IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v566y2021ics0378437120309353.html
   My bibliography  Save this article

How to achieve a win–win scenario between cost and customer satisfaction for cold chain logistics?

Author

Listed:
  • Wang, Minxi
  • Wang, Yajie
  • Liu, Wei
  • Ma, Yu
  • Xiang, Longtao
  • Yang, Yunqi
  • Li, Xin

Abstract

This paper aims to study how to achieve a win–win scenario between distribution cost and customer satisfaction; and propose a reasonable scheme for the terminal distribution of cold chain logistics. The paper focuses on the customers’ time requirements and establishes the penalty costs incurred when service time requirements are not met. In addition, this paper combines different aspects, such as considering the refrigeration energy consumption and the damage costs. Meanwhile, because of the different potential impacts of the various costs, different weights are assigned to each cost. Then, a cold chain logistics vehicle routing optimization model considering time windows is constructed. Finally, the Clarke–Wright (CW) algorithm, which is suitable for the model, is selected to solve the problem, and the validity of the model is verified by an example. The results of the calculation example show that although the transportation cost and energy cost of the optimized distribution route increase slightly, the optimized distribution route greatly reduces the total cost. Customer satisfaction and vehicle load rate have also increased significantly. Cost reduction and satisfaction improvement can sometimes be achieved at the same time. Using mathematical methods to plan routes can help companies save costs. And help planners and delivery staff to improve the timeliness of the last-mile delivery. Simplify complex algorithms without having to use professional software. More ordinary employees can also use this method.

