An enhanced memetic algorithm for energy-efficient and low-carbon flexible job shop scheduling problem considering machine restart

Journal of Manufacturing Systems, Journal Year: 2025, Volume and Issue: 80, P. 457 - 478

Published: April 3, 2025

Language: Английский

---

Hybrid Gaussian quantum particle swarm optimization and adaptive genetic algorithm for flexible job-shop scheduling problem

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 154, P. 110882 - 110882

Published: April 29, 2025

Language: Английский

---

Total slack transmission graph-based robust scheduling for flexible job shop scheduling under machine breakdowns

Journal of Manufacturing Systems, Journal Year: 2025, Volume and Issue: 80, P. 963 - 975

Published: May 4, 2025

Language: Английский

---

Enhanced artificial ecosystem-based optimization for global optimization and constrained engineering problems

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(7), P. 10053 - 10092

Published: May 4, 2024

Language: Английский

---

Energy-efficient Multi-objective Distributed Assembly Permutation Flowshop Scheduling by Q-learning based Meta-heuristics

Applied Soft Computing, Journal Year: 2024, Volume and Issue: unknown, P. 112247 - 112247

Published: Sept. 1, 2024

Language: Английский

---

A novel method-based reinforcement learning with deep temporal difference network for flexible double shop scheduling problem

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: April 20, 2024

Abstract This paper studies the flexible double shop scheduling problem (FDSSP) that considers simultaneously job and assembly shop. It brings about of association related tasks. To this end, a reinforcement learning algorithm with deep temporal difference network is proposed to minimize makespan. Firstly, FDSSP defined as mathematical model job-shop joined constraint level. translated into Markov decision process directly selects behavioral strategies according historical machining state data. Secondly, ten generic features are input neural fit value function. Similarly, eight simple constructive heuristics used candidate actions for decisions. From greedy mechanism, optimally combined all machines obtained each step. Finally, framework established, large number comparative experiments designed analyze basic performance algorithm. The results showed was better than most other methods, which contributed solving practical production manufacturing industry.

Language: Английский

---

A multi-strategy improved beluga whale optimization algorithm for constrained engineering problems

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(10), P. 14685 - 14727

Published: July 29, 2024

Language: Английский

---

Chaos-BBO: Chaos balanced butterfly optimizer with dynamic continuum chaotic strategies and its applications

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(9), P. 11911 - 11952

Published: July 30, 2024

Language: Английский

---

Multi-objective scheduling for an energy-efficient flexible job shop problem with peak power constraint

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 167, P. 112330 - 112330

Published: Oct. 8, 2024

Language: Английский

---

Hybrid Quantum-Classical Computation Framework for Production Optimization: A Case Study in Precision Mechanical Assembly

Published: Jan. 1, 2024

Language: Английский

---