TY - JOUR
T1 - Towards greater resilience
T2 - A systematic review of dynamic shop floor scheduling in industry 5.0
AU - Zheng, Liang
AU - Cai, Yunchen
AU - Gao, Qinglin
AU - Liu, Jianhua
AU - Zhuang, Cunbo
N1 - Publisher Copyright:
© 2025 Elsevier Ltd.
PY - 2026/4
Y1 - 2026/4
N2 - Within the Industry 5.0 framework emphasizing human considerations, sustainability, and resilience, dynamic scheduling proves pivotal for enhancing production system resilience. By enabling manufacturers to sustain flexibility and efficiency amid disruptions, it has stimulated significant academic investigation and industrial adoption. Capitalizing on this momentum, this review surveys recent advances in dynamic job shop scheduling, providing a comprehensive overview of modeling approaches, classifications of objective functions, and typical types of dynamic events to clarify the theoretical foundations of the field. Recent algorithmic developments, including traditional methods, metaheuristics, hyper-heuristics, reinforcement learning, and other emerging paradigms, are comprehensively analyzed and compared in terms of their strengths and limitations. Various scheduling strategies, including reactive, proactive, and hybrid strategies (predictive-reactive and proactive–reactive), are discussed with respect to their application scenarios and distinguishing features. Furthermore, enabling technologies such as digital twins and edge computing that enhance scheduling resilience are evaluated, and a novel conceptual framework for resilient dynamic scheduling is proposed. The review concludes by identifying current challenges and outlining promising research directions, providing actionable insights for both academic research and industrial practice.
AB - Within the Industry 5.0 framework emphasizing human considerations, sustainability, and resilience, dynamic scheduling proves pivotal for enhancing production system resilience. By enabling manufacturers to sustain flexibility and efficiency amid disruptions, it has stimulated significant academic investigation and industrial adoption. Capitalizing on this momentum, this review surveys recent advances in dynamic job shop scheduling, providing a comprehensive overview of modeling approaches, classifications of objective functions, and typical types of dynamic events to clarify the theoretical foundations of the field. Recent algorithmic developments, including traditional methods, metaheuristics, hyper-heuristics, reinforcement learning, and other emerging paradigms, are comprehensively analyzed and compared in terms of their strengths and limitations. Various scheduling strategies, including reactive, proactive, and hybrid strategies (predictive-reactive and proactive–reactive), are discussed with respect to their application scenarios and distinguishing features. Furthermore, enabling technologies such as digital twins and edge computing that enhance scheduling resilience are evaluated, and a novel conceptual framework for resilient dynamic scheduling is proposed. The review concludes by identifying current challenges and outlining promising research directions, providing actionable insights for both academic research and industrial practice.
KW - Dynamic scheduling
KW - Flexible job shop scheduling
KW - Industry 5.0
KW - Resilience
KW - Scheduling algorithm
KW - Scheduling strategy
UR - https://www.scopus.com/pages/publications/105025134782
U2 - 10.1016/j.aei.2025.104241
DO - 10.1016/j.aei.2025.104241
M3 - Review article
AN - SCOPUS:105025134782
SN - 1474-0346
VL - 71
JO - Advanced Engineering Informatics
JF - Advanced Engineering Informatics
M1 - 104241
ER -