Minimizing quay crane downtime in container terminals using genetic algorithms with a case study of Tangier MED Port.
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| Title: | Minimizing quay crane downtime in container terminals using genetic algorithms with a case study of Tangier MED Port. |
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| Authors: | Garmouch H; ISISA Team, Faculty of Science, Abdelmalek Essaadi University Tetouan, Tetouan, Morocco. hamza.garmouch@etu.uae.ac.ma., Abdoun O; ISISA Team, Faculty of Science, Abdelmalek Essaadi University Tetouan, Tetouan, Morocco., Garmouch O; ISISA Team, Faculty of Science, Abdelmalek Essaadi University Tetouan, Tetouan, Morocco.; SMPNT, Faculty of Science, Abdelmaled Essaadi University, Tetouan, Morocco. |
| Source: | Scientific reports [Sci Rep] 2025 Nov 23. Date of Electronic Publication: 2025 Nov 23. |
| Publication Model: | Ahead of Print |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: London : Nature Publishing Group, copyright 2011- |
| Abstract: | The problem of quay crane downtime continues to challenge container terminals across the globe, particularly in fully and partially automated ones like Tangier MED Port in Morocco. The persistent breakdowns of the cranes increase vessel turnaround times, delay completion times, add to the cost burden, diminish terminal productivity. This study formulates a genetic algorithm (GA) model aimed at reducing quay crane downtime due to planned maintenance, unplanned failures, equipment idle time coordination, and job scheduling performed within a single optimization framework. Unlike previous methods that address individual aspects in isolation, our approach models all crane operations to optimally reduce idle time and disruptions. The simulation results substantiate the claims that the proposed GA significantly improves crane and terminal efficiency. Also, analysis done in comparison to PSO and SA showed that GA is capable of providing better and more scalable solutions in modern container terminals. Specifically, GA achieved an average downtime of 98.3 min, compared to 99.5 min for PSO and 197.8 min for SA, confirming its superior performance. (© 2025. The Author(s).) |
| Competing Interests: | Declarations. Competing interests: The authors declare no competing interests. |
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| Contributed Indexing: | Keywords: Automated container terminals (ACT); Downtime; Genetic algorithm (GA); Metaheuristic methods; Particle swarm optimization (PSO); Simulated annealing (SA) |
| Entry Date(s): | Date Created: 20251122 Latest Revision: 20251122 |
| Update Code: | 20251123 |
| DOI: | 10.1038/s41598-025-29190-0 |
| PMID: | 41274993 |
| Database: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstract: | The problem of quay crane downtime continues to challenge container terminals across the globe, particularly in fully and partially automated ones like Tangier MED Port in Morocco. The persistent breakdowns of the cranes increase vessel turnaround times, delay completion times, add to the cost burden, diminish terminal productivity. This study formulates a genetic algorithm (GA) model aimed at reducing quay crane downtime due to planned maintenance, unplanned failures, equipment idle time coordination, and job scheduling performed within a single optimization framework. Unlike previous methods that address individual aspects in isolation, our approach models all crane operations to optimally reduce idle time and disruptions. The simulation results substantiate the claims that the proposed GA significantly improves crane and terminal efficiency. Also, analysis done in comparison to PSO and SA showed that GA is capable of providing better and more scalable solutions in modern container terminals. Specifically, GA achieved an average downtime of 98.3 min, compared to 99.5 min for PSO and 197.8 min for SA, confirming its superior performance.<br /> (© 2025. The Author(s).) |
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| ISSN: | 2045-2322 |
| DOI: | 10.1038/s41598-025-29190-0 |