• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
JIAO Yuling, ZHANG Linjing, XING Xiaocui. LIRP Joint Collaborative Optimization under Stochastic Demand and Time Constraints[J]. Journal of Southwest Jiaotong University, 2020, 55(5): 963-970. doi: 10.3969/j.issn.0258-2724.20190463
Citation: JIAO Yuling, ZHANG Linjing, XING Xiaocui. LIRP Joint Collaborative Optimization under Stochastic Demand and Time Constraints[J]. Journal of Southwest Jiaotong University, 2020, 55(5): 963-970. doi: 10.3969/j.issn.0258-2724.20190463

LIRP Joint Collaborative Optimization under Stochastic Demand and Time Constraints

doi: 10.3969/j.issn.0258-2724.20190463
  • Received Date: 23 May 2019
  • Rev Recd Date: 21 Feb 2020
  • Available Online: 10 Mar 2020
  • Publish Date: 01 Oct 2020
  • Aiming at improving the overall efficiency of the multi-node, multi-level, and multi-functional supply chain management, a secondary distribution network composed of a single supplier, multiple distribution centers, and multiple retail stores for a chain supermarket wasexplored to establish the multi-objective location-inventory-routing problem (LIRP) integrated planning model with the objectives of the total system cost and supply time. The linear weighting method was used to transform the model into the single-objective programming one. A two-stage heuristic algorithm combining genetic algorithm and mileage saving method was proposed to solve the model. In the first phase, the location-inventory problem was solved by the genetic algorithm, and in the second phase, vehicle routing problem was solved by the mileage saving method. A chain supermarket example was used for the LIRP integration optimization of the distribution network with different decision schemes and total cost weights. Compared the results from a reference, the optimized system scheme reduced the total mileage by 3 606.9 km, the total system cost by 6 526.2 yuan, and the cost of back orders by 124.6 yuan, being 19.7 yuan, which verifies the model and algorithm.

     

  • LIU S C, LEE S B. A two-phase heuristic method for the multi-depot location routing problem taking inventory control decisions into consideration[J]. International Journal Advanced Manufacturing Technology, 2003, 22(11/12): 941-950.
    LIU S C, LIN C C. A heuristic method for the combined location routing and inventory problem[J]. International Journal Advanced Manufacturing Technology, 2005, 26(4): 372-381. doi: 10.1007/s00170-003-2005-3
    LIU Bailing, CHEN Hui, LI Yanhui, et al. A pseudo-parallel genetic algorithm integrating simulated annealing for stochastic LIRP with consideration of returns in e-commerce[J]. Discrete Dynamics in Nature and Society, 2015, 2015: 586581.1-586581.15.
    YUCHI Q, HE Zhengwen, YANG Zhen, et al. A location-inventory-routing problem in forward and reverse logistics network design[J]. Discrete Dynamics in Nature and Society, 2016, 2016: 3475369.1-3475369.18.
    ATIYE G, MOHAMMAD R A J. A hybrid imperialist competitive simulated annealing algorithm for a multi-source multi-product location-routing-inventory problem[J]. Computers & Industrial Engineering, 2016, 101: 116-127.
    FARNAZ R, MIR M M, ALI B A. Bi-objective reliable location-inventory-routing problem with partial backorder-ing under disruption risks:a modified ASOSA approach[J]. Applied Soft Comput-ing, 2017, 59: 622-643. doi: 10.1016/j.asoc.2017.06.036
    FARHAD H, EHSAN A, SEYED J S. A location-inventory-routing optimization model for cost effective microalgae biofuel distribution system:a case study in Iran[J]. Energy Strategy Reviews, 2018, 22: 82-93. doi: 10.1016/j.esr.2018.08.006
    NOVA I S, SENATOR N B, SUPRAYOGI, et al. A heuristic method for location-inventory-routing problem in a three-echelon supply chain system[J]. Computers & Industrial Engineering, 2019, 127: 875-886.
    ZHENG Xiaojin, YIN Meixia, ZHANG Yanxia. Integrated optimization of location,inventory and routing in supply chain network design[J]. Transpor- tation Research Part B, 2019, 121: 1-20. doi: 10.1016/j.trb.2019.01.003
    崔广彬,李一军. 模糊需求下物流系统 CLRIP 问题研究[J]. 控制与决策,2007,22(9): 1001-1016.

