Joint Optimization of Rack Retrieval and Repositioning in Robotic Mobile Fulfillment Systems

In robotic mobile fulfillment system (RMFS), the efficient scheduling of multiple automated guided vehicles (AGVs) for rack retrieval and repositioning is a pivotal determinant of overall picking performance. This operation entails a composite decision-making process that involves the joint determination of multi-AGV task set allocation, task sequencing for each AGV, and repositioning strategies for the retrieved racks. As these decision components are tightly coupled, addressing any single subproblem in isolation leads to poor performance. To address this complexity, this paper proposes a hybridization of meta-heuristic algorithm and deep reinforcement learning with temporal-spatial storage constraints (HMDRL-TSC) to jointly optimize these interdependent decisions. Grounded in a bi-level optimization framework, the HMDRL-TSC employs a strategic problem decomposition algorithm. In the upper-level optimization, a general variable neighborhood search (GVNS) algorithm, incorporating generalized travel distance metrics, is utilized to address the task set allocation problem for the multi-AGVs. Subsequently, the lower-level optimization employs deep reinforcement learning (DRL) integrated with a spatiotemporal conflict resolution strategy, which rapidly generates high-quality rack retrieval sequences and eliminates inter-AGV conflicts to ensure solution feasibility. Computational results demonstrate the superior performance of the proposed HMDRL-TSC in solving the rack retrieval and repositioning problem.