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Probabilistic Tabu Search for the Cross-Docking Assignment Problem

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  • Guemri, Oualid
  • Nduwayo, Placide
  • Todosijević, Raca
  • Hanafi, Saïd
  • Glover, Fred

Abstract

The Cross-Docking Assignment Problem (CDAP) is a challenging optimization problem in supply chain management with important practical applications in the trucking industry. The goal is to assign incoming trucks (outgoing trucks) to inbound (outbound) doors to minimize the material handling cost within a cross-docking platform while respecting the capacity and assignment constraints. A capacity constraint is imposed on each inbound/outbound door and an associated assignment constraint is imposed on each incoming/outgoing truck requiring it to be assigned to only one inbound/outbound door. To solve this NP-hard optimization problem, we develop two novel heuristics based on Probabilistic Tabu Search utilizing a new neighborhood structure applicable both to CDAP and related problems. The proposed heuristics are evaluated on 99 benchmark instances from the literature, disclosing that our approaches outperform recent state-of-the-art approaches by reaching 45 previous best-known solutions and discovering 53 new best-known solutions while consuming significantly less CPU time.

Suggested Citation

  • Guemri, Oualid & Nduwayo, Placide & Todosijević, Raca & Hanafi, Saïd & Glover, Fred, 2019. "Probabilistic Tabu Search for the Cross-Docking Assignment Problem," European Journal of Operational Research, Elsevier, vol. 277(3), pages 875-885.
    • Handle: RePEc:eee:ejores:v:277:y:2019:i:3:p:875-885
    DOI: 10.1016/j.ejor.2019.03.030
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Feifeng Zheng & Yaxin Pang & Yinfeng Xu, 2022. "Heuristics for cross-docking scheduling of truck arrivals, truck departures and shop-floor operations," Journal of Combinatorial Optimization, Springer, vol. 43(5), pages 1571-1601, July.
    2. Lyu, Zhongyuan & Huang, George Q., 2023. "Cross-docking based factory logistics unitisation process: An approximate dynamic programming approach," European Journal of Operational Research, Elsevier, vol. 311(1), pages 112-124.
    3. Hanafi, Saïd & Wang, Yang & Glover, Fred & Yang, Wei & Hennig, Rick, 2023. "Tabu search exploiting local optimality in binary optimization," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1037-1055.
    4. Li, Mingjie & Hao, Jin-Kao & Wu, Qinghua, 2024. "A flow based formulation and a reinforcement learning based strategic oscillation for cross-dock door assignment," European Journal of Operational Research, Elsevier, vol. 312(2), pages 473-492.
    5. Fred Glover & Gary Kochenberger & Moses Ma & Yu Du, 2020. "Quantum Bridge Analytics II: QUBO-Plus, network optimization and combinatorial chaining for asset exchange," 4OR, Springer, vol. 18(4), pages 387-417, December.
    6. Fred Glover & Vicente Campos & Rafael Martí, 2021. "Tabu search tutorial. A Graph Drawing Application," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 319-350, July.
    7. Fred Glover & Gary Kochenberger & Moses Ma & Yu Du, 2022. "Quantum Bridge Analytics II: QUBO-Plus, network optimization and combinatorial chaining for asset exchange," Annals of Operations Research, Springer, vol. 314(1), pages 185-212, July.
    8. Xi, Xiang & Changchun, Liu & Yuan, Wang & Loo Hay, Lee, 2020. "Two-stage conflict robust optimization models for cross-dock truck scheduling problem under uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 144(C).

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