Integrated task assignment and path planning for multi-type robots in an intelligent warehouse system

Zihan Qiu, Jiancheng Long, Yang Yu and Shukai Chen

Transportation Research Part E: Logistics and Transportation Review, 2025, vol. 194, issue C

Abstract: This paper considers an intelligent warehouse system (IWS) that requires the seamless cooperation of three types of mobile robots: automated guided vehicles (AGVs), rail-guided vehicles (RGVs), and gantry lifting devices (GLDs). Compared to the conventional system, which comprises AGVs, the IWS is more flexible in addressing with the customized demands of diverse enterprises. This paper proposes an integrated task assignment and path planning problem for multi-type robots (e.g., AGVs, RGVs, and GLDs) in IWS. The cooperative constraints between AGVs and GLDs, RGVs and GLDs, as well as the conflict-free constraints among AGVs, are considered. It is challenging to solve the multi-type robots scheduling problem with the conflict-free constraints of AGVs because these constraints can result in the unfixed task completion time of AGVs and pose computational challenges of the task assignment for AGVs, RGVs, and GLDs. The proposed integrated task assignment and path planning problem for multi-type robots is modeled as a multi-commodity flow problem on a novel state-time–space network and is formulated as an integer linear programming (ILP) model, where the warehouse operator aims to minimize the total completion time of all tasks. We developed a Lagrangian relaxation heuristic with a customized efficient strategy to find feasible solutions. We also solved our proposed model using CPLEX. The tailored Lagrangian relaxation heuristic was tested on simulated and real instances provided by a manufacturing company. The results show that the proposed heuristic outperforms the baseline algorithm. Sensitivity analyses from the numerical experiments are discussed, which can help the company improve the efficiency of the IWS.

Keywords: AGV; RGV; Warehouse operation management; Lagrangian relaxtion; State-time–space network (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.tre.2024.103883

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