Energy-Efficient Scheduling of Multi-Load AGVs Based on the SARSA-TTAO Algorithm

Hongtao Tang (), Hanyue Wang, Yan Zhan and Xuesong Xu
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Hongtao Tang: College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310000, China
Hanyue Wang: College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310000, China
Yan Zhan: College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310000, China
Xuesong Xu: College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310000, China

Sustainability, 2025, vol. 17, issue 16, 1-22

Abstract: The Multi-load Automated Guided Vehicle (M-AGV) has emerged as a key enabling technology for intelligent and sustainable workshop logistics owing to its potential to enhance transportation efficiency and reduce system costs. To address the limitations in energy optimization caused by simplified AGV speed and payload modeling in existing scheduling models, this study develops a multi-factor coupled energy consumption model—integrating vehicle speed, travel distance, and dynamic payload—to minimize the total energy consumption of M-AGV systems. To effectively solve the model, a hybrid optimization algorithm that combines the State–Action–Reward–State–Action (SARSA) learning algorithm with the Triangulation Topology Aggregation Optimizer (TTAO), complemented by a similarity-based individual generation strategy, is designed to jointly enhance the algorithm’s exploration and exploitation capabilities. Comparative experiments were conducted across task scenarios involving three different handling task scales and three levels of M-AGV fleet heterogeneity, demonstrating that the proposed SARSA-TTAO algorithm outperforms Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the Hybrid Genetic Algorithm with Large Neighborhood Search (GA-LNS) in terms of solution accuracy and convergence performance. The study also reveals the differences between homogeneous and heterogeneous M-AGV fleets in task allocation and resource utilization under energy-optimal conditions.

Keywords: multi-load automated guided vehicle; energy-efficient scheduling; multi-factor energy consumption modeling; SARSA; TTAO (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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