Collaborative scheduling of automated guided vehicles and elevator operations for multi-floor hospital material distribution

This study addresses the cross-floor material distribution problem involving the collaboration of automated guided vehicles (AGVs) and a logistics elevator in smart hospital scenarios. A mixed-integer linear programming model is developed for collaborative scheduling with the objective of minimizing the makespan. An improved genetic algorithm (IGA), integrating heuristic initialization, diverse operators, and a double-layer encoding scheme, jointly determines AGV task assignments, distribution paths, and the elevator service sequence. Computational results demonstrate that for small-scale instances, the IGA achieves solution quality comparable to the Gurobi exact solver. For large-scale instances, it maintains high solution quality while providing substantial computational-efficiency advantages over three typical metaheuristic algorithms. A case study using real-world hospital data from Singapore further confirms the superiority of the IGA over traditional scheduling strategies. Sensitivity analysis provides practical management insights into AGV fleet configuration and elevator bottlenecks, further validating the effectiveness of the proposed collaborative scheduling framework.