Review on ensemble meta-heuristics and reinforcement learning for manufacturing scheduling problems

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 120, P. 109780 - 109780

Published: Oct. 18, 2024

Language: Английский

Dynamic scheduling of hybrid flow shop problem with uncertain process time and flexible maintenance using NeuroEvolution of Augmenting Topologies

IET Collaborative Intelligent Manufacturing, Journal Year: 2024, Volume and Issue: 6(3)

Published: Sept. 1, 2024

Abstract A hybrid flow shop is pivotal in modern manufacturing systems, where various emergencies and disturbances occur within the smart context. Efficiently solving dynamic scheduling problem (HFSP), characterised by release times, uncertain job processing flexible machine maintenance has become a significant research focus. NeuroEvolution of Augmenting Topologies (NEAT) algorithm proposed to minimise maximum completion time. To improve NEAT algorithm's efficiency effectiveness, several features were integrated: multi‐agent system with autonomous interaction centralised training develop parallel policy, maintenance‐related action for optimal decision learning, proactive avoid waiting jobs at moments, thereby exploring broader solution space. The performance trained model was experimentally compared Deep Q‐Network (DQN) five classical priority dispatching rules (PDRs) across scales. results show that achieves better solutions responds more quickly changes than DQN PDRs. Furthermore, generalisation test demonstrate NEAT's rapid problem‐solving ability on instances different from set.

Language: Английский

Scheduling with Sequence-Dependent Setup Times in Short-Term Production Planning: A Main Path Analysis-Based Review

Operations Research Perspectives, Journal Year: 2025, Volume and Issue: unknown, P. 100340 - 100340

Published: April 1, 2025

Language: Английский