Journal Article10.1287/IJOC.1070.0254
A Maximal-Space Algorithm for the Container Loading Problem
TL;DR: A greedy randomized adaptive search procedure (GRASP) for the container loading problem is presented, based on a constructive block heuristic that builds upon the concept of maximal space, a nondisjoint representation of the free space in a container.
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Abstract: In this paper, a greedy randomized adaptive search procedure (GRASP) for the container loading problem is presented. This approach is based on a constructive block heuristic that builds upon the concept of maximal space, a nondisjoint representation of the free space in a container.
This new algorithm is extensively tested over the complete set of Bischoff and Ratcliff problems [Bischoff, E. E., M. S. W. Ratcliff. 1995. Issues in the development of approaches to container loading. Omega23 377--390], ranging from weakly heterogeneous to strongly heterogeneous cargo, and outperforms all the known nonparallel approaches that, partially or completely, have used this set of test problems. When comparing against parallel algorithms, it is better on average but not for every class of problem. In terms of efficiency, this approach runs in much less computing time than that required by parallel methods. Thorough computational experiments concerning the evaluation of the impact of algorithm design choices and internal parameters on the overall efficiency of this new approach are also presented.
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Citations
Constraints in container loading – A state-of-the-art review
TL;DR: This work states that container loading problems have been dealt with frequently in the operations research literature and that the proposed approaches are of limited practical value since they do not pay enough attention to constraints encountered in practice.
370
A parallel multi-population biased random-key genetic algorithm for a container loading problem
TL;DR: 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 using a maximal-space representation to manage the free spaces in the container.
164
A Tree Search Algorithm for Solving the Container Loading Problem
Tobias Fanslau,Andreas Bortfeldt +1 more
TL;DR: A tree search algorithm for the three-dimensional container loading problem (3D-CLP), carried out in a special fashion called a partition-controlled tree search, enabling a sufficient search width as well as a suitable degree of foresight.
A comparative review of 3D container loading algorithms
TL;DR: A review of the literature focusing on the solution methodologies employed by researchers, with the aim of providing insight into some of the critical algorithmic design issues, and an extensive comparison of algorithm performance across the benchmark literature.
144
A new Load Balance Methodology for Container Loading Problem in Road Transportation
TL;DR: A multi-population biased random-key genetic algorithm (BRKGA) is proposed, with a new fitness function that takes static stability and load balance into account, thus fulfilling and complying with real-world regulations and legislation.
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References
Greedy Randomized Adaptive Search Procedures.
Mauricio G. C. Resende,Celso C. Ribeiro +1 more
- 01 Jan 2003
TL;DR: Greedy Randomized Adaptive Search Procedures (GRASP) as mentioned in this paper is an iterative randomized sampling technique in which each iteration provides a solution to the problem at hand, and the incumbent solution over all GRASP iterations is kept as the final result.
2.5K
Greedy Randomized Adaptive Search Procedures
TL;DR: This paper defines the various components comprising a GRASP and demonstrates, step by step, how to develop such heuristics for combinatorial optimization problems.
An improved typology of cutting and packing problems
TL;DR: An improved typology of C&P problems is presented, which is partially based on Dyckhoff’s original ideas, but introduces new categorisation criteria, which define problem categories different from those of Dykhoff.
1.5K
Greedy Randomized Adaptive Search Procedures.
Mauricio G. C. Resende
- 01 Jan 2009
TL;DR: This paper defines the various components comprising a GRASP and demonstrates, step by step, how to develop such heuristics for combinatorial optimization problems.
1.3K
A probabilistic heuristic for a computationally difficult set covering problem
TL;DR: An efficient probabilistic set covering heuristic is presented that provides the best known solutions to all other instances attempted to solve set covering problems that arise from Steiner triple systems.
1.1K