Suggested Citation

  • Wang, Minxi & Wang, Yajie & Liu, Wei & Ma, Yu & Xiang, Longtao & Yang, Yunqi & Li, Xin, 2021. "How to achieve a win–win scenario between cost and customer satisfaction for cold chain logistics?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    • Handle: RePEc:eee:phsmap:v:566:y:2021:i:c:s0378437120309353
    DOI: 10.1016/j.physa.2020.125637
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437120309353
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2020.125637?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lenstra, J. K. & Rinnooy Kan, A. H. G., 1979. "Complexity Of Vehicle Routing And Scheduling Problems," Econometric Institute Archives 272191, Erasmus University Rotterdam.
    2. G. B. Dantzig & J. H. Ramser, 1959. "The Truck Dispatching Problem," Management Science, INFORMS, vol. 6(1), pages 80-91, October.
    3. Pelletier, Samuel & Jabali, Ola & Laporte, Gilbert, 2019. "The electric vehicle routing problem with energy consumption uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 225-255.
    4. Jingling Zhang & Wanliang Wang & Yanwei Zhao & Carlo Cattani, 2012. "Multiobjective Quantum Evolutionary Algorithm for the Vehicle Routing Problem with Customer Satisfaction," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-19, December.
    5. Govindan, K. & Jafarian, A. & Khodaverdi, R. & Devika, K., 2014. "Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food," International Journal of Production Economics, Elsevier, vol. 152(C), pages 9-28.
    6. Fanting Meng & Yong Ding & Wenjie Li & Rongge Guo, 2019. "Customer-Oriented Vehicle Routing Problem with Environment Consideration: Two-Phase Optimization Approach and Heuristic Solution," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-19, March.
    7. Houda Derbel & Bassem Jarboui & Rim Bhiri, 2019. "A skewed general variable neighborhood search algorithm with fixed threshold for the heterogeneous fleet vehicle routing problem," Annals of Operations Research, Springer, vol. 272(1), pages 243-272, January.
    8. S. F. Ghannadpour & S. Noori & R. Tavakkoli-Moghaddam, 2014. "A multi-objective vehicle routing and scheduling problem with uncertainty in customers’ request and priority," Journal of Combinatorial Optimization, Springer, vol. 28(2), pages 414-446, August.
    9. Alinaghian, Mahdi & Shokouhi, Nadia, 2018. "Multi-depot multi-compartment vehicle routing problem, solved by a hybrid adaptive large neighborhood search," Omega, Elsevier, vol. 76(C), pages 85-99.
    10. Yi, Wen-Jing & Zou, Le-Le & Guo, Jie & Wang, Kai & Wei, Yi-Ming, 2011. "How can China reach its CO2 intensity reduction targets by 2020? A regional allocation based on equity and development," Energy Policy, Elsevier, vol. 39(5), pages 2407-2415, May.
    11. Wu, Wentao & Beretta, Claudio & Cronje, Paul & Hellweg, Stefanie & Defraeye, Thijs, 2019. "Environmental trade-offs in fresh-fruit cold chains by combining virtual cold chains with life cycle assessment," Applied Energy, Elsevier, vol. 254(C).
    12. Songyi Wang & Fengming Tao & Yuhe Shi, 2018. "Optimization of Inventory Routing Problem in Refined Oil Logistics with the Perspective of Carbon Tax," Energies, MDPI, vol. 11(6), pages 1-17, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Han Yan & Min-Ju Song & Hee-Yong Lee, 2021. "A Systematic Review of Factors Affecting Food Loss and Waste and Sustainable Mitigation Strategies: A Logistics Service Providers’ Perspective," Sustainability, MDPI, vol. 13(20), pages 1-20, October.
    2. Cui, Huixia & Qiu, Jianlong & Cao, Jinde & Guo, Ming & Chen, Xiangyong & Gorbachev, Sergey, 2023. "Route optimization in township logistics distribution considering customer satisfaction based on adaptive genetic algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 204(C), pages 28-42.
    3. Hamid R. Sayarshad & Vahid Mahmoodian & Nebojša Bojović, 2021. "Dynamic Inventory Routing and Pricing Problem with a Mixed Fleet of Electric and Conventional Urban Freight Vehicles," Sustainability, MDPI, vol. 13(12), pages 1-16, June.
    4. Benyamin Moghaddasi & Amir Salar Ghafari Majid & Zahra Mohammadnazari & Amir Aghsami & Masoud Rabbani, 2023. "A green routing-location problem in a cold chain logistics network design within the Balanced Score Card pillars in fuzzy environment," Journal of Combinatorial Optimization, Springer, vol. 45(5), pages 1-33, July.
    5. Lihua Liu & Aneng He & Tian Tian & Lai Soon Lee & Hsin-Vonn Seow, 2024. "Bi-Objective Mixed Integer Nonlinear Programming Model for Low Carbon Location-Inventory-Routing Problem with Time Windows and Customer Satisfaction," Mathematics, MDPI, vol. 12(15), pages 1-35, July.
