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Heuristics for container loading of furniture

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  • Egeblad, Jens
  • Garavelli, Claudio
  • Lisi, Stefano
  • Pisinger, David

Abstract

We consider a container loading problem that occurs at a typical furniture manufacturer. Each furniture item has an associated profit. Given container dimensions and a set of furniture items, the problem is to determine a subset of items with maximal profit sum that is loadable in the container. In the studied company, the problem arises hundreds of times daily during transport planning. Instances may contain more than one hundred different items with irregular shapes. To solve this complex problem we apply a set of heuristics successively that each solve one part of the problem. Large items are combined in specific structures to ensure proper protection of the items during transportation and to simplify the problem. The solutions generated by the heuristic has an average loading utilization of 91.3% for the most general instances with average running times around 100 seconds.

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  • Egeblad, Jens & Garavelli, Claudio & Lisi, Stefano & Pisinger, David, 2010. "Heuristics for container loading of furniture," European Journal of Operational Research, Elsevier, vol. 200(3), pages 881-892, February.
    • Handle: RePEc:eee:ejores:v:200:y:2010:i:3:p:881-892
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    3. Lim, Andrew & Ma, Hong & Qiu, Chaoyang & Zhu, Wenbin, 2013. "The single container loading problem with axle weight constraints," International Journal of Production Economics, Elsevier, vol. 144(1), pages 358-369.
    4. Bortfeldt, Andreas & Wäscher, Gerhard, 2013. "Constraints in container loading – A state-of-the-art review," European Journal of Operational Research, Elsevier, vol. 229(1), pages 1-20.
    5. I. Gimenez-Palacios & M. T. Alonso & R. Alvarez-Valdes & F. Parreño, 2021. "Logistic constraints in container loading problems: the impact of complete shipment conditions," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(1), pages 177-203, April.
    6. 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.
    7. Qiu, Huaxin & Wang, Sutong & Yin, Yunqiang & Wang, Dujuan & Wang, Yanzhang, 2022. "A deep reinforcement learning-based approach for the home delivery and installation routing problem," International Journal of Production Economics, Elsevier, vol. 244(C).
    8. 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.
    9. Alonso, M.T. & Alvarez-Valdes, R. & Iori, M. & Parreño, F. & Tamarit, J.M., 2017. "Mathematical models for multicontainer loading problems," Omega, Elsevier, vol. 66(PA), pages 106-117.
    10. Ramos, António G. & Silva, Elsa & Oliveira, José F., 2018. "A new load balance methodology for container loading problem in road transportation," European Journal of Operational Research, Elsevier, vol. 266(3), pages 1140-1152.
    11. Silva, Elsa & Ramos, António G. & Oliveira, José F., 2018. "Load balance recovery for multi-drop distribution problems: A mixed integer linear programming approach," Transportation Research Part B: Methodological, Elsevier, vol. 116(C), pages 62-75.
    12. 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.
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