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A biased random-key genetic algorithm for the container pre-marshalling problem
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AI
DOI：10.1016/j.cor.2016.05.011.png)
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Abstract
En 中文
The container pre-marshalling problem (CPMP) is performed at container terminals around the world to re-order containers so that they can be more efficiently transferred through the terminal. We introduce a novel decoder for a biased random-key genetic algorithm (BRKGA) that solves the CPMP. The decoder consists of a construction algorithm that learns how to best apply single and compound containers moves to quickly sort a bay of containers. Our approach finds better solutions than the state-of-the-art method on many instances of the standard pre-marshalling benchmarks in less computational time. Furthermore, we perform a computational analysis of different components of the BRKGA decoder to determine what types of heuristics work best for pre-marshalling problems, as well as conduct a feature space analysis of different pre-marshalling approaches. (C) 2016 Elsevier Ltd. All rights reserved.
Keywords:
Container pre-marshalling
Maritime applications
Biased random-key genetic algorithm
Journal
C
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