IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i11p4910-d1665300.html
   My bibliography  Save this article

Advanced Optimization for Enhancing Sustainability in Metropolitan Cold Chain Systems

Author

Listed:
  • Yanxia Wang

    (School of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China)

  • Yuchen Wang

    (School of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China)

  • Shaojun Gan

    (School of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China)

Abstract

The objective of this study is to explore the cold chain system in a metropolitan area, focusing on the overall system cost encompassing both distribution centers and transportation. The research delves into the planning of urban cold chain systems, considering fluctuating minimum customer demands, the traffic conditions of potential new centers, and the variability in carbon-trading prices. To manage the complexity of these objectives and inherent uncertainties, we introduce a flexible chance-constrained programming model for the cold chain system (FCCP-CCS). An FCCP-CCS programming model is developed to address the multifaceted goals and various uncertainties. The effectiveness of this model is validated through experimental analysis using real-world data from a major city’s cold chain system. The findings of this study reveal several key insights: (1) The levels of confidence and satisfaction significantly impact system optimization, with higher levels leading to increased consumption. (2) Customer demand variations would determine the transportation and the potential new centers in the system. (3) The surroundings of a distribution center partly indicate its service quality. (4) Governmental adjustments in carbon-trading prices can effectively enhance the overall sustainability of the urban cold chain system. This research highlights the importance of optimization in designing and managing urban cold chain systems, particularly in environmental sustainability.

Suggested Citation

  • Yanxia Wang & Yuchen Wang & Shaojun Gan, 2025. "Advanced Optimization for Enhancing Sustainability in Metropolitan Cold Chain Systems," Sustainability, MDPI, vol. 17(11), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:11:p:4910-:d:1665300
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/11/4910/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/11/4910/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Yuxi & Wang, Jingxin & Schuler, Jamie & Hartley, Damon & Volk, Timothy & Eisenbies, Mark, 2020. "Optimization of harvest and logistics for multiple lignocellulosic biomass feedstocks in the northeastern United States," Energy, Elsevier, vol. 197(C).
    2. Bruce L. Miller & Harvey M. Wagner, 1965. "Chance Constrained Programming with Joint Constraints," Operations Research, INFORMS, vol. 13(6), pages 930-945, December.
    3. Daqing Wu & Jiazhen Huo & Gefu Zhang & Weihua Zhang, 2018. "Minimization of Logistics Cost and Carbon Emissions Based on Quantum Particle Swarm Optimization," Sustainability, MDPI, vol. 10(10), pages 1-15, October.
    4. Zhang, Ying & Snyder, Lawrence V. & Ralphs, Ted K. & Xue, Zhaojie, 2016. "The competitive facility location problem under disruption risks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 93(C), pages 453-473.
    5. Li, Ye & Liu, Zihan & Sang, Yufeng & Hu, Jingfan & Li, Bojia & Zhang, Xinyu & Jurasz, Jakub & Zheng, Wandong, 2023. "Optimization of integrated energy system for low-carbon community considering the feasibility and application limitation," Applied Energy, Elsevier, vol. 348(C).
    6. Yu Yvette Zhang & Qiujie Zheng & Huashu Wang, 2021. "Challenges and Opportunities Facing the Chinese Economy in the New Decade: Epidemics, Food, Labor, E-Commerce, and Trade," Chinese Economy, Taylor & Francis Journals, vol. 54(6), pages 373-375, November.
    7. Aboolian, Robert & Berman, Oded & Krass, Dmitry, 2007. "Competitive facility location and design problem," European Journal of Operational Research, Elsevier, vol. 182(1), pages 40-62, October.
    8. Kaiqi Zhao & Hongxu Shi & Yu Yvette Zhang & Jiping Sheng, 2021. "Fresh Produce E-Commerce and Online Shoppers’ Purchase Intention," Chinese Economy, Taylor & Francis Journals, vol. 54(6), pages 415-429, November.
    Full references (including those not matched with items on IDEAS)

