A Self-Adaptive Evolutionary Algorithm for the Berth Scheduling Problem: Towards Efficient Parameter Control

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ID: 43568
2018
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Abstract
Since ancient times, maritime transportation has played a very important role for the global trade and economy of many countries. The volumes of all major types of cargo, which are transported by vessels, has substantially increased in recent years. Considering a rapid growth of waterborne trade, marine container terminal operators should focus on upgrading the existing terminal infrastructure and improving operations planning. This study aims to assist marine container terminal operators with improving the seaside operations and primarily focuses on the berth scheduling problem. The problem is formulated as a mixed integer linear programming model, minimizing the total weighted vessel turnaround time and the total weighted vessel late departures. A self-adaptive Evolutionary Algorithm is proposed to solve the problem, where the crossover and mutation probabilities are encoded in the chromosomes. Numerical experiments are conducted to evaluate performance of the developed solution algorithm against the alternative Evolutionary Algorithms, which rely on the deterministic parameter control, adaptive parameter control, and parameter tuning strategies, respectively. Results indicate that all the considered solution algorithms demonstrate a relatively low variability in terms of the objective function values at termination from one replication to another and can maintain the adequate population diversity. However, application of the self-adaptive parameter control strategy substantially improves the objective function values at termination without a significant impact on the computational time.
Reference Key
dulebenets2018aalgorithms Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Dulebenets, Maxim A.;Kavoosi, Masoud;Abioye, Olumide;Pasha, Junayed;
Journal algorithms
Year 2018
DOI
DOI not found
URL
http://www.mdpi.com/1999-4893/11/7/100
Keywords
chemistry Microbiology Medicine (General) Chemical technology Biotechnology Other systems of medicine environmental effects of industries and plants renewable energy sources environmental sciences business public aspects of medicine special aspects of education electronic computers. computer science industrial engineering. management engineering

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