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Title

Niching Genetic Programming to Learn Actions for Deep Reinforcement Learning in Dynamic Flexible Scheduling

Content partner

Victoria University of Wellington

Collection

Open Access Victoria University of Wellington

Description

Dynamic Flexible Job Shop Scheduling (DFJSS) is a critical combinatorial optimisation problem known for its dynamic nature and flexibility of machines. Traditional scheduling methods face limitations in adapting to such dynamic and flexible environments. Recently, there has been a trend in employing reinforcement learning (RL) to train scheduling agents for selecting manual scheduling heuristics at various decision points for DFJSS. However, the effectiveness of RL is constrained by the limit...

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Format

Research paper

Research format

Scholarly text / Journal article

Date created

2024

Creator

Meng Xu / Yi Mei / Fangfang Zhang / Mengjie Zhang

URL

https://figshare.com/articles/journal_contribution/Niching_Genetic_Programming_to_Learn_Actions_f...

Related subjects

Information and computing sciences / Artificial intelligence / Data management and data science / Data Management and Data Science / Information and Computing Sciences / Artificial Intelligence / Behavioral and Social Science / Basic Behavioral and Social Science / Artificial Intelligence & Image Processing

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