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<p>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.</p>

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Minimizing quay crane downtime in container terminals using genetic algorithms with a case study of Tangier MED Port

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月23日

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  • Hamza Garmouch,
  • Otman Abdoun,
  • Oumaima Garmouch

摘要

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.