Cloud-based multi-agent architecture for effective planning and scheduling of distributed manufacturing

Mishra, Nishikant; Singh, Akshit; Kumari, Sushma; Govindan, Kannan; Ali, Syed Imran

Abstract

In modern world, manufacturing processes have become very complex because of consistently fluctuating demand of customers. Numerous production facilities located at various geographical locations are being utilised to address the demands of their multiple clients. Often, the components manufactured at distinct locations are being assembled in a plant to develop the final product. In this complex scenario, manufacturing firms have to be responsive enough to cope with the fluctuating demand of customers. To accomplish it, there is a need to develop an integrated, dynamic and autonomous system. In this article, a self-reactive cloud-based multi-agent architecture for distributed manufacturing system is developed. The proposed architecture will assist manufacturing industry to establish real-time information exchange between the autonomous agents, clients, suppliers and manufacturing unit. The mechanism described in this study demonstrates how the autonomous agents interact with each other to rectify the internal discrepancies in manufacturing system. It can also address the external interferences like variations in client’s orders to maximise the profit of manufacturing firm in both short and long term. Execution process of proposed architecture is demonstrated using simulated case study.

Citation

Mishra, N., Singh, A., Kumari, S., Govindan, K., & Ali, S. I. (2016). Cloud-based multi-agent architecture for effective planning and scheduling of distributed manufacturing. International Journal of Production Research, 54(23), 7115-7128. https://doi.org/10.1080/00207543.2016.1165359

• Journal Article Type: Article
• Acceptance Date: Feb 27, 2016
• Online Publication Date: Apr 4, 2016
• Publication Date: Dec 1, 2016
• Deposit Date: Nov 22, 2018
• Journal: International Journal of Production Research
• Print ISSN: 0020-7543
• Publisher: Taylor and Francis
• Peer Reviewed: Peer Reviewed
• Volume: 54
• Issue: 23
• Pages: 7115-7128
• DOI: https://doi.org/10.1080/00207543.2016.1165359
• Public URL: https://hull-repository.worktribe.com/output/974393
• Publisher URL: https://www.tandfonline.com/doi/full/10.1080/00207543.2016.1165359
• Additional Information: Peer Review Statement: The publishing and review policy for this title is described in its Aims & Scope.; Aim & Scope: http://www.tandfonline.com/action/journalInformation?show=aimsScope&journalCode=tprs20