The container loading problem

Authors:
Andrew Lim;Xingwen Zhang
Affiliations:
Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong;Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
Venue:
Proceedings of the 2005 ACM symposium on Applied computing
Year:
2005

Citing 2
Cited 6

“Squeaky Wheel” optimization

AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
"Squeaky Wheel" optimization

Journal of Artificial Intelligence Research

Smart container loading

Journal of Computational Methods in Sciences and Engineering - Special Supplement Issue in Section A and B: Selected Papers from the ISCA International Conference on Software Engineering and Data Engineering, 2009
Two natural heuristics for 3D packing with practical loading constraints

PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
A heuristic for the multiple container loading cost minimization problem

IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
A two-stage tabu search algorithm with enhanced packing heuristics for the 3L-CVRP and M3L-CVRP

Computers and Operations Research
Automating the packing heuristic design process with genetic programming

Evolutionary Computation
The six elements to block-building approaches for the single container loading problem

Applied Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper addresses single and multiple container loading problems. We propose to use dynamic prioritization to handle awkward box types. The box type with a higher priority will be packed onto lower surfaces of the container, or packed in earlier containers. The solution found in one iteration of the algorithm is analyzed, and the priorities are updated to be used in the next iteration. Our algorithm outperformed all previous methods using standard benchmark data sets. We found the existing test data for the multiple container loading problem to be deficient and supplemented them by generating new test data consisting of 2800 test cases. The results from our algorithm using this data set are excellent.