    6. Feng Li & Zhi-Ping Fan & Bing-Bing Cao & Xin Li, 2020. "Logistics Service Mode Selection for Last Mile Delivery: An Analysis Method Considering Customer Utility and Delivery Service Cost," Sustainability, MDPI, vol. 13(1), pages 1-22, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gaoyuan Qin & Fengming Tao & Lixia Li, 2019. "A Vehicle Routing Optimization Problem for Cold Chain Logistics Considering Customer Satisfaction and Carbon Emissions," IJERPH, MDPI, vol. 16(4), pages 1-17, February.
    2. Wang, Yong & Peng, Shouguo & Zhou, Xuesong & Mahmoudi, Monirehalsadat & Zhen, Lu, 2020. "Green logistics location-routing problem with eco-packages," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 143(C).
    3. Benyamin Moghaddasi & Amir Salar Ghafari Majid & Zahra Mohammadnazari & Amir Aghsami & Masoud Rabbani, 2023. "A green routing-location problem in a cold chain logistics network design within the Balanced Score Card pillars in fuzzy environment," Journal of Combinatorial Optimization, Springer, vol. 45(5), pages 1-33, July.
    4. Jin Li & Feng Wang & Yu He, 2020. "Electric Vehicle Routing Problem with Battery Swapping Considering Energy Consumption and Carbon Emissions," Sustainability, MDPI, vol. 12(24), pages 1-20, December.
    5. Abdul Salam Khan & Bashir Salah & Dominik Zimon & Muhammad Ikram & Razaullah Khan & Catalin I. Pruncu, 2020. "A Sustainable Distribution Design for Multi-Quality Multiple-Cold-Chain Products: An Integrated Inspection Strategies Approach," Energies, MDPI, vol. 13(24), pages 1-25, December.
    6. Jing Chen & Pengfei Gui & Tao Ding & Sanggyun Na & Yingtang Zhou, 2019. "Optimization of Transportation Routing Problem for Fresh Food by Improved Ant Colony Algorithm Based on Tabu Search," Sustainability, MDPI, vol. 11(23), pages 1-22, November.
    7. Liang Song & Hao Gu & Hejiao Huang, 2017. "A lower bound for the adaptive two-echelon capacitated vehicle routing problem," Journal of Combinatorial Optimization, Springer, vol. 33(4), pages 1145-1167, May.
    8. Yusuf Yilmaz & Can B. Kalayci, 2022. "Variable Neighborhood Search Algorithms to Solve the Electric Vehicle Routing Problem with Simultaneous Pickup and Delivery," Mathematics, MDPI, vol. 10(17), pages 1-22, August.
    9. Yanjun Shi & Na Lin & Qiaomei Han & Tongliang Zhang & Weiming Shen, 2020. "A Method for Transportation Planning and Profit Sharing in Collaborative Multi-Carrier Vehicle Routing," Mathematics, MDPI, vol. 8(10), pages 1-23, October.
    10. Fu, Zhengtang & Dong, Peiwu & Ju, Yanbing & Gan, Zhenkun & Zhu, Min, 2022. "An intelligent green vehicle management system for urban food reliably delivery:A case study of Shanghai, China," Energy, Elsevier, vol. 257(C).
    11. Wenzhu Liao & Lin Liu & Jiazhuo Fu, 2019. "A Comparative Study on the Routing Problem of Electric and Fuel Vehicles Considering Carbon Trading," IJERPH, MDPI, vol. 16(17), pages 1-25, August.
    12. Xiong Qiang & Martinson Yeboah Appiah & Kwasi Boateng & Frederick VonWolff Appiah, 2020. "Route optimization cold chain logistic distribution using greedy search method," OPSEARCH, Springer;Operational Research Society of India, vol. 57(4), pages 1115-1130, December.
    13. Liang Song & Hejiao Huang & Hongwei Du, 2016. "Approximation schemes for Euclidean vehicle routing problems with time windows," Journal of Combinatorial Optimization, Springer, vol. 32(4), pages 1217-1231, November.
    14. Jumbo, Olga & Moghaddass, Ramin, 2022. "Resource optimization and image processing for vegetation management programs in power distribution networks," Applied Energy, Elsevier, vol. 319(C).
    15. Ostermeier, Manuel & Henke, Tino & Hübner, Alexander & Wäscher, Gerhard, 2021. "Multi-compartment vehicle routing problems: State-of-the-art, modeling framework and future directions," European Journal of Operational Research, Elsevier, vol. 292(3), pages 799-817.
    16. Babagolzadeh, Mahla & Zhang, Yahua & Abbasi, Babak & Shrestha, Anup & Zhang, Anming, 2022. "Promoting Australian regional airports with subsidy schemes: Optimised downstream logistics using vehicle routing problem," Transport Policy, Elsevier, vol. 128(C), pages 38-51.
    17. Tianlu Zhao & Yongjian Yang & En Wang, 2020. "Minimizing the average arriving distance in carpooling," International Journal of Distributed Sensor Networks, , vol. 16(1), pages 15501477198, January.
    18. A. Mor & M. G. Speranza, 2020. "Vehicle routing problems over time: a survey," 4OR, Springer, vol. 18(2), pages 129-149, June.
    19. Chou, Chang-Chi & Chiang, Wen-Chu & Chen, Albert Y., 2022. "Emergency medical response in mass casualty incidents considering the traffic congestions in proximity on-site and hospital delays," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    20. Zhu, Bangzhu & Jiang, Mingxing & He, Kaijian & Chevallier, Julien & Xie, Rui, 2018. "Allocating CO2 allowances to emitters in China: A multi-objective decision approach," Energy Policy, Elsevier, vol. 121(C), pages 441-451.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:566:y:2021:i:c:s0378437120309353. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.