A Hybrid GA with Variable Quay Crane Assignment for Solving Berth Allocation Problem and Quay Crane Assignment Problem Simultaneously
| Autor: | Chien-Chang Chou, Chia-Nan Wang, Hsien-Pin Hsu, Tai-Lin Chiang, Hsin-Pin Fu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Mathematical optimization
Computer science Quay crane Geography Planning and Development Crossover TJ807-830 02 engineering and technology Management Monitoring Policy and Law TD194-195 Renewable energy sources quay crane assignment problem (QCAP) Operator (computer programming) 0502 economics and business Genetic algorithm 0202 electrical engineering electronic engineering information engineering GE1-350 050210 logistics & transportation Environmental effects of industries and plants Renewable Energy Sustainability and the Environment 05 social sciences hybrid genetic algorithm (HGA) Environmental sciences Variable (computer science) Berth allocation problem Container (abstract data type) berth allocation problem (BAP) 020201 artificial intelligence & image processing variable QC assignment Assignment problem |
| Zdroj: | Sustainability, Vol 11, Iss 7, p 2018 (2019) Sustainability Volume 11 Issue 7 |
| ISSN: | 2071-1050 |
| Popis: | Container terminals help countries to sustain their economic development. Improving the operational efficiency in a container terminal is important. In past research, genetic algorithms (GAs) have been widely used to cope with seaside operational problems, including the berth allocation problem (BAP) and quay crane assignment problem (QCAP) individually or simultaneously. However, most GA approaches in past studies were dedicated to generate time-invariant QC assignment that does not adjust QCs assigned to a ship. This may underutilize available QC capacity. In this research, three hybrid GAs (HGAs) have been proposed to deal with the dynamic and discrete BAP (DDBAP) and the dynamic QCAP (DQCAP) simultaneously. The three HGAs supports variable QC assignment in which QCs assigned to a ship can be further adjusted. The three HGAs employ the same crossover operator but a different mutation operator and a two-stage procedure is used. In the first stage, these HGAs can generate a BAP solution and a QCAP solution that is time-invariant. The time-invariant QC assignment solution is then further transformed into a variable one in the second stage. Experiments have been conducted to investigate the effects of the three HGA and the results showed that these HGAs outperformed traditional GAs in terms of fitness value. In particular, the HGA3 with Thoros mutation operator had the best performance. |
| Databáze: | OpenAIRE |
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