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A multi-agent system architecture for coordination of just-in-time production and distribution
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Proceedings of the 2002 ACM symposium on Applied computing table of contents
Madrid, Spain
SESSION: Coordination models, languages and applications table of contents
Pages: 294 - 299  
Year of Publication: 2002
ISBN:1-58113-445-2
Authors
Paul Davidsson  Blekinge Institute of Technology, Soft Center, 372 25 Ronneby, Sweden
Fredrik Wernstedt  Blekinge Institute of Technology, Soft Center, 372 25 Ronneby, Sweden
Sponsor
SIGAPP: ACM Special Interest Group on Applied Computing
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 3,   Downloads (12 Months): 13,   Citation Count: 2
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ABSTRACT

A multi-agent system architecture for coordination of just-in-time production and distribution is presented. The problem to solve is two-fold: first the right amount of resources at the right time should be produced, and then these resources should be distributed to the right consumers. In order to solve the first problem, which is hard when the production and/or distribution time is relatively long, each consumer is equipped with an agent that makes predictions of future needs that it sends to a production agent. The second part of the problem is approached by forming clusters of consumers within which it is possible to redistribute resources fast and at a low cost in order to cope with discrepancies between predicted and actual consumption. Reallocation agents are introduced (one for each cluster) to manage the redistribution of resources. The suggested architecture is evaluated in a case study concerning management of district heating systems. Results from a preliminary simulation study show that the suggested approach makes it possible to control the trade-off between quality-of-service and degree of surplus production. We also compare the suggested