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The multiple container loading problem with preference

Author

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  • Tian, Tian
  • Zhu, Wenbin
  • Lim, Andrew
  • Wei, Lijun

Abstract

An international audio equipment manufacturer would like to help its customers reduce unit shipping costs by adjusting order quantity according to product preference. We introduce the problem faced by the manufacturer as the Multiple Container Loading Problem with Preference (MCLPP) and propose a combinatorial formulation for the MCLPP. We develop a two-phase algorithm to solve the problem. In phase one, we estimate the most promising region of the solution space based on performance statistics of the sub-problem solver. In phase two, we find a feasible solution in the promising region by solving a series of 3D orthogonal packing problems. A unique feature of our approach is that we try to estimate the average capability of the sub-routine algorithm for the single container loading problem in phase one and take it into account in the overall planning. To obtain a useful estimate, we randomly generate a large set of single container loading problem instances that are statistically similar to the manufacturer’s historical order data. We generate a large set of test instances based on the historical data provided by the manufacturer and conduct extensive computational experiments to demonstrate the effectiveness of our approach.

Suggested Citation

  • Tian, Tian & Zhu, Wenbin & Lim, Andrew & Wei, Lijun, 2016. "The multiple container loading problem with preference," European Journal of Operational Research, Elsevier, vol. 248(1), pages 84-94.
    • Handle: RePEc:eee:ejores:v:248:y:2016:i:1:p:84-94
    DOI: 10.1016/j.ejor.2015.07.002
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    References listed on IDEAS

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    Cited by:

    1. Sheng, Liu & Hongxia, Zhao & Xisong, Dong & Changjian, Cheng, 2016. "A heuristic algorithm for container loading of pallets with infill boxes," European Journal of Operational Research, Elsevier, vol. 252(3), pages 728-736.
    2. Novas, Juan M. & Ramello, Juan Ignacio & Rodríguez, María Analía, 2020. "Generalized disjunctive programming models for the truck loading problem: A case study from the non-alcoholic beverages industry," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 140(C).
    3. Kurpel, Deidson Vitorio & Scarpin, Cassius Tadeu & Pécora Junior, José Eduardo & Schenekemberg, Cleder Marcos & Coelho, Leandro C., 2020. "The exact solutions of several types of container loading problems," European Journal of Operational Research, Elsevier, vol. 284(1), pages 87-107.
    4. Vélez-Gallego, Mario C. & Teran-Somohano, Alejandro & Smith, Alice E., 2020. "Minimizing late deliveries in a truck loading problem," European Journal of Operational Research, Elsevier, vol. 286(3), pages 919-928.
    5. Castellucci, Pedro B. & Toledo, Franklina M.B. & Costa, Alysson M., 2019. "Output maximization container loading problem with time availability constraints," Operations Research Perspectives, Elsevier, vol. 6(C).
    6. Carlos A. Vega-Mejía & Jairo R. Montoya-Torres & Sardar M. N. Islam, 2019. "Consideration of triple bottom line objectives for sustainability in the optimization of vehicle routing and loading operations: a systematic literature review," Annals of Operations Research, Springer, vol. 273(1), pages 311-375, February.
    7. Araya, Ignacio & Moyano, Mauricio & Sanchez, Cristobal, 2020. "A beam search algorithm for the biobjective container loading problem," European Journal of Operational Research, Elsevier, vol. 286(2), pages 417-431.

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