Abstract
This paper studies an optimized container loading problem with the goal of maximizing the 3D space utilization. Based on the characteristics of the mathematical loading model, we develop a dedicated placement heuristic integrated with a novel dynamic space division method, which enables the design of the adaptive genetic algorithm in order to maximize the loading space utilization. We use both weakly and strongly heterogeneous loading data to test the proposed algorithm. By choosing 15 classic sets of test data given by Loh and Nee as weakly heterogeneous data, the average space utilization of our algorithm reaching 70.62% outperforms those of 13 algorithms from the related literature. Taking a set of test data given by George and Robinson as strongly heterogeneous data, the space utilization in this paper can be improved by 4.42% in comparison with their heuristic algorithm.
Highlights
Container loading problems mainly address the issues of planning the loading order and loading position on the basis of ensuring certain constraints [1,2,3]
Based on the characteristics of the mathematical loading model, we develop a dedicated placement heuristic integrated with a novel dynamic space division method, which enables the design of the adaptive genetic algorithm in order to maximize the loading space utilization
The container loading problem is a Nondeterministic Polynomial- (NP-) Hard problem that focuses on establishing mathematical models and seeking efficient algorithms depending on the specific environments [7, 8]
Summary
Our paper studies two categories of loading a single container with selections from either weakly or strongly heterogeneous set of cargoes such that the value of the loaded items is maximized. Intelligent optimized algorithms, such as simulated annealing, tabu search algorithm, and genetic algorithm, have been proposed in literature, in order to compute the optimal solution of the complex NP-hard problem including the practical case of container loading [10, 11]. The basic heuristic has a high practical value for the optimization of the packing problem. The improved heuristic is a hybrid algorithm which combines basic heuristic with neighborhood search algorithm, such as genetic algorithm, tabu search algorithm, and greedy algorithm [12,13,14]
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