CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

A Modified Artificial Bee Colony for Solving the Container Loading Problem

by Ramadan A. Zeineldin, Alaa M. Morsy
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 114 - Number 3
Year of Publication: 2015
Authors: Ramadan A. Zeineldin, Alaa M. Morsy
10.5120/19958-1786

Ramadan A. Zeineldin, Alaa M. Morsy . A Modified Artificial Bee Colony for Solving the Container Loading Problem. International Journal of Computer Applications. 114, 3 ( March 2015), 19-24. DOI=10.5120/19958-1786

@article{ 10.5120/19958-1786,
author = { Ramadan A. Zeineldin, Alaa M. Morsy },
title = { A Modified Artificial Bee Colony for Solving the Container Loading Problem },
journal = { International Journal of Computer Applications },
issue_date = { March 2015 },
volume = { 114 },
number = { 3 },
month = { March },
year = { 2015 },
issn = { 0975-8887 },
pages = { 19-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume114/number3/19958-1786/ },
doi = { 10.5120/19958-1786 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:51:43.143928+05:30
%A Ramadan A. Zeineldin
%A Alaa M. Morsy
%T A Modified Artificial Bee Colony for Solving the Container Loading Problem
%J International Journal of Computer Applications
%@ 0975-8887
%V 114
%N 3
%P 19-24
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a modified artificial bee colony for solving the container loading problem because the cost can be reduced by increasing the space utilization ratio. This problem is solved in a two phased a Modified Artificial Bee Colony Optimization (MABCO) and a Wall-building approach. In the first phase, MABCO with its probabilistic decision rule is used to construct a sequence of boxes. The boxes are then arranged into a container with the Wall-building heuristic in the second phase. The nectar information feedback of MABCO using neighborhood updating rule helped to improve the solutions. Computational experiments were conducted on benchmark data set and the results obtained from the proposed approach are shown to be comparable with other methods from the literatures.

References
  1. Huang, W, He, K. A caving degree approach for the single container loading problem, European Journal of Operational Research 196 (2009) 93–101.
  2. Dereli, T, Das, S, G. A hybrid bee algorithm for solving container loading problems, Applied Soft Computing 11 (2011) 2854–2862.
  3. Dereli, T, Das, S, G. A hybrid simulated annealing algorithm for solving multi-objective container loading problems, Applied Artificial Intelligence 24 (5) (2010) 463–48.
  4. Bortfeldt, A, Gehring, H, Mack, D. A parallel tabu search algorithm for solving the container loading problem, Parallel Computing 29 (2003) 641–662.
  5. Scheithauer, G. Algorithm for the container loading problem, Operational Research Proceedings 26 (1992) 445–52.
  6. GonÇalves, F, J, Resende, C, G, M. A parallel multi-population biased random key genetic algorithm for a container loading problem, Computers & Operations Research 39 (2012) 179–190
  7. Dereli, T, Das, S, G. Development of a decision support system for solving container loading problems, Parallel Computing 25(2) (2010) 138–147.
  8. Chen, S, C, Lee, M, S, Shen, S, Q. An analytical model for the container loading problem, European Journal of operational research 80 (1995) 68-76.
  9. Karaboga, D, Basturk, B. Artificial bee colony optimization algorithm for solving constrained optimization problems, Advances in Soft Computing (2007) 789-798.
  10. Bischoff, E, E, Ratcliff, W, S, M. Issues in the development of approaches to Container loading, Omega23 (1995) 377-39.
  11. Karaboga, D. An idea based on honey bee swarm for numerical optimization. Technical Report-TR06, Kayseri, Turkey: Erciyes University; 2005.
  12. Alatas, B. Chaotic bee colony algorithms for global numerical optimization, Expert Systems with Applications (2010); 37:5682–7.
  13. Pisinger, D. Heuristics for the container loading problem, European Journal of Operational Research 141 (2002) 382–392.
  14. Fanslau, T, Bortfeldt, A. A tree search algorithm for solving the container loading problem, INFORMS Journal on Computing 22(2) (2010) 222–35.
  15. Rahnamayan, S. Opposition-based differential evolution. IEEE Transaction on Evolutionary Computation 2008; 12:64–79.
  16. Storn, R, Price, K. Differential evolution a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 2010; 23:689–94.
  17. Eley, M. Solving container loading problems by block arrangement, European Journal of Operational Research 141(2) (2002) 393–409.
  18. Bortfeldt, A, Gehring, H, Mack, D. A parallel tabu search algorithm for solving the container loading problem, Parallel Computing (2003);29(5):641–62.
  19. Morabito, R, Arenales, M. An AND/OR-graph approach to the container loading problem, International Transactions in Operational Research (1994); 1(1): 59–73.
  20. Bortfeldt, A, Gehring, H. A hybrid genetic algorithm for the container loading problem, European Journal of Operational Research 131(2001)143-161.
  21. George, J, A, Robinson, D, F. A heuristic for packing boxes into a container, Computers and Operations Research 7 (1980) 147–156.
  22. He, K, Huang, W. Solving the single container loading problem by a fast heuristic method, Optimization Methods and Software (2009) 1–15.
  23. Gehring, H, Menschner, K, Meyer, M, A. Computer-based heuristic for packing pooled shipment containers, European Journal of Operational Research 44 (1990) 277–288.
  24. Dyer, F, C. The biology of the dance language, Annual Review of Entomology 47 (2002): 917–949.
  25. Seow, V, H, Majid, Z, A, Yap. Ant Colony Optimization for Container Loading Problem, Journal of Mathematics and Statistics 8 (2): 169-175, 2012.
  26. Da?, G, S, Dereli, T. Container loading using hybrid bees algorithm, in Proceedings of The EU/ME 2007 – Meta-heuristics in the Service Industry, 8th Workshop of the EURO Working Group, EU/ME, The European Chapter on Meta-heuristics, University of Hohenheim, October 04–05 2007, Hohenheim, Stuttgart, Germany, 52–59.
  27. Lim, A, Ying, W. A new method for the three dimensional container packing Problem, AAAI American Association for Artificial Intelligence 1(2001) 342-347.
  28. Bahriye, A, Dervis, K. A modified Artificial Bee Colony algorithm for real-parameter optimization, Information Sciences 192 (2012) 120-142.
  29. Loh, H, T, Nee, A, Y, C. A packing algorithm for hexahedral boxes, in: Proceedings of the Industrial Automation Conference, Singapore 2 (1992) 115-126.
  30. Ngoi, B, K, Tay, A, M, L, Chua, E, S. Applying spatial representation techniques to the container packing problem. Int. J. Prod 32 (1994): 111-123.
  31. Bischoff, E, E, Janetz, F, Ratcliff, M, S, W. Loading pallets with non-identical items. Eur. J. Operat. Res (1995): 681-692.
  32. Liang, S, C, Lee C, Y, Huang S, W. A hybrid meta-heuristic for the container loading problem Commun. IIMA7 (2007): 73-84.
  33. Ali, K, Ahmad, A. Evaluating the effects of uncertainty in fuel price on transmission network expansion planning using DABC approach. Proceedings of the 2012 International Conference on Industrial Engineering and Operations Management Istanbul, Turkey, July 3 – 6, (2012).
  34. Jingqiao, Z, Arthur, C, S. A Adaptive differential evolution with optional external archive. IEEE (2009): 1051-8215.
Index Terms

Computer Science
Information Sciences

Keywords

Packing problem Container loading Bee colony Wall-building Meta-heuristic

Powered by PhDFocusTM