Browse > Article

A Study on the Quadratic Multiple Container Packing Problem  

Yeo, Gi-Tae (인천대학교 동북아물류대학원)
Soak, Sang-Moon (특허청 정보심사과)
Lee, Sang-Wook (목원대학교 정보통신공학과)
Publication Information
Journal of the Korean Operations Research and Management Science Society / v.34, no.3, 2009 , pp. 125-136 More about this Journal
Abstract
The container packing problem Is one of the traditional optimization problems, which is very related to the knapsack problem and the bin packing problem. In this paper, we deal with the quadratic multiple container picking problem (QMCPP) and it Is known as a NP-hard problem. Thus, It seems to be natural to use a heuristic approach such as evolutionary algorithms for solving the QMCPP. Until now, only a few researchers have studied on this problem and some evolutionary algorithms have been proposed. This paper introduces a new efficient evolutionary algorithm for the QMCPP. The proposed algorithm is devised by improving the original network random key method, which is employed as an encoding method in evolutionary algorithms. And we also propose local search algorithms and incorporate them with the proposed evolutionary algorithm. Finally we compare the proposed algorithm with the previous algorithms and show the proposed algorithm finds the new best results in most of the benchmark instances.
Keywords
Quadratic Multiple Container Packing Problem; Evolutionary Algorithm; Network Random Key Encoding;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Hiley, A. and Julstrom, B.A., 'The quadratic multiple knapsack problem and three heuristic approaches to it,' Procs. of the genetic and evolutionary computation conference, Vol.1(2006), pp.547-552   DOI
2 Raidl, G.R, 'A Weight-Coded Genetic Algorithm for the Multiple Container Packing Problem,' proc. of the 1999 ACM sympo. on Applied Computing, (1999), pp.291-296
3 Sarac, T. and A. Sipahioglu, 'A Genetic Algorithm for the Quadratic Multiple Knapsack Problem,' in BVAl2007, LNCS, Vol.4729(2007), pp.490-498   DOI
4 Singh, A. and A.K. Gupta, 'A hybrid heuristic for the maximum clique problem,' Journal of Heuristic, Vol.12(2006), pp.5-22   DOI   ScienceOn
5 Korea Maritime Institute, Dynamic Changes on Transshipment Cargoes among Northeast Asian Ports, Seoul, 2004
6 Bean, J., 'Genetic Algorithms and Random Keys for Sequencing and Optimization,' ORSA Journal on Computing, Vol.6, No.2(1994), pp.154-160   DOI
7 Rothlauf, F., D. Goldberg, and A Heinzl, 'Network Random Keys-A Tree Network Representation Scheme for Genetic and Evolutionary Algorithms,' Evolutionary Computation, Vol.10, No.1(2002), pp.75-97   DOI   ScienceOn
8 석상문, 장석철, 이상욱, 안병하, '고정비용수송 문제를 위한 효율적인 진화알고리듬', '대한산업공학회지', 제31권, 제1호(2005), pp.79-86
9 Soak, S.M., D. Come, and B.H. Ahn, 'A New Encoding for the Degree Constrained Minimum Spanning Tree Problem,' in KES 2004, LNAl, Vol.3213(2004), pp.952-958   DOI
10 http//cermsemuniv-parisl.fr/soutif/QKP
11 Singh, A. and A.S. Baghel, 'A New Grouping Algorithm for the Quadratic Multiple Knapsack Problem,' in EvoCOP 2007, INCS, Vol.4446(2007), pp.210-218
12 Back, T, Fogel, D.B., and Michalewicz, Z., Handbook of Evolutionary Canputation, Oxford University Press, 1997