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Cold chain distribution: How to deal with node and arc time windows?

Author

Listed:
  • Yi Zhang

    (Beijing Wuzi University)

  • Guowei Hua

    (Beijing Jiaotong University)

  • T. C. E. Cheng

    (The Hong Kong Polytechnic University)

  • Juliang Zhang

    (Beijing Jiaotong University)

Abstract

Commonly encountered in cold chain logistics, third-party distribution firms are required to deliver temperature-sensitive food products to various retailers with two kinds of time-window constraints: (1) the delivery service must begin within the time windows imposed by the retailers (called node time windows) and (2) each vehicle route is available only in a predefined time interval prescribed by the government (called arc time windows). We study the effects of the retailer time window type (i.e., density of the node time-window constraints) and other cost-related factors on a distribution firm’s legitimacy choice (i.e., the firm chooses to either comply with or violate the governmental time-window policy), food quality, and pollutant emissions in the urban environment. We model the problem as an intractable vehicle routing problem with node and arc time windows and develop a genetic algorithm to tackle it. We conduct a case study to generate the managerial insights on dealing with time windows. We find that the governmental time windows will increase the distribution cost. The governmental time windows has a negative effect on pollutant emissions while showing a positive effect on food safety. Given governmental time windows, a higher demand for node time windows will result in more governmental time-window violations or lower vehicle load factor, which depends on the vehicle fixed cost, fuel price, and government penalty.

Suggested Citation

  • Yi Zhang & Guowei Hua & T. C. E. Cheng & Juliang Zhang, 2020. "Cold chain distribution: How to deal with node and arc time windows?," Annals of Operations Research, Springer, vol. 291(1), pages 1127-1151, August.
    • Handle: RePEc:spr:annopr:v:291:y:2020:i:1:d:10.1007_s10479-018-3071-0
    DOI: 10.1007/s10479-018-3071-0
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    3. Francesco Ciardiello & Andrea Genovese & Shucheng Luo & Antonino Sgalambro, 2023. "A game-theoretic multi-stakeholder model for cost allocation in urban consolidation centres," Annals of Operations Research, Springer, vol. 324(1), pages 663-686, May.
    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. Hafiz Wasim Akram & Samreen Akhtar & Alam Ahmad & Imran Anwar & Mohammad Ali Bait Ali Sulaiman, 2023. "Developing a Conceptual Framework Model for Effective Perishable Food Cold-Supply-Chain Management Based on Structured Literature Review," Sustainability, MDPI, vol. 15(6), pages 1-28, March.
    6. Meiling He & Mei Yang & Xiaohui Wu & Jun Pu & Kazuhiro Izui, 2024. "Evaluating and Analyzing the Efficiency and Influencing Factors of Cold Chain Logistics in China’s Major Urban Agglomerations under Carbon Constraints," Sustainability, MDPI, vol. 16(5), pages 1-19, February.

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