Journal of Engineering, Project, and Production Management, 2026, 16(1), 2025-122
Optimizing Logistics and Transportation Scheduling for Automated Guided Vehicles Using Discrete Network and Objective Functions
Professor, School of Digital Commerce, Jiangsu Vocational Institute of Commerce, Nanjing 211168, China, E-mail: 17314958873@163.com
Project Management
Received June 5, 2025; revised July 22, 2025; August 25, 2025; accepted September 12, 2025
Available online December 24, 2025
Abstract: The logistics and transportation sector is undergoing automation and intelligent transformation, fueled by the globalization of information and the progress in e-commerce. Leveraging automated equipment to enhance production and transportation efficiency has emerged as a widely acknowledged strategy. To address the persistent problem of inefficient transportation scheduling, this study investigates logistics transportation scheduling using Automated Guided Vehicles (AVG’s) as the primary research object to assess the scheduling problem. Considering the challenges of the AGV resource matching and the complexity of the scheduling environment, an improved discrete network model was introduced and the objective function with the least number of network variation triggers was established. The multi-AGV scheduling conflict was also analyzed to facilitate the optimization of the objective function and the design of the multi-scheduling system. The results indicated that the proposed algorithm outperformed other algorithms in logistics iteration results, with an iteration error margin of greater than 4%. The algorithms also showed superior dynamic adaptability. Scheduling optimized vehicles effectively reduced the conflicts, achieving an average transportation efficiency exceeding 90% with a computation time significantly lower than the pre-improvement levels. The planning and scheduling framework proposed in the study effectively improves distribution efficiency and reduces transportation costs.
Keywords: Discrete network, logistics, scheduling, automated guided vehicle, reachable tree, conflict, objective function.
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Citation: Cai, Y. (2026). Optimizing Logistics and Transportation Scheduling for Automated Guided Vehicles Using Discrete Network and Objective Functions. Journal of Engineering, Project, and Production Management, 16(1), 2025-122.
DOI:
10.32738/JEPPM-2025-122
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