A new heuristic algorithm for rectangle packing
Computers and Operations Research
Evolving reusable 3d packing heuristics with genetic programming
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Ray casting of multiple volumetric datasets with polyhedral boundaries on manycore GPUs
ACM SIGGRAPH Asia 2009 papers
An efficient placement heuristic for three-dimensional rectangular packing
Computers and Operations Research
A global search framework for practical three-dimensional packing with variable carton orientations
Computers and Operations Research
A heuristic block-loading algorithm based on multi-layer search for the container loading problem
Computers and Operations Research
Automating the packing heuristic design process with genetic programming
Evolutionary Computation
Journal of Mathematical Modelling and Algorithms
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The three-dimensional cuboids packing is NP-hard and finds many applications in the transportation industry. The problem is to pack a subset of cuboid boxes into a big cuboid container such that the total volume of the packed boxes is maximized. The boxes have no orientation constraints, i.e. they can be rotated by 90^@? in any direction. A new heuristic algorithm is presented that defines a conception of caving degree to judge how close a packing box is to those boxes already packed into the container, and always chooses a packing with the largest caving degree to do. The performance is evaluated on all the 47 related benchmarks from the OR-Library. Experiments on a personal computer show a high average volume utilization of 94.6% with an average computation time of 23min for the strengthened A1 algorithm, which improves current best records by 3.6%. In addition, the top-10 A2 algorithm achieved an average volume utilization of 91.9% with an average computation time of 55s, which also got higher utilization than current best records reported in the literature.