    CUI Guangbin, LI Yijun. Research on CLRIP of logistics system under fuzzy demand[J]. Control and Decision, 2007, 22(9): 1001-1016.
    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. doi: 10.3969/j.issn.1007-3221.2014.04.010

    DU Lijing, LI Yanhui. Integrated models and approach for location inventory and routing problem[J]. Operations Research and Management Science, 2014, 23(4): 70-79. doi: 10.3969/j.issn.1007-3221.2014.04.010
    吴迪,王诺,宋南奇,等. 边远群岛物流体系的选址-库存-路径优化[J]. 系统工程理论与实践,2016,36(12): 3175-3187. doi: 10.12011/1000-6788(2016)12-3175-13

    WU Di, WANG Nuo, SONG Nanqi, et al. Location-inventory-path optimization of logistics system in remote islands[J]. System Engineering Theory and Practice, 2016, 36(12): 3175-3187. doi: 10.12011/1000-6788(2016)12-3175-13
    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 Dezhi, PAN Lihong, LI Shuangyan. Joint optimization of location-inventory-path considering supplier selection[J]. Computer Application Research, 2019, 36(8): 2338-2341.
    汪伟. J公司选址-库存-路径集成规划问题研究[D]. 长沙: 东南大学, 2018.
  • Relative Articles

    [1]TANG Xifeng, HE Jie, ZHANG Hao. Two-Echelon Location Routing Problem Considering Carbon Emissions and Its Algorithm[J]. Journal of Southwest Jiaotong University, 2023, 58(5): 1110-1116, 1125. doi: 10.3969/j.issn.0258-2724.20210773
    [2]ZHU Xinping, TANG Xinmin, HAN Songchen. Aircraft Initial Taxiing Route Planning Based on Petri Net and Genetic Algorithm[J]. Journal of Southwest Jiaotong University, 2013, 26(3): 565-573. doi: 10.3969/j.issn.0258-2724.2013.03.027
    [3]WU Guangning, FU Longhai, WANG Hao, LI Jin. Optimal Design of Grounding Grid Based on Improved Genetic Algorithm[J]. Journal of Southwest Jiaotong University, 2007, 20(2): 169-174.
    [4]HE Fengdao, LIANG Xiangyang, HE Dongyun. Self-Adaptive Genetic Algorithm for Locomotive Diagram[J]. Journal of Southwest Jiaotong University, 2006, 19(3): 273-278.
    [5]DAI Chaohua, ZHU Yunfang, CHEN Weirong. Cloud Theory-Based Genetic Algorithm[J]. Journal of Southwest Jiaotong University, 2006, 19(6): 729-732.
    [6]FANG Lei, ZHANG Huan-chun, JING Ya-zhi. New Fuzzy Self-Tuning Genetic Algorithm[J]. Journal of Southwest Jiaotong University, 2005, 18(1): 22-25.
    [7]SHIYu-feng, SU Shi, PENG Qi-yuan. Optim ization ofM ilitary Transportation Routes Based on Genetic Algorithm[J]. Journal of Southwest Jiaotong University, 2005, 18(2): 241-243.
    [8]GAO Wei-zeng, ZHANG Bao-jian, CHEN Fu-gui, ZHU Jia-yi, . Optim ization ofCutting Path Based on Genetic Algorithm[J]. Journal of Southwest Jiaotong University, 2005, 18(4): 457-461.
    [9]DAI Ying. Partner Selection in Supply Chain Alliance Based on Genetic Algorithm[J]. Journal of Southwest Jiaotong University, 2004, 17(4): 531-534.
    [10]WANG Qing-rong, LIXian-lin, WENJu. Optimal Algorithm for Multi-period Inventory System with Quantity Discoun[J]. Journal of Southwest Jiaotong University, 2004, 17(4): 535-539.
    [11]FENG Chun, CHEN Yong. Genetic Algorithms for Period-Double Bifurcation of Logistic Mapping[J]. Journal of Southwest Jiaotong University, 2003, 16(3): 290-293.
    [12]ZHANG Ge-xiang, JIN Wei-dong. Improvement of Quantum Genetic Algorithm and Its Application[J]. Journal of Southwest Jiaotong University, 2003, 16(6): 717-722.
    [13]QIUXiao-ping, TANG Yong-chuan, MENG Dan, XU Yang. Multivalue Coded Genetic Algorithm[J]. Journal of Southwest Jiaotong University, 2003, 16(2): 227-130.
    [14]ZHANG Zhi-yuan, HE Chuan. A Genetic Algorithm Based on Uniform Design Paralleled with Genetic Operation[J]. Journal of Southwest Jiaotong University, 2002, 15(5): 536-340.
    [15]FENGHao, HEHong-yun, MI Zu-qiang. Nonlinear System Identification with Recurrent Neural Network Based on Genetic Algorithm[J]. Journal of Southwest Jiaotong University, 2002, 15(4): 404-407.
    [16]XIEBing-lei, LIJun, LIUJian-xin. A Heuristic Genetic Algorithm for the Travelling Salesman Problem with Time Restraints[J]. Journal of Southwest Jiaotong University, 2001, 14(2): 211-213.
  • Cited by

