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Intelligent Sensing and Decision-Making in Advanced Manufacturing: 2nd Edition

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Special Issue Editors

Special Issue Information

Dear Colleagues,

This publication is a continuation of our previous Special Issue on the same topic, entitled “Intelligent Sensing and Decision-Making in Advanced Manufacturing”.

Recently, with the rapid development of advanced network technologies (e.g., 5G) and artificial intelligence technologies (e.g., deep neural networks), advanced manufacturing (AM) systems are being applied more often, and they are becoming increasingly digitalized, networked, and intelligent. Sensing and decision-making techniques, as the fundamental elements in intelligent manufacturing, are highly significant and have been widely adopted in this process of change. This Special Issue therefore aims to publish original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the field of intelligent sensing and decision-making for AM systems. Potential topics include but are not limited to the following:

Smart sensors for manufacturing devices;
Intrusive sensors and non-intrusive sensors;
AI-based sensing technologies;
Intelligent sensors on industrial robots;
Dynamic decision-making methods for AM;
Uncertainty-oriented methods in AM;
Modeling and simulation for AM;
Planning and scheduling for AM;
Human–robot collaborative planning in AM;
Motion planning and control of industrial robotics;
Supply chain management and logistics in AM.

Dr. Longfei Zhou
Dr. Pai Zheng
Prof. Dr. Xinyu Li
Guest Editors

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Keywords

smart sensors for manufacturing devices
intrusive sensors and non-intrusive sensors
AI-based sensing technologies
intelligent sensors on industrial robots
dynamic decision-making methods for AM
uncertainty-oriented methods in AM
modeling and simulation for AM
planning and scheduling for AM
human–robot collaborative planning in AM
motion planning and control of industrial robotics
supply chain management and logistics in AM

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26 pages, 2510 KB

Open AccessArticle

GA-HPO PPO: A Hybrid Algorithm for Dynamic Flexible Job Shop Scheduling

by Yiming Zhou, Jun Jiang, Qining Shi, Maojie Fu, Yi Zhang, Yihao Chen and Longfei Zhou

Sensors 2025, 25(21), 6736; https://doi.org/10.3390/s25216736 - 4 Nov 2025

Cited by 1 | Viewed by 859

Abstract

The Job Shop Scheduling Problem (JSP), a classical NP-hard challenge, has given rise to various complex extensions to accommodate modern manufacturing requirements. Among them, the Dynamic Flexible Job Shop Scheduling Problem (DFJSP) remains particularly challenging, due to its stochastic task arrivals, heterogeneous deadlines, and varied task types. Traditional optimization- and rule-based approaches often fail to capture these dynamics effectively. To address this gap, this study proposes a hybrid algorithm, GA-HPO PPO, tailored for the DFJSP. The method integrates genetic-algorithm–based hyperparameter optimization with proximal policy optimization to enhance learning efficiency and scheduling performance. The algorithm was trained on four datasets and evaluated on ten benchmark datasets widely adopted in DFJSP research. Comparative experiments against Double Deep Q-Network (DDQN), standard PPO, and rule-based heuristics demonstrated that GA-HPO PPO consistently achieved superior performance. Specifically, it reduced the number of overdue tasks by an average of 18.5 in 100-task scenarios and 197 in 1000-task scenarios, while maintaining a machine utilization above 67% and 28% in these respective scenarios, and limiting the makespan to within 108–114 and 506–510 time units. The model also demonstrated a 25% faster convergence rate and 30% lower variance in performance across unseen scheduling instances compared to standard PPO, confirming its robustness and generalization capability across diverse scheduling conditions. These results indicate that GA-HPO PPO provides an effective and scalable solution for the DFJSP, contributing to improved dynamic scheduling optimization in practical manufacturing environments. Full article

(This article belongs to the Special Issue Intelligent Sensing and Decision-Making in Advanced Manufacturing: 2nd Edition)

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