Scheduling of container-handling equipment during the loading process at an automated container terminal

•Integration of scheduling all container handling equipment in loading process is studied.•Optimal solutions are obtained for small-sized problems.•An efficient adaptive GA is designed for large-sized problems.•Efficiency analysis is carried out for large-sized problems.•Single cycle and dual cycle...

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Veröffentlicht in:Computers & industrial engineering Jg. 149; S. 106848
Hauptverfasser: Luo, Jiabin, Wu, Yue
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.11.2020
Schlagworte:
Adaptive GA
Automated container terminal
Container handling equipment
Container loading
Mixed integer programming
Mixed integer programming
Automated container terminal
Adaptive GA
Container loading
Container handling equipment
ISSN:0360-8352, 1879-0550
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Zusammenfassung:•Integration of scheduling all container handling equipment in loading process is studied.•Optimal solutions are obtained for small-sized problems.•An efficient adaptive GA is designed for large-sized problems.•Efficiency analysis is carried out for large-sized problems.•Single cycle and dual cycle operations are compared in terms of computational efficiency. To improve the operational efficiency of container terminals, it is important to consider the coordination of different types of container-handling equipment, which typically include vehicles, yard cranes and quay cranes. This paper addresses the integration of scheduling each constituent of handling equipment in an automated container terminal, in order to minimise the loading element of the ship’s berthing time. A mixed-integer programming (MIP) model was developed to mathematically formulate this challenge. Small-sized problems can be solved optimally using existing solver. In order to obtain approximately optimal solutions for large-sized problems, an adaptive heuristic algorithm was created that can adjust the parameters of a genetic algorithm (GA), according to the observed performance. Experiments were carried out for both small-sized and large-sized problems to analyse the impact of equipment used in the loading process on berthing and computation times, as well as to test the efficiency of our proposed adaptive GA in solving this integrated problem.
ISSN:0360-8352
1879-0550
DOI:10.1016/j.cie.2020.106848