A Hybrid Meta-Heuristic Approach for Solving Single-Vessel Quay Crane Scheduling with Double-Cycling
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| Yayımlandı: | Journal of Marine Science and Engineering vol. 13, no. 2 (2025), p. 371 |
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| Yazar: | |
| Diğer Yazarlar: | |
| Baskı/Yayın Bilgisi: |
MDPI AG
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| Konular: | |
| Online Erişim: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3171121312 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2077-1312 | ||
| 024 | 7 | |a 10.3390/jmse13020371 |2 doi | |
| 035 | |a 3171121312 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231479 |2 nlm | ||
| 100 | 1 | |a Eldemir, Fahrettin |u Department of Industrial and Systems Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia | |
| 245 | 1 | |a A Hybrid Meta-Heuristic Approach for Solving Single-Vessel Quay Crane Scheduling with Double-Cycling | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The escalating global demand for containerized cargo has intensified pressure on container terminals, which serve as vital nodes in maritime logistics. This study aims to enhance operational efficiency in non-automated container terminals by examining two meta-heuristic approaches—Ant Colony Optimization (ACO) and a hybrid Greedy Randomized Adaptive Search Procedure (GRASP)—Genetic Algorithm (GA)—for quay crane scheduling. Their performance is benchmarked across various problem scales, with process completion time serving as the primary metric. Based on these findings, the most effective approach is integrated into a newly developed Decision Support System (DSS) to streamline practical implementation. Statistical analyses confirm the robustness of both methods, underscoring how meta-heuristics combined with a DSS can optimize quay crane utilization, bolster maritime logistics, and ultimately boost terminal productivity. | |
| 653 | |a Quays | ||
| 653 | |a Containers | ||
| 653 | |a Integer programming | ||
| 653 | |a Ports | ||
| 653 | |a Cranes | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Decision support systems | ||
| 653 | |a Automation | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Transport buildings, stations and terminals | ||
| 653 | |a Ant colony optimization | ||
| 653 | |a Heuristic | ||
| 653 | |a Heuristic methods | ||
| 653 | |a Efficiency | ||
| 653 | |a Scheduling | ||
| 653 | |a Simulation | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Decision making | ||
| 653 | |a Problem solving | ||
| 653 | |a Algorithms | ||
| 653 | |a Logistics | ||
| 653 | |a Statistical methods | ||
| 653 | |a Completion time | ||
| 653 | |a Adaptive search techniques | ||
| 653 | |a Cranes & hoists | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Taner, Mustafa Egemen |u Department of Industrial Engineering, Tarsus University, Mersin 33402, Turkey; <email>metaner@tarsus.edu.tr</email> | |
| 773 | 0 | |t Journal of Marine Science and Engineering |g vol. 13, no. 2 (2025), p. 371 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3171121312/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3171121312/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3171121312/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |