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| Format: | Artículo científico |
| Language: | en |
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Asociación Española para la Inteligencia Artificial
2015
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| Online Access: | https://www.redalyc.org/articulo.oa?id=92538718001 |
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Table of Contents:
- Decentralized Cooperative Metaheuristic for the Dynamic Berth Allocation Problem Eduardo Lalla-Ruiz Christopher Expósito-Izquierdo Belén Melián-Batista J. Marcos Moreno-Vega Ingeniería Berth Allocation Problem Maritime Container Terminal Decentralized Cooperative Metaheuristic The increasing demand of maritime transport and the great competition among port terminals force theirmanagers to reduce costs by exploiting its resources accurately. In this environment, the Berth Allocation Problem,which aims to allocate and schedule incoming vessels along the quay, plays a relevant role in improving the overallterminal productivity. In order to address this problem, we propose Decentralized Cooperative Metaheuristic(DCM), which is a population-based approach that exploits the concepts of communication and grouping. InDCM, the individuals are organized into groups, where each individual shares information with its group partners.This grouping strategy allows to diversify as well as intensify the search in some regions by means of informationshared among the individuals of each group. Moreover, the constrained relation for sharing information amongindividuals through the proposed grouping strategy allows to reduce computational resources in comparison tothe `all to all' communication strategy. The computational experiments for this problem reveal that DCM reportshigh-quality solutions and identies promising regions within the search space in short computational times. 2015 artículo científico 1137-3601 https://www.redalyc.org/articulo.oa?id=92538718001 en http://www.redalyc.org/revista.oa?id=925 Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial application/pdf Asociación Española para la Inteligencia Artificial Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial (España) Num.55 Vol.18