Research on the Energy Efficiency Optimization Scheduling Method of Multi-Load Trolleys in Workshops

Abstract

With the shift of China’s industrial structure toward green and intelligent manufacturing, energy-efficient and flexible production scheduling has become an essential research focus. In response to increasing complexity in mixed-flow assembly environments—such as the diversification of products, compressed delivery cycles, and growing energy consumption—this study reviews recent developments in the scheduling optimization of workshop material handling systems, especially those involving multi-load AGVs. The literature reveals a clear evolution from static scheduling models based on fixed plans to dynamic, real-time strategies that address uncertainties in production rhythms and resource availability. In particular, energy-aware scheduling has gained prominence, emphasizing the need to jointly optimize delivery timeliness, path planning, and energy efficiency. Deep reinforcement learning (DRL) has emerged as a promising solution, offering strong capabilities in modeling high-dimensional states, capturing temporal dependencies, and making adaptive decisions. However, challenges remain in sparse rewards, action feasibility, and decision interpretability. This review highlights the need for DRL frameworks that incorporate energy consumption modeling, action masking, and semantic state encoding to achieve robust, efficient, and sustainable AGV scheduling under dynamic manufacturing scenarios.