Research on Ship Crew Scheduling Algorithm Based on Improved Grey Wolf Optimization

Crew scheduling in civilian ships is a combinatorial optimization problem with various constraints. Traditional methods struggle with large-scale scheduling, while classic algorithms often fail to balance performance and practicality. This paper presents an Improved Grey Wolf Optimization (IGWO) algorithm. We establish a mixed-integer programming model that considers hard and soft constraints to maximize crew-position matching. The IGWO algorithm enhances the standard Grey Wolf Optimization (GWO) framework by integrating strategies like heuristic initialization, adaptive encirclement search, elite leader selection, and constraint violation repair. Simulation on random and real-world datasets show that IGWO outperforms manual methods, rule-based heuristics, and the standard GWO algorithm in objective function value, constraint satisfaction, and employee satisfaction. This research advances heuristic optimization in combinatorial problems.