Developing a simulated annealing algorithm for the cutting stock problem
Computers and Industrial Engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Hybrid Genetic Algorithm for Assembly Line Balancing
Journal of Heuristics
A New Exact Algorithm for General Orthogonal D-Dimensional Knapsack Problems
ESA '97 Proceedings of the 5th Annual European Symposium on Algorithms
The Three-Dimensional Bin Packing Problem
Operations Research
A parallel tabu search algorithm for solving the container loading problem
Parallel Computing - Special issue: Parallel computing in logistics
An evolutionary algorithm for manufacturing cell formation
Computers and Industrial Engineering
A GRASP Approach to the Container-Loading Problem
IEEE Intelligent Systems
Survivable IP network design with OSPF routing
Networks - Special Issue on Multicommodity Flows and Network Design
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
Solving the single-container loading problem by a fast heuristic method
Optimization Methods & Software - The 22nd European Conference on Operational Research, 8-11 July 2007, Prague, Czech Republic
A Tree Search Algorithm for Solving the Container Loading Problem
INFORMS Journal on Computing
Loading problem in multiple containers and pallets using strategic search method
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
A global search framework for practical three-dimensional packing with variable carton orientations
Computers and Operations Research
Packing first, routing second-a heuristic for the vehicle routing and loading problem
Computers and Operations Research
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This paper presents a multi-population biased random-key genetic algorithm (BRKGA) for the single container loading problem (3D-CLP) where several rectangular boxes of different sizes are loaded into a single rectangular container. The approach uses a maximal-space representation to manage the free spaces in the container. The proposed algorithm hybridizes a novel placement procedure with a multi-population genetic algorithm based on random keys. The BRKGA is used to evolve the order in which the box types are loaded into the container and the corresponding type of layer used in the placement procedure. A heuristic is used to determine the maximal space where each box is placed. A novel procedure is developed for joining free spaces in the case where full support from below is required. The approach is extensively tested on the complete set of test problem instances of Bischoff and Ratcliff [1] and Davies and Bischoff [2] and is compared with 13 other approaches. The test set consists of 1500 instances from weakly to strongly heterogeneous cargo. The computational experiments demonstrate that not only the approach performs very well in all types of instance classes but also it obtains the best overall results when compared with other approaches published in the literature.