Dynamic Multi-agent Real Time Scheduling Framework for Production Management

Production management is a complex problem. The problem becomes more complex in open, uncertain, dynamic environments and distributed productions. The existing static scheduling methods are not efficient to deal with this problem efficiently. This paper proposed multi-agent approach for real time scheduling in production management to efficiently manage production. The proposed scheduling optimizes the performance of production management in dynamic uncertain environment. Dynamic multi-agent approach in real time scheduling consist of the agent collect and analysis the production data in real time and ensure real time response to emergency events. Real time scheduling agents increase the resource utilization and success rate in production management by using real time feedback.

[1]  José Palazzo Moreira de Oliveira,et al.  Autonomic computing approach for resource allocation , 2005, Expert Syst. Appl..

[2]  Lyes Khoukhi,et al.  Industrial IoT Data Scheduling Based on Hierarchical Fog Computing: A Key for Enabling Smart Factory , 2018, IEEE Transactions on Industrial Informatics.

[3]  T. Yalcinoz,et al.  Implementing soft computing techniques to solve economic dispatch problem in power systems , 2008, Expert Syst. Appl..

[4]  Panos M. Pardalos,et al.  Uniform parallel machine scheduling problems with fixed machine cost , 2018, Optim. Lett..

[5]  Sohyung Cho,et al.  Distributed adaptive control of production scheduling and machine capacity , 2007 .

[6]  Fei Tao,et al.  A multi-agent architecture for scheduling in platform-based smart manufacturing systems , 2019, Frontiers of Information Technology & Electronic Engineering.

[7]  Alexandre Dolgui,et al.  A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0 , 2016 .

[8]  Om Ji Shukla,et al.  A Review of Multi Agent based Production Scheduling in Manufacturing System , 2020 .

[9]  Jinliang Ding,et al.  Two-objective stochastic flow-shop scheduling with deteriorating and learning effect in Industry 4.0-based manufacturing system , 2017, Appl. Soft Comput..

[10]  Lihui Wang,et al.  Scheduling in cloud manufacturing: state-of-the-art and research challenges , 2019, Int. J. Prod. Res..

[11]  Xingjun Zhang,et al.  Memory-aware resource management algorithm for low-energy cloud data centers , 2020, Future Gener. Comput. Syst..

[12]  Salvatore Cannella,et al.  Insights on Multi-Agent Systems Applications for Supply Chain Management , 2020, Sustainability.