NDLI: A dynamic scheme for scheduling complex tasks in manufacturing systems based on collaboration of agents

Content Provider	Springer Nature Link
Author	Hsieh, Fu Shiung Lin, Jim Bon
Copyright Year	2014
Abstract	The characteristics of scheduling tasks in the real world is a dynamic and challenging issue as the processes and the companies involved may change from time to time. For small flexible enterprises to respond to business opportunities, an effective scheme to facilitate dynamic coalition, share the core competencies and resources and support inter-enterprise collaboration must be developed. Although multi-agent systems (MAS) provide a paradigm for modeling these characteristics, scheduling tasks in MAS is a complex problem due to the computational complexity involved, distributed architecture for scheduling tasks by individual agents and dependency of different agents’ workflows. How to develop a problem solver that can be applied in MAS to achieve coherent and consistent workflow schedules that can meet a customer’s order is an important issue. In this paper, we propose a solution methodology for scheduling workflows in MAS. Our solution combines the multi-agent system architecture to dynamically discover services, workflow and activity models to specify the capabilities of agents, contract net protocol to facilitate negotiation and coordination of agents and optimization theories to optimize the cost for fulfilling an order. A problem solver for scheduling tasks in MAS has been implemented. An application scenario has also been provided to verify our solution methodology.
Starting Page	366
Ending Page	382
Page Count	17
File Format	PDF
ISSN	0924669X
Journal	Applied Intelligence
Volume Number	41
Issue Number	2
e-ISSN	15737497
Language	English
Publisher	Springer US
Publisher Date	2014-03-15
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Intelligent manufacturing system Multi-agent system Collaboration Artificial Intelligence (incl. Robotics) Mechanical Engineering Manufacturing, Machines, Tools
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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