基于两阶段鲁棒优化的可靠性物流网络设计

doi:10.16097/j.cnki.1009-6744.2023.02.030

交通运输系统工程与信息 ›› 2023, Vol. 23 ›› Issue (2): 285-299.DOI: 10.16097/j.cnki.1009-6744.2023.02.030

基于两阶段鲁棒优化的可靠性物流网络设计

石褚巍^1a,1b，马昌喜^*1a,1b，麻存瑞²

1. 兰州交通大学，a. 交通运输学院，b. 高原铁路运输智慧管控铁路行业重点实验室，兰州 730070； 2. 重庆邮电大学，现代邮政学院，重庆 400000

收稿日期:2023-02-12 修回日期:2023-03-23 接受日期:2023-03-28 出版日期:2023-04-25 发布日期:2023-04-19
作者简介:石褚巍(1992- )，男，甘肃合作人，博士生
基金资助:
国家自然科学基金(71861023，52062027)；甘肃省基础研究计划-软科学专项(22JR4ZA035)

Reliability Logistics Network Design Based on Two Stage Robust Optimization

SHI Chu-wei^1a,1b, MA Chang-xi^*1a,1b, MA Cun-rui²

1a. School of Traffic and Transportation, 1b. Key Laboratory of Railway lndustry on Plateau Railway Transportation Intelligent Management and Control, Lanzhou Jiaotong University, Lanzhou 730070, China; 2. Modern Post School, Chongqing University of Posts and Telecommunications, Chongqing 400000, China

Received:2023-02-12 Revised:2023-03-23 Accepted:2023-03-28 Online:2023-04-25 Published:2023-04-19
Supported by:
National Natural Science Foundation of China (71861023，52062027)；Soft Science Special Project of Gansu Basic Research PIan (22JR4ZA035)

摘要/Abstract

摘要： 针对网络中存在节点及线路损坏不确定性的可靠性物流网络设计问题，提出一种基于两阶段鲁棒优化的可靠性物流网络设计方法。以供应和中转节点选址，节点连通关系确定，流量分配作为决策变量，构建两阶段可靠性物流网络设计模型，追求网络总成本和总运行时间两个独立目标的最小化。其中，网络总成本目标函数包含两个阶段的成本，第1阶段，计算网络建设成本及网络正常状态下的运行成本；第2阶段，计算网络损坏情景不确定集下的网络运行成本。网络总运行时间目标函数用于计算网络正常状态下的运行时间。设计具有双层编码结构染色体的混合进化算法，以NPGA(Niched Pareto Genetic Algorithm)作为主算法框架，设计大邻域搜索机制优化个体连通关系基因层，同时，嵌套基于聚类的交叉和变异策略提升算法对解空间的搜索能力。以多组不同规模的可靠性物流网络设计问题进行案例分析，验证模型及算法的合理性和有效性。研究结果表明：两阶段可靠性物流网络设计模型能够通过少量增加前期网络建设成本的投入，显著降低网络在受损情况下的运行成本，有效提升网络可靠性。在5个供应节点、10个中转节点及15个需求节点的案例对比中，该模型求得的成本偏好及时间偏好的两组Pareto解，相比于传统多目标物流网络模型的两组对应偏好解，在同一网络损坏情景集中最多能够分别节省 20.6%和28.2%的网络运行成本；设计的混合进化算法在迭代初期就收敛到较优的目标值，表现出较强地搜索和寻优性能，能够实现对两阶段可靠性物流网络设计模型的有效求解。

关键词: 物流工程, 网络设计, 鲁棒优化, 可靠性, NPGA, 大邻域搜索, 聚类

Abstract: This paper investigates the reliability logistics network design problem with uncertainty due to node and road damages. A reliability logistics network design method based on two-stage robust optimization is proposed. Taking the location of supply and transit nodes, determination of node connectivity, and distribution of cargo flow as decision variables, a two-stage reliability logistics network design model was constructed. The model has two independent objectives, i.e., the total network cost objective function and the total network operation time objective function. The former includes two stages of cost, in which the first stage calculates the network construction cost and the network operation cost under the normal state and the second stage calculates the network operation cost under a disruptive scenario set. The total network operation time objective function is used to calculate the network operation time under the normal state. A hybrid evolutionary algorithm with double-layer encoding structure chromosomes is designed. The NPGA is used as the main algorithm framework, and a large neighborhood search mechanism is designed to optimize the connectivity relationship genes of individuals. Cluster-based crossover and mutation strategies are combined to improve the search ability of the algorithm in the solution space. The effectiveness of the model and algorithm are verified by case studies with several groups of reliability logistics network design problems of different scales. The results show that the two-stage reliability logistics network design model can significantly reduce the operation cost of the network in case of damage and effectively improve network reliability through a small increase in the initial network construction cost. In the case comparison of 5 supply nodes, 10 transit nodes, and 15 demand nodes, the two sets of Pareto solutions for cost preference and time preference obtained by the model can save up to 20.6% and 28.2% of network operation cost in the same network damage scenario set, respectively, compared with the two sets of corresponding preference solutions of the traditional multi-objective logistics network model. The hybrid evolutionary algorithm converges to a better target value at the initial stage of iteration, showing a better search and optimization performance, which can effectively solve the two-stage reliability logistics network design model.

Key words: logistics engineering, network design, robust optimization, reliability, NPGA, large neighborhood search, clustering

中图分类号:

F502

石褚巍, 马昌喜, 麻存瑞. 基于两阶段鲁棒优化的可靠性物流网络设计[J]. 交通运输系统工程与信息, 2023, 23(2): 285-299.

SHI Chu-wei, MA Chang-xi, MA Cun-rui. Reliability Logistics Network Design Based on Two Stage Robust Optimization[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(2): 285-299.

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参考文献

[1] 石琴, 陈朝阳, 覃运梅. 多目标物流网络优化模型的研究[J]. 中国管理科学, 2005(4): 40-43. [SHI Q, CHEN C Y, QIN Y M. A multi-objective optimal model on logistics network programming[J]. Chinese Journal of Management Science, 2005(4): 40-43.]

[2] 张桓奇, 张毅. 基于图论和模糊规划的物流网络优化[J]. 交通运输系统工程与信息, 2005, 5(6): 91-95. [ZHANG H Q, ZHANG Y. Optimization of logistics network based on graph theory and fuzzy planning[J]. Journal of Transportation Systems Engineering and Information Technology, 2005, 5(6): 91-95.]

[3] ALUMUR S A, NICKEL S, SALDANHA-DA-GAMA F. Hub location under uncertainty[J]. Transportation Research Part B: Methodological, 2012, 46(4): 529-543.

[4] MISKOVIC S, STANIMIROVIC Z, GRUJICIC I. Solving the robust two-stage capacitated facility location problem with uncertain transportation costs[J]. Optimization Letters, 2017, 11: 1169-1184.

[5] 彭永涛, 张锦, 王坤. 具有随机供需特征的物流超网络优化模型研究[J]. 交通运输系统工程与信息, 2014, 14(2): 184-191. [PENG Y T, ZHANG J, WANG K. Logistics supernetwork optimization model with stochastic supply and demand characteristics[J]. Journal of Transportation Systems Engineering and Information Technology, 2014, 14(2): 184-191.]

[6] PENG P, SNYDER L V, LIM A, et al. Reliable logistics networks design with facility disruptions[J]. Transportation Research Part B: Methodological, 2011, 45(8): 1190-1211.

[7] SNYDER L V, DASKIN M S. Stochastic p-robust location problems[J]. Iie Transactions, 2006, 38(11): 971-985.

[8] CHENG C, QI M, ZHANG Y, et al. A two-stage robust approach for the reliable logistics network design problem [J]. Transportation Research Part B: Methodological, 2018, 111: 185-202.

[9] HATEFI S M, JOLAI F. Robust and reliable forwardreverse logistics network design under demand uncertainty and facility disruptions[J]. Applied Mathematical Modeling, 2014, 38(9/10): 2630-2647.

[10] 林锦辉, 胡万杰, 董建军, 等. 基于动态可靠性评价的城市地下物流网络设计[J]. 地下空间与工程学报, 2022, 18(4): 1062-1074. [LIN J H, HU W J, DONG J J, et al. Design on urban underground logistics network based on dynamic reliability evaluation[J]. Chinese Journal of Underground Space and Engineering, 2022, 18 (4): 1062-1074.]

[11] MOHAMMADI M, TAVAKKOLI-MOGHADDAM R, SIADAT A, et al. Design of a reliable logistics network with hub disruption under uncertainty[J]. Applied Mathematical Modelling, 2016, 40(9/10): 5621-5642.

[12] 李锐, 黄敏. 多等级蓄意攻击下的第三方物流可靠性网络设计[J]. 控制与决策, 2017, 32(8): 1368-1376. [LI R, HUANG M. Reliable network design of third-party logistics under multi-grade proactive attacks[J]. Control and Decision, 2017, 32(8): 1368-1376.]

[13] 谢婷, 周骞, 史鸽飞. 基于时间可靠性分析的农产品轴辐式物流网络节点选址[J]. 长沙理工大学学报(自然科学版), 2015, 12(2): 15-20. [XIE T, ZHOU Q, SHI G F. Nodes location of the shaft radial logistics network of agricultural products based on the analysis of time reliability[J]. Journal of Changsha University of Science & Technology (Natural Science), 2015, 12(2): 15-20.]

[14] 尹小庆, 莫宇迪, 董陈晨, 等. 考虑行程时间可靠性的城市冷链末端配送站选址研究[J]. 交通运输系统工程与信息, 2019, 19(6): 176-183, 198. [YIN X Q, MO Y D, DONG C C, et al. Location of terminal distribution station of urban cold chain logistics considering travel time reliability[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(6): 176-183, 198.]

[15] 陈坚, 晏启鹏, 霍娅敏, 等. 基于可靠性分析的区域灾害应急物流网络设计[J]. 西南交通大学学报, 2011, 46 (6): 1025-1031. [CHEN J, YAN Q P, HUO Y M, et al. Regional emergency logistics network design based on reliability analysis[J]. Journal of Southwest Jiaotong University, 2011, 46(6): 1025-1031.]

[16] KE G Y. Managing reliable emergency logistics for hazardous materials: A two-stage robust optimization approach[J]. Computers & Operations Research, 2022, 138: 105557.

[17] BERTSIMAS D, SIM M. Robust discrete optimization and network flows[J]. Mathematical Programming, 2003, 98(1/3): 49-71.

[18] 钱颂迪. 运筹学(第 4 版)[M]. 北京: 清华大学出版社, 2012. [QIANG S D. Operations research (The 4th Edition)[M]. Beijing: Tsinghua University Press, 2012.]

[19] SIMON D. Evolutionary optimization algorithms[M]. New Jersey: John Wiley & Sons, 2013.

基于两阶段鲁棒优化的可靠性物流网络设计

Reliability Logistics Network Design Based on Two Stage Robust Optimization

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