The Dynamic Job Shop Scheduling Approach Based on Data-Driven Genetic Algorithm

Yu, Yang; Yu, Ying

doi:10.2174/1874129001408010653

TOEEJ

2014

DOI: 10.2174/1874129001408010653

|View full text |Cite

The Dynamic Job Shop Scheduling Approach Based on Data-Driven Genetic Algorithm

Yang Yu¹,

Ying Yu²

Abstract: Rapid development of the Internet of Things not only provides large amounts of data to the job-shop scheduling, but also proposes a great challenge for dynamic job shop scheduling. A dynamic job shop scheduling approach is proposed based on the data-driven genetic algorithm. Application examples suggest that this approach is correct, feasible and available. This approach can provide the technical support for the long-term development of enterprises in the field of intelligent production.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Advancing Sustainable Manufacturing: Reinforcement Learning with Adaptive Reward Machine Using an Ontology-Based Approach

Golpayegani,

Ghanadbashi,

Zarchini

2024

Sustainability

View full text Add to dashboard Cite

Sustainable manufacturing practices are crucial in job shop scheduling (JSS) to enhance the resilience of production systems against resource shortages and regulatory changes, contributing to long-term operational stability and environmental care. JSS involves rapidly changing conditions and unforeseen disruptions that can lead to inefficient resource use and increased waste. However, by addressing these uncertainties, we can promote more sustainable operations. Reinforcement learning-based job shop scheduler agents learn through trial and error by receiving scheduling decisions feedback in the form of a reward function (e.g., maximizing machines working time) from the environment, with their primary challenge being the handling of dynamic reward functions and navigating uncertain environments. Recently, Reward Machines (RMs) have been introduced to specify and expose reward function structures through a finite-state machine. With RMs, it is possible to define multiple reward functions for different states and switch between them dynamically. RMs can be extended to incorporate domain-specific prior knowledge, such as task-specific objectives. However, designing RMs becomes cumbersome as task complexity increases and agents must react to unforeseen events in dynamic and partially observable environments. Our proposed Ontology-based Adaptive Reward Machine (ONTOADAPT-REWARD) model addresses these challenges by dynamically creating and modifying RMs based on domain ontologies. This adaptability allows the model to outperform a state-of-the-art baseline algorithm in resource utilization, processed orders, average waiting time, and failed orders, highlighting its potential for sustainable manufacturing by optimizing resource usage and reducing idle times.

show abstract

Advancing Sustainable Manufacturing: Reinforcement Learning with Adaptive Reward Machine Using an Ontology-Based Approach

Golpayegani,

Ghanadbashi,

Zarchini

2024

Sustainability

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

The Dynamic Job Shop Scheduling Approach Based on Data-Driven Genetic Algorithm

Cited by 1 publication

References 8 publications

Advancing Sustainable Manufacturing: Reinforcement Learning with Adaptive Reward Machine Using an Ontology-Based Approach

Advancing Sustainable Manufacturing: Reinforcement Learning with Adaptive Reward Machine Using an Ontology-Based Approach

Contact Info

Product

Resources

About