Achieving cost competitiveness with an agent based integrated process planning and production scheduling system


From a manufacturing perspective, the efficiency of manufacturing operations (such as process planning and production scheduling) are the key element for enhancing manufacturing competence. Process planning and production scheduling functions have been traditionally treated as two separate activities, and have resulted in a range of inefficiencies. These include infeasible process plans, non-available/overloaded resources, high production costs, long production lead times, and so on. Above all, it is unlikely that the dynamic changes can be efficiently dealt with. Despite much research has been conducted to integrate process planning and production scheduling to generate optimised solutions to improve manufacturing efficiency, there is still a gap to achieve the competence required for the current global competitive market. In this research, the concept of multi-agent system (MAS) is adopted as a means to address the aforementioned gap. A MAS consists of a collection of intelligent autonomous agents able to solve complex problems. These agents possess their individual objectives and interact with each other to fulfil the global goal. This paper describes a novel use of an autonomous agent system to facilitate the integration of process planning and production scheduling functions to cope with unpredictable demands, in terms of uncertainties in product mix and demand pattern. The novelty lies with the currency-based iterative agent bidding mechanism to allow process planning and production scheduling options to be evaluated simultaneously, so as to search for an optimised, cost-effective solution. This agent based system aims to achieve manufacturing competence by means of enhancing the flexibility and agility of manufacturing enterprises.

Divisions: College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Engineering Systems and Supply Chain Management
College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > Sustainable environment research group
Additional Information: Creative Commons - Attribution 3.0
ISBN: 978-953-307-935-6
Last Modified: 22 May 2024 07:25
Date Deposited: 25 Apr 2012 11:02
Full Text Link: http://www.inte ... tion-scheduling
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PURE Output Type: Chapter (peer-reviewed)
Published Date: 2012-01
Authors: Lim, Ming
Zhang, David



Version: Published Version

License: Creative Commons Attribution

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