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

Mishra, Nishikant, Singh, Akshit, Kumari, Sushma and Govindan, Kannan (2016) Cloud-based multi-agent architecture for effective planning and scheduling of distributed manufacturing. International Journal of Production Research, 54 (23: Distributed Manufacturing to Enhance Productivity). pp. 7115-7128. ISSN 0020-7543

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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.

Item Type: Article
Uncontrolled Keywords: multi-agent architecture,cloud-computing technology,distributed manufacturing
Faculty \ School: Faculty of Social Sciences > Norwich Business School
Related URLs:
Depositing User: Pure Connector
Date Deposited: 22 Apr 2016 10:00
Last Modified: 31 Oct 2019 14:51
URI: https://ueaeprints.uea.ac.uk/id/eprint/58341
DOI: 10.1080/00207543.2016.1165359

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