Optimizing Shipbuilding Production Project Scheduling Under Resource Constraints Using Genetic Algorithms and Fuzzy Sets

This study explores the application of Genetic Algorithms (GA) in optimizing shipbuilding production processes in the presence of uncertain environments. The research addresses two key aspects: firstly, the integration of GA RCPSP (Resource-Constrained Project Scheduling Problem) with techniques for managing uncertainty in shipbuilding production; and secondly, the analysis of Pareto optimal solutions generated by GA to achieve optimal scheduling in the shipbuilding context. The proposed framework aims to minimize project completion time and maximize resource utilization by incorporating probabilistic models, scenario analysis to handle uncertainties. Furthermore, the study focuses on evaluating the trade-offs between project completion time, resource allocation, and cost through the analysis of Pareto optimal solutions, using visualization techniques and sensitivity analyses to support decision-making processes. The findings contribute to enhancing shipbuilding production by providing a comprehensive approach for effectively managing uncertainty, improving resource allocation, and reducing project duration through the integration of GA RCPSP and uncertainty management techniques.