Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
A recursive computational procedure for container loading
Proceedings of the 23rd international conference on on Computers and industrial engineering
A parallel tabu search algorithm for solving the container loading problem
Parallel Computing - Special issue: Parallel computing in logistics
A Combinatorial Characterization of Higher-Dimensional Orthogonal Packing
Mathematics of Operations Research
3-D Container Packing Heuristics
Applied Intelligence
Proceedings of the 2005 ACM symposium on Applied computing
A GRASP Approach to the Container-Loading Problem
IEEE Intelligent Systems
A new heuristic algorithm for rectangle packing
Computers and Operations Research
An Exact Algorithm for Higher-Dimensional Orthogonal Packing
Operations Research
A Maximal-Space Algorithm for the Container Loading Problem
INFORMS Journal on Computing
Neighborhood structures for the container loading problem: a VNS implementation
Journal of Heuristics
A Tree Search Algorithm for Solving the Container Loading Problem
INFORMS Journal on Computing
An efficient placement heuristic for three-dimensional rectangular packing
Computers and Operations Research
A parallel multi-population biased random-key genetic algorithm for a container loading problem
Computers and Operations Research
A beam search approach to the container loading problem
Computers and Operations Research
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In the Single Container Loading Problem, the aim is to pack three-dimensional boxes into a three-dimensional container so as to maximize the volume utilization of the container. Many recently successful techniques for this problem share a similar structure involving the use of blocks of boxes. However, each technique comprises several seemingly disparate parts, which makes it difficult to analyze these techniques in a systematic manner. By dissecting block building approaches into 6 common elements, we found that existing techniques only differ in the strategies used for each element. This allows us to better understand these algorithms and identify their effective strategies. We then combine those effective strategies into a greedy heuristic for the SCLP problem. Computational experiments on 1,600 commonly used test cases show that our approach outperforms all other existing single-threaded approaches, and is comparable to the best parallel approach to the SCLP. It demonstrates the usefulness of our component-based analysis in the design of block building algorithms.