A prototype column generation strategy for the multiple container loading problem

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

25 Scopus Citations
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  • Wenbin Zhu
  • Weili Huang
  • Andrew Lim

Related Research Unit(s)


Original languageEnglish
Pages (from-to)27-39
Journal / PublicationEuropean Journal of Operational Research
Issue number1
Online published27 May 2012
Publication statusPublished - 16 Nov 2012


The multiple container loading cost minimization problem (MCLCMP) is a practical and useful problem in the transportation industry, where products of various dimensions are to be loaded into containers of various sizes so as to minimize the total shipping cost. The MCLCMP can be naturally formulated as a set cover problem and solved using column generation techniques, which is a popular method for handling huge numbers of variables. However, the direct application of column generation is not effective because feasible solutions to the pricing subproblem is required, which for the MCLCMP is NP-hard. We show that efficiency can be greatly improved by generating prototypes that approximate feasible solutions to the pricing problem rather than actual columns. For many hard combinatorial problems, the subproblem in column generation based algorithms is NP-hard; if suitable prototypes can be quickly generated that approximate feasible solutions, then our strategy can also be applied to speed up these algorithms.

Research Area(s)

  • Column generation, Heuristic, Multiple container loading problem, Packing