Scheduling and Optimization in Production and Transportation Systems

Dear Colleagues,

Production systems and transportation systems are inherently complex due to various complex factors such as various dynamic disruptions, capacity limitations, and large-scale problem sizes [1]. Scheduling plays a crucial role in both production and transportation systems, ensuring efficiency, cost-effectiveness, and timely delivery of goods and services [2]. Obtaining high-quality schedules depends on mathematical optimization. However, these complex factors make mathematical modeling and obtaining optimal solutions highly challenging, often necessitating a trade-off between computational performance and solution quality.

Scheduling and optimization are crucial components in this new industrial paradigm. The integration of advanced information technologies in both production and transportation systems is revolutionizing these systems [3]. By leveraging real-time data, advanced analytics, and automation, businesses can achieve unprecedented levels of efficiency, agility, and sustainability [4]. They involve the strategic allocation of resources and the sequencing of tasks to maximize efficiency, reduce costs, and improve overall system performance. Therefore, the comprehensive application of various optimization methodologies holds enormous potential for addressing challenges in production and transportation systems [5,6].

This special issue aims to gather high-quality research that addresses the latest advancements, challenges, and solutions in the field of scheduling and optimization within production systems and transportation systems. The theoretical research, case study, and literature review associated with this special issue are warmly welcomed.

Topics of interest include, but are not limited to:

• Advanced scheduling algorithms for manufacturing systems
• Optimization techniques for supply chain management
• Real-time scheduling and optimization in transportation networks
• Integration of production and transportation scheduling
• Heuristic and metaheuristic approaches for complex scheduling problems
• Machine learning applications in scheduling and optimization
• Case studies and practical implementations in industrial settings
• Sustainable and green logistics optimization
• Multi-objective optimization in production and transportation
• Disruption management and resilience in scheduling

We look forward to receiving your valuable contributions to this special issue, which aims to make a significant impact on the optimization field of production systems and transportation systems.

References:

1. Lei, K.; Guo, P.; Wang, Y.; Zhang, J.; Meng, X.; Qian, L.; Large-Scale Dynamic Scheduling for Flexible Job-Shop With Random Arrivals of New Jobs by Hierarchical Reinforcement Learning. IEEE Transactions on Industrial Informatics 2024, 20(1), 1007–1018.
2. Huang, M.; Huang, S.; Du, B.; Guo, J.; Li, Y.; Fuzzy Superposition Operation and Knowledge-driven Co-evolutionary Algorithm for Integrated Production Scheduling and Vehicle Routing Problem with Soft Time Windows and Fuzzy Travel Times. IEEE Transactions on Fuzzy Systems 2024.
3. Huang, J.; Huang, S.; Moghaddam, S.K.; Lu, Y.; Wang, G.; Yan, Y.; Deep Reinforcement Learning-Based Dynamic Reconfiguration Planning for Digital Twin-Driven Smart Manufacturing Systems With Reconfigurable Machine Tools. IEEE Transactions on Industrial Informatics 2024, 20(11), 13135–13146.
4. Guo, P.; Shi, H.; Wang, Y.; Xiong. J.; Multi-Objective Scheduling of Cloud-Edge Cooperation in Distributed Manufacturing via Multi-Agent Deep Reinforcement Learning. International Journal of Production Research 2024, 1–25.
5. Lei, K.; Guo, P.; Zhao, W.; Wang, Y.; Qian, L.; Meng, X.; Tang, L.; A multi-action deep reinforcement learning framework for flexible Job-shop scheduling problem. Expert Systems with Applications 2022, 205(1), 117796.
6. Zhu, Q.; Huang, S.; Wang, G.; Moghaddam, S.K.; Lu, Y.; Yan, Y.; Dynamic reconfiguration optimization of intelligent manufacturing system with human-robot collaboration based on digital twin. Journal of Manufacturing Systems 2022, 65, 330–338.

Dr. Peng Guo
Dr. Sihan Huang
Guest Editors

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• scheduling optimization
• production systems
• transportation systems
• advanced scheduling algorithms
• real-time data analytics
• heuristic and metaheuristic methods
• supply chain optimization
• machine learning in scheduling
• resilience in scheduling