A computer-aided box stacking model for truck transport and pallets
Computers in Industry
Container packing in a multi-customer delivering operation
Proceedings of the 23rd international conference on on Computers and industrial engineering
The Three-Dimensional Bin Packing Problem
Operations Research
Effective methods for a container packing operation
Mathematical and Computer Modelling: An International Journal
A caving degree based flake arrangement approach for the container loading problem
Computers and Industrial Engineering
A novel hybrid tabu search approach to container loading
Computers and Operations Research
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 hybrid 'bee(s) algorithm' for solving container loading problems
Applied Soft Computing
Three-dimensional container loading models with cargo stability and load bearing constraints
Computers and Operations Research
A two-stage tabu search algorithm with enhanced packing heuristics for the 3L-CVRP and M3L-CVRP
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
A memetic algorithm for the quadratic multiple container packing problem
Applied Intelligence
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In this paper, we study the 3-D container packing problem. The problem is divided into box selection, space selection, box orientation and new space generation sub-problems. As a first step, a basic heuristic is devised. From results using this heuristic, problems are categorized as homogeneous and heterogeneous. Two augmenting heuristics are then formulated to deal with these categories. They are complementary in their capabilities in dealing with a range of practical problems, and in terms of their computational consumption. Results using our algorithmsexceed the benchmark by 4.5% on average.