Neuro-Evolution of Augmenting Topologies for Dynamic Scheduling of Hybrid Flow Shop Problem

Neuro-Evolution of Augmenting Topologies for Dynamic Scheduling of Hybrid Flow Shop Problem

In this paper, the Neuro-Evolution of Augmenting Topologies (NEAT) algorithm is proposed to minimize the maximum completion time in a dynamic scheduling problem of hybrid flow shops. In hybrid flow shops, machines require flexible preventive maintenance and jobs arrive randomly with uncertain proces...

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Bibliographic Details
Main Authors: Junjie Zhang, Yarong Chen, Jabir Mumtaz, Shengwei Zhou
Format: Article
Language:English
Published: MDPI AG 2023-09-01
Series:Engineering Proceedings
Subjects:
hybrid flow shop
reinforcement learning
Neuro-Evolution of Augmenting Topologies
makespan
dynamic scheduling
Online Access:https://www.mdpi.com/2673-4591/45/1/25

Internet

https://www.mdpi.com/2673-4591/45/1/25

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