in a three-echelon supply chain system[J]. Computers and Industrial Engineering, 2019, 127: 875-886. [27] 杜丽敬, 李延晖. 选址-库存-路径问题模型及其集成优化算法[J]. 运筹与管理, 2014, 23(4): 70-79.DU Li-jing, LI Yan-hui. Integrated models and approach for location inventory and routing problem[J]. Operations Research and Management Science, 2014, 23(4): 70-79. [28] GHORBANI A, AKBARI JOKAR M R. A hybrid imperialist competitive-simulated annealing algorithm for a multisource multi-product location-routing-inventory problem[J]. Computers and Industrial Engineering, 2016, 101: 116-127. -
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