    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. Shamekhi Amiri, A. & Torabi, S. Ali & Ghodsi, R., 2018. "An iterative approach for a bi-level competitive supply chain network design problem under foresight competition and variable coverage," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 109(C), pages 99-114.
    2. Minjiao Zhang & Simge Küçükyavuz & Saumya Goel, 2014. "A Branch-and-Cut Method for Dynamic Decision Making Under Joint Chance Constraints," Management Science, INFORMS, vol. 60(5), pages 1317-1333, May.
    3. Borodin, Valeria & Dolgui, Alexandre & Hnaien, Faicel & Labadie, Nacima, 2016. "Component replenishment planning for a single-level assembly system under random lead times: A chance constrained programming approach," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 79-86.
    4. Xiang Li & Tianyu Zhang & Liang Wang & Hongguang Ma & Xiande Zhao, 2022. "A minimax regret model for the leader–follower facility location problem," Annals of Operations Research, Springer, vol. 309(2), pages 861-882, February.
    5. Fernández, José & Hendrix, Eligius M.T., 2013. "Recent insights in Huff-like competitive facility location and design," European Journal of Operational Research, Elsevier, vol. 227(3), pages 581-584.
    6. Luo, Li & O'Hehir, Jim & Regan, Courtney M. & Meng, Li & Connor, Jeffery D. & Chow, Christopher W.K., 2021. "An integrated strategic and tactical optimization model for forest supply chain planning," Forest Policy and Economics, Elsevier, vol. 131(C).
    7. Burcin Bozkaya & Seda Yanik & Selim Balcisoy, 2010. "A GIS-Based Optimization Framework for Competitive Multi-Facility Location-Routing Problem," Networks and Spatial Economics, Springer, vol. 10(3), pages 297-320, September.
    8. Dam, Tien Thanh & Ta, Thuy Anh & Mai, Tien, 2022. "Submodularity and local search approaches for maximum capture problems under generalized extreme value models," European Journal of Operational Research, Elsevier, vol. 300(3), pages 953-965.
    9. S Rezapour & R Zanjirani Farahani & T Drezner, 2011. "Strategic design of competing supply chain networks for inelastic demand," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(10), pages 1784-1795, October.
    10. Patrice Gaillardetz & Saeb Hachem, 2019. "Risk-Control Strategies," Papers 1908.02228, arXiv.org.
    11. Rocchetta, Roberto & Crespo, Luis G., 2021. "A scenario optimization approach to reliability-based and risk-based design: Soft-constrained modulation of failure probability bounds," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    12. Bilsel, R. Ufuk & Ravindran, A., 2011. "A multiobjective chance constrained programming model for supplier selection under uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 45(8), pages 1284-1300, September.
    13. Luís M. Fernandes & Joaquim J. Júdice & Hanif D. Sherali & António P. Antunes, 2011. "Siting and Sizing of Facilities under Probabilistic Demands," Journal of Optimization Theory and Applications, Springer, vol. 149(2), pages 420-440, May.
    14. Ümit Sakallı & Ömer Baykoç & Burak Birgören, 2011. "Stochastic optimization for blending problem in brass casting industry," Annals of Operations Research, Springer, vol. 186(1), pages 141-157, June.
    15. Azad, Nader & Hassini, Elkafi, 2019. "Recovery strategies from major supply disruptions in single and multiple sourcing networks," European Journal of Operational Research, Elsevier, vol. 275(2), pages 481-501.
    16. Zare M., Yahia & Daneshmand, Ahmad, 1995. "A linear approximation method for solving a special class of the chance constrained programming problem," European Journal of Operational Research, Elsevier, vol. 80(1), pages 213-225, January.
    17. Sinha, Priyank & Kumar, Sameer & Chandra, Charu, 2023. "Strategies for ensuring required service level for COVID-19 herd immunity in Indian vaccine supply chain," European Journal of Operational Research, Elsevier, vol. 304(1), pages 339-352.
    18. Yanikoglu, I. & den Hertog, D., 2011. "Safe Approximations of Chance Constraints Using Historical Data," Other publications TiSEM ab77f6f2-248a-42f1-bde1-0, Tilburg University, School of Economics and Management.
    19. Cooper, W. W. & Hemphill, H. & Huang, Z. & Li, S. & Lelas, V. & Sullivan, D. W., 1997. "Survey of mathematical programming models in air pollution management," European Journal of Operational Research, Elsevier, vol. 96(1), pages 1-35, January.
    20. L. Jeff Hong & Zhiyuan Huang & Henry Lam, 2021. "Learning-Based Robust Optimization: Procedures and Statistical Guarantees," Management Science, INFORMS, vol. 67(6), pages 3447-3467, June.

    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:gam:jsusta:v:17:y:2025:i:11:p:4910-:d:1665300. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.