A Bi-Level Programming Approach for Coordinated Task Sequencing and Collision-Free Path Planning in Robotic Mobile Fulfillment Systems

Peipei Ding, Shi Qiang Liu (), Sai-Ho Chung, Mahmoud Masoud and Qiang Zhang
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Peipei Ding: School of Economics and Management, Fuzhou University, Fuzhou 350108, China
Shi Qiang Liu: School of Economics and Management, Fuzhou University, Fuzhou 350108, China
Sai-Ho Chung: Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong
Mahmoud Masoud: Department of Information Systems and Operations Management, Business School, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
Qiang Zhang: School of Economics and Management, Anqing Normal University, Anqing 246011, China

Mathematics, 2025, vol. 13, issue 23, 1-31

Abstract: In Robotic Mobile Fulfillment Systems (RMFS), the tight coupling between Task Allocation and Sequencing (TAS) and Conflict-free Path Planning (CPP) poses substantial complexities for operational-level coordination. This paper presents a Bi-Level Programming (BLP) model that jointly captures the interdependent decisions of TAS and CPP. The upper level allocates tasks to Automated Guided Vehicles (AGV) to improve the efficiency and balance local workload, while the lower level generates dynamically collision-free routes that respect real-world movement constraints. To efficiently solve this complicated BLP model, we develop a hybrid metaheuristic algorithm (GA-A*-CP) that integrates a Genetic Algorithm (GA), an improved A* algorithm and a collision-avoidance prediction (CP) mechanism into a unified framework. A key feature of the proposed approach is its iterative closed-loop optimization structure, where TAS decisions guide the generation of CPP results, while the resulting execution feedback capturing spatial constraints and agent interactions is recursively used to refine TAS decisions. This bidirectional coupling enables the RMFS to adapt dynamically congestion and coordination complexity for enhancing operational interaction and coordination. Extensive computational experiments under varying task intensities and AGV configurations show that the proposed BLP approach consistently achieves lower execution costs and better responsiveness in comparison to conventional decoupled approaches. These results show that integrating data-driven feedback across decision layers enables the system to dynamically adapt its planning and allocation strategies in response to execution results. The proposed BLP approach advances the design of a more responsive and structurally coherent architecture for multi-agent logistics systems.

Keywords: robotic mobile fulfillment systems (RMFS); bi-level programming; task allocation and sequencing; conflict-free path planning; genetic algorithm; A* algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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