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Heuristic algorithms for truck scheduling of cross-docking operations in cold-chain logistics

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  • Feifeng Zheng
  • Yaxin Pang
  • Yinfeng Xu
  • Ming Liu

Abstract

Nowadays cross-docking operations play a significant role in the cold-chain logistics. This paper addresses the cold-chain cross-docking truck scheduling problem where two types of products, i.e. refrigerated and frozen ones, demand separate trucks and storage areas with distinct temperature settings during their storage and transportation. We present a mixed-integer linear programming model with the objective of minimising the total operational costs that consist of inbound truck arrival penalties for violating contracted time windows, product delivery tardiness penalties, inventory costs and outbound truck transportation costs. Due to the strong NP-hardness of the considered problem, we solve it in two phases where the inbound truck arrival schedule and the schedule of outbound truck departure together with product processing are produced, respectively. Four heuristic algorithms are proposed to generate complete solutions of the considered two-stage problem, which are the combinations of two solution frameworks for the first stage and two methods for the second stage. Computational experiments are carried out to verify the effectiveness and efficiency of the proposed heuristic algorithms in terms of the solution quality and running time, respectively.

Suggested Citation

  • Feifeng Zheng & Yaxin Pang & Yinfeng Xu & Ming Liu, 2021. "Heuristic algorithms for truck scheduling of cross-docking operations in cold-chain logistics," International Journal of Production Research, Taylor & Francis Journals, vol. 59(21), pages 6579-6600, November.
    • Handle: RePEc:taf:tprsxx:v:59:y:2021:i:21:p:6579-6600
    DOI: 10.1080/00207543.2020.1821118
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    Cited by:

    1. Kochakkashani, Farid & Kayvanfar, Vahid & Haji, Alireza, 2023. "Supply chain planning of vaccine and pharmaceutical clusters under uncertainty: The case of COVID-19," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    2. Qinyang Bai & Yuxiang Yuan & Xueqin Fu & Zhili Zhou, 2024. "Vehicle routing Problem for cold chain logistics based on data fusion technology to predict travel time," Operational Research, Springer, vol. 24(4), pages 1-34, December.
    3. Feng Yang & Zhong Wu & Xiaoyan Teng & Shaojian Qu, 2022. "Robust Counterpart Models for Fresh Agricultural Product Routing Planning Considering Carbon Emissions and Uncertainty," Sustainability, MDPI, vol. 14(22), pages 1-17, November.
    4. Suprava Chakraborty & Devaraj Elangovan & Padma Lakshmi Govindarajan & Mohamed F. ELnaggar & Mohammed M. Alrashed & Salah Kamel, 2022. "A Comprehensive Review of Path Planning for Agricultural Ground Robots," Sustainability, MDPI, vol. 14(15), pages 1-19, July.

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