Skip to main content

Research Repository

Advanced Search

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

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


Akshit Singh

Profile Image

Dr. Sushma Kumari
Senior Lecturer and Programme Director- MSc Logistics and Supply Chain Management and Education Lead Logistics and Supply Chain Management

Kannan Govindan

Syed Imran Ali


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.


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.

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
Electronic ISSN 1366-588X
Publisher Taylor and Francis
Peer Reviewed Peer Reviewed
Volume 54
Issue 23
Pages 7115-7128
Public URL
Publisher URL
Additional Information Peer Review Statement: The publishing and review policy for this title is described in its Aims & Scope.; Aim & Scope: