A Self-Adaptive Evolutionary Algorithm for the Berth Scheduling Problem: Towards Efficient Parameter Control
Clicks: 391
ID: 43568
2018
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.
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dulebenets2018aalgorithms
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| Authors | Dulebenets, Maxim A.;Kavoosi, Masoud;Abioye, Olumide;Pasha, Junayed; |
| Journal | algorithms |
| Year | 2018 |
| DOI | DOI not found |
| URL | |
| 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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