Multi-agent Based Integration of Scheduling Algorithms

into account of the complexity of real-life systems and integrate almost all of scheduling algorithms into one system architecture and then use them dynamically to adapt to the change of production environments.

 If we look upon the algorithm research of classical scheduling theory as a microcosmic work for simple problems, then the idea described here is the solution to macroscopically applications for complex systems. Further research will be carried out in this aspect as follow:

 1)     Research of MASS need fully utilize results of classical scheduling theory and other production control techniques.

 2)     The functions of both computing agent and manager in this paper are basic and relatively simple. We will expend their functions in next work.

 3)     In general, structure of agent is often complex and need lots of work to design. So we may develop standard model of some types of agent (such as computing agent, resource agent, job agent and etc.) on the base of current research.

It will make reusing agent easy.

 4)     As we know, it is unpractical to design directly a universal system for scheduling. But we can dynamically assemble scheduling system for requirements of practical production environment with standard agents which response basic elements of enterprise.

 References

 

[1]Grace Yuh-jiun Lin, James J. Solberg. Integrated shop floor control using autonomous Agents. IIE Transactions,Volume 24, Number 3, July 1992: 57-71

[2] Carlos Ramos. An Architecture and a negotiation protocol for the dynamic scheduling of manufacturing systems．Proceedings - IEEE International Conference on Robotics and Automation pt 4 May 8-13 1994, Sponsored by IEEE: 3161-3166

[3] Carla P. Gomes, Austin Tate, Lyn Thomas. Distributed scheduling framework．Proceedings of the International Conference on Tools with Artificial Intelligence Nov 6-9 1994: 49~55

[4] D.Ouelhadj, C.Hanachi, B.Bouzouia. Multi-agent system for dynamic scheduling and control in manufacturing cells. IEEE International Conference on Robotics and Automation v 3 May 16-20 1998, Sponsored by IEEE:2128-2133

[5] R.J. Rabelo, L.M.Camarinha-Matos. Multi-Agent-Based agile scheduling. Robotics and Autonomous Systems(1999)27,15-28

[6] Rachel Lau, Joel Favrel. Intelligent scheduling agent for distributed decision-making. Proceedings of the 35th Conference on Decision and Control, Kobe, Japan, December, 1996, Sponsored by IEEE:3849-3850

[7] Koji Morikawa, Takeshi Furuhashi, Yoshiki Uchikawa. Evolution of CIM system with genetic algorithm. IEEE Conference on Evolutionary Computation - Proceedings v/2 Jun 27-Jun 29, 1994, Sponsored by IEEE: 746-749

[8] Gary Knotts, Moshe Dror, Bruce C. Hartman. Agent-based project scheduling. IIE Transactions (2000) 32, 387-401

[9] Albert D. Baker. Survey of factory control algorithms that can be implemented in a multi-agent heterarchy: Dispatching, scheduling, and pull. Journal of Manufacturing Systems v 17 n 4 1998 SME:297-320

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