Genetic algorithm crossover operators for ordering applications
Computers and Operations Research - Special issue on genetic algorithms
The Three-Dimensional Bin Packing Problem
Operations Research
Guided Local Search for the Three-Dimensional Bin-Packing Problem
INFORMS Journal on Computing
The Ordered Open-End Bin-Packing Problem
Operations Research
3-D Container Packing Heuristics
Applied Intelligence
Algorithm 864: General and robot-packable variants of the three-dimensional bin packing problem
ACM Transactions on Mathematical Software (TOMS)
A new heuristic algorithm for cuboids packing with no orientation constraints
Computers and Operations Research
Heuristic approaches for the two- and three-dimensional knapsack packing problem
Computers and Operations Research
An Optimization Algorithm for the Ordered Open-End Bin-Packing Problem
Operations Research
An Exact Algorithm for Higher-Dimensional Orthogonal Packing
Operations Research
Extreme Point-Based Heuristics for Three-Dimensional Bin Packing
INFORMS Journal on Computing
A Tree Search Algorithm for Solving the Container Loading Problem
INFORMS Journal on Computing
A parallel multi-population biased random-key genetic algorithm for a container loading problem
Computers and Operations Research
Review: Measuring instance difficulty for combinatorial optimization problems
Computers and Operations Research
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This article aims to tackle a practical three-dimensional packing problem, where a number of cartons of diverse sizes are to be packed into a bin with fixed length and width but open height. Each carton is allowed to be packed in any one of the six orientations, and the carton edges are parallel to the bin edges. The allowance of variable carton orientations exponentially increases the solution space and makes the problem very challenging to solve. This study first elaborately devises the packing procedure, which converts an arbitrary sequence of cartons into a compact packing solution and subsequently develops an improved genetic algorithm (IGA) to evolve a set of solutions. Moreover, a novel global search framework (GSF), utilizing the concept of evolutionary gradient, is proposed to further improve the solution quality. Numerical experiments indicate that IGA provides faster and better results and GSF demonstrates its superior performance, especially in solving relative large-size and heterogeneous instances. Applying the proposed algorithms to some benchmarking cases of the three-dimensional strip packing problem also indicates that the algorithms are robust and effective compared to existing methods in the literature.