Conflict-Free Path Planning and Scheduling for Rgvs in Automated Warehousing Systems

This paper investigates a conflict-free path planning and scheduling problem for Rail-Guided Vehicles (RGVs) in automated warehouse systems. To address the limitations of conventional two-stage approaches that treat task allocation and path planning independently, a novel integrated state-space-time network model is proposed. This model simultaneously incorporates task assignment and path coordination within a unified mixed-integer programming framework, where conflict constraints are explicitly represented through node capacity limitations and arc conflict conditions. To enhance computational scalability for large-scale instances, an Adaptive Large Neighborhood Search (ALNS) algorithm is developed, featuring dynamic destroy-and-repair strategies and adaptive operator weighting. Extensive computational experiments demonstrate that the proposed algorithm consistently delivers superior performance across both small-scale and large-scale scenarios, significantly outperforming classical heuristics and exact optimization methods in terms of solution quality and computational efficiency. Moreover, sensitivity analyses confirm the robustness of the proposed approach under diverse operational conditions and parameter settings.