On solving multiobjective bin packing problems using evolutionary particle swarm optimization

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

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Author(s)

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

Detail(s)

Original languageEnglish
Pages (from-to)357-382
Journal / PublicationEuropean Journal of Operational Research
Volume190
Issue number2
Online published29 Jun 2007
Publication statusPublished - 16 Oct 2008
Externally publishedYes

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.

Research Area(s)

  • Bin packing, Evolutionary algorithms, Multiobjective, Particle swarm optimization