    Periodical cited type(1)

    1. 户佐安,郑磊,周姝. 考虑列位合理占用的动车所调车作业计划编制优化. 西南交通大学学报. 2022(01): 65-73 . 本站查看

    Other cited types(9)

  • Created with Highcharts 5.0.7Amount of accessChart context menuAbstract Views, HTML Views, PDF Downloads StatisticsAbstract ViewsHTML ViewsPDF Downloads2024-092024-102024-112024-122025-012025-022025-032025-042025-052025-062025-072025-070510152025
    Created with Highcharts 5.0.7Chart context menuAccess Class DistributionFULLTEXT: 41.0 %FULLTEXT: 41.0 %META: 56.4 %META: 56.4 %PDF: 2.6 %PDF: 2.6 %FULLTEXTMETAPDF
    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 2.5 %其他: 2.5 %上海: 1.0 %上海: 1.0 %临汾: 0.3 %临汾: 0.3 %北京: 4.3 %北京: 4.3 %十堰: 0.7 %十堰: 0.7 %南京: 3.1 %南京: 3.1 %南通: 0.2 %南通: 0.2 %台州: 0.5 %台州: 0.5 %哥伦布: 0.2 %哥伦布: 0.2 %大连: 1.0 %大连: 1.0 %天津: 0.7 %天津: 0.7 %宣城: 0.3 %宣城: 0.3 %山景城: 0.2 %山景城: 0.2 %广州: 0.3 %广州: 0.3 %张家口: 3.5 %张家口: 3.5 %德州: 0.2 %德州: 0.2 %成都: 1.2 %成都: 1.2 %扬州: 0.5 %扬州: 0.5 %杭州: 0.8 %杭州: 0.8 %格兰特县: 0.2 %格兰特县: 0.2 %武汉: 0.7 %武汉: 0.7 %池州: 2.3 %池州: 2.3 %淮南: 0.2 %淮南: 0.2 %湖州: 0.2 %湖州: 0.2 %漯河: 1.7 %漯河: 1.7 %石家庄: 0.8 %石家庄: 0.8 %福州: 0.2 %福州: 0.2 %纽约: 0.3 %纽约: 0.3 %芒廷维尤: 26.9 %芒廷维尤: 26.9 %芜湖: 0.2 %芜湖: 0.2 %芝加哥: 0.3 %芝加哥: 0.3 %衢州: 0.2 %衢州: 0.2 %西宁: 42.1 %西宁: 42.1 %贵阳: 0.2 %贵阳: 0.2 %运城: 0.8 %运城: 0.8 %邯郸: 0.3 %邯郸: 0.3 %长春: 0.2 %长春: 0.2 %长沙: 0.8 %长沙: 0.8 %青岛: 0.2 %青岛: 0.2 %其他上海临汾北京十堰南京南通台州哥伦布大连天津宣城山景城广州张家口德州成都扬州杭州格兰特县武汉池州淮南湖州漯河石家庄福州纽约芒廷维尤芜湖芝加哥衢州西宁贵阳运城邯郸长春长沙青岛

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(4)  / Tables(6)

    Article views(766) PDF downloads(19) Cited by(10)
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return