Eldemir, FahrettinTaner, Mustafa Egemen2025-03-172025-03-1720252077-1312https://doi.org/10.3390/jmse13020371https://hdl.handle.net/20.500.13099/1579The 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.eninfo:eu-repo/semantics/openAccessport managementmaritime transportcontainer terminalsquay crane schedulingmeta-heuristicsGRASP-GAant colony optimizationdecision support systemArticle10.3390/jmse13020371132Q1WOS:0014299232000012-s2.0-85218862902Q2