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On solving multiobjective bin packing problems using evolutionary particle swarm optimization

Author

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  • Liu, D.S.
  • Tan, K.C.
  • Huang, S.Y.
  • Goh, C.K.
  • Ho, W.K.

Abstract

The bin packing problem is widely found in applications such as loading of tractor trailer trucks, cargo airplanes and ships, where a balanced load provides better fuel efficiency and safer ride. In these applications, there are often conflicting criteria to be satisfied, i.e., to minimize the bins used and to balance the load of each bin, subject to a number of practical constraints. Unlike existing studies that only consider the issue of minimum bins, a multiobjective two-dimensional mathematical model for bin packing problems with multiple constraints (MOBPP-2D) is formulated in this paper. To solve MOBPP-2D problems, a multiobjective evolutionary particle swarm optimization algorithm (MOEPSO) is proposed. Without the need of combining both objectives into a composite scalar weighting function, MOEPSO incorporates the concept of Pareto's optimality to evolve a family of solutions along the trade-off surface. Extensive numerical investigations are performed on various test instances, and their performances are compared both quantitatively and statistically with other optimization methods to illustrate the effectiveness and efficiency of MOEPSO in solving multiobjective bin packing problems.

Suggested Citation

  • Liu, D.S. & Tan, K.C. & Huang, S.Y. & Goh, C.K. & Ho, W.K., 2008. "On solving multiobjective bin packing problems using evolutionary particle swarm optimization," European Journal of Operational Research, Elsevier, vol. 190(2), pages 357-382, October.
  • Handle: RePEc:eee:ejores:v:190:y:2008:i:2:p:357-382
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    2. Kristina Yancey Spencer & Pavel V. Tsvetkov & Joshua J. Jarrell, 2019. "A greedy memetic algorithm for a multiobjective dynamic bin packing problem for storing cooling objects," Journal of Heuristics, Springer, vol. 25(1), pages 1-45, February.
    3. Tseng, Lin-Yu & Lin, Ya-Tai, 2009. "A hybrid genetic local search algorithm for the permutation flowshop scheduling problem," European Journal of Operational Research, Elsevier, vol. 198(1), pages 84-92, October.
    4. Goh, C.K. & Tan, K.C. & Liu, D.S. & Chiam, S.C., 2010. "A competitive and cooperative co-evolutionary approach to multi-objective particle swarm optimization algorithm design," European Journal of Operational Research, Elsevier, vol. 202(1), pages 42-54, April.
    5. Jie Fang & Yunqing Rao & Xusheng Zhao & Bing Du, 2023. "A Hybrid Reinforcement Learning Algorithm for 2D Irregular Packing Problems," Mathematics, MDPI, vol. 11(2), pages 1-17, January.
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    7. Wang, Yujia & Yang, Yupu, 2010. "Particle swarm with equilibrium strategy of selection for multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 200(1), pages 187-197, January.
    8. Bin, Wei & Qinke, Peng & Jing, Zhao & Xiao, Chen, 2012. "A binary particle swarm optimization algorithm inspired by multi-level organizational learning behavior," European Journal of Operational Research, Elsevier, vol. 219(2), pages 224-233.
    9. Cho, Huidae & Kim, Dongkyun & Olivera, Francisco & Guikema, Seth D., 2011. "Enhanced speciation in particle swarm optimization for multi-modal problems," European Journal of Operational Research, Elsevier, vol. 213(1), pages 15-23, August.
    10. 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.
    11. Xiangling Zhao & Yun Dong & Lei Zuo, 2023. "A Combinatorial Optimization Approach for Air Cargo Palletization and Aircraft Loading," Mathematics, MDPI, vol. 11(13), pages 1-16, June.
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