Dynamic cascading workshop energy-efficiency scheduling optimization based on deep reinforcement learning
BAO Hai-zhu
PAN Quan-ke
Abstract：This study addresses the dynamic cascading workshop energy-efficiency scheduling problem(DCSESP)that consists of distributed flow shops(DFS),hybrid flow shops(HFS),and the transportation stage between them.The objective is to minimize the total tardiness and total energy consumption.To achieve this,a mixed-integer linear programming(MILP)model is developed,and a graph-based deep reinforcement learning(GDRL)algorithm is proposed.First,a heterogeneous graph model is designed for the DCSESP,and a three-stage node embedding method is introduced to capture the real-time shop floor state features.Second,based on these state features,the algorithm directly selects jobs and operations for both stages of the cascaded dual-shop system.Finally,a multilayer perceptron(MLP)and graph attention network(GAT)are integrated into the proximal policy optimization(PPO)algorithm with an actor-critic framework to facilitate learning and decision-making for rapid joint scheduling.The experimental results demonstrated that the proposed GDRL algorithm outperformed the three state-of-the-art scheduling methods in solving the DCSESP,particularly in complex scheduling scenarios,where it achieved higher optimization performance and robustness.
Keywords：cascading workshop schedulingflow shopdynamic schedulingdeep reinforcement learninggraph neural network
Publication Date：2025-11-30
Online Publishing Date：2025-12-29(First online date of this platform, not the publication date of the document)
Pages：12( 2310-2321 )