Evolutionary algorithm incorporating reinforcement learning for energy-conscious flexible job-shop scheduling problem with transportation and setup times

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 133, С. 107974 - 107974

Опубликована: Фев. 13, 2024

Язык: Английский

---

Manufacturing resource-based self-organizing scheduling using multi-agent system and deep reinforcement learning

Journal of Manufacturing Systems, Год журнала: 2025, Номер 79, С. 179 - 198

Опубликована: Янв. 24, 2025

Язык: Английский

---

A reinforcement learning-based hyper-heuristic for AGV task assignment and route planning in parts-to-picker warehouses

Transportation Research Part E Logistics and Transportation Review, Год журнала: 2024, Номер 185, С. 103518 - 103518

Опубликована: Апрель 4, 2024

Язык: Английский

---

Dynamic Job-Shop Scheduling via Graph Attention Networks and Deep Reinforcement Learning

IEEE Transactions on Industrial Informatics, Год журнала: 2024, Номер 20(6), С. 8662 - 8672

Опубликована: Март 21, 2024

The dynamic job-shop scheduling problem (DJSSP) is an advanced form of the classical (JSSP), incorporating events that make it even more challenging. This article proposes a novel approach involving deep reinforcement learning and graph neural networks to solve this optimization problem. To effectively model DJSSP, we use disjunctive graph, designing specific node features reflect unique characteristics JSSP with machine breakdowns stochastic job arrivals. Our proposed method can dynamically adapt occurrence disruptions, ensuring accurately reflects current state environment. Furthermore, attention mechanism prioritize crucial nodes while discarding irrelevant ones. study applies learn embeddings, serving as input for actor–critic model. proximal policy then utilized train model, which assists in operations machines. We conducted extensive experiments static public environments. Experimental results indicate our superior state-of-the-art methods.

Язык: Английский

---

Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions

Computers & Industrial Engineering, Год журнала: 2025, Номер unknown, С. 110856 - 110856

Опубликована: Янв. 1, 2025

Язык: Английский

---

Digital twin-driven dynamic scheduling for the assembly workshop of complex products with workers allocation

Robotics and Computer-Integrated Manufacturing, Год журнала: 2024, Номер 89, С. 102786 - 102786

Опубликована: Май 25, 2024

Язык: Английский

---

Fast Pareto set approximation for multi-objective flexible job shop scheduling via parallel preference-conditioned graph reinforcement learning

Swarm and Evolutionary Computation, Год журнала: 2024, Номер 88, С. 101605 - 101605

Опубликована: Май 28, 2024

Язык: Английский

---

Ship pipe production optimization method for solving distributed heterogeneous energy-efficient flexible flowshop scheduling with mobile resource limitation

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 126603 - 126603

Опубликована: Янв. 1, 2025

Язык: Английский

---

An integrated framework of preventive maintenance and task scheduling for repairable multi-unit systems

Reliability Engineering & System Safety, Год журнала: 2024, Номер 247, С. 110129 - 110129

Опубликована: Апрель 4, 2024

Язык: Английский

---

Joint optimization of job scheduling, condition-based maintenance planning, and spare parts ordering for degrading production systems

Reliability Engineering & System Safety, Год журнала: 2024, Номер 252, С. 110447 - 110447

Опубликована: Авг. 16, 2024

Язык: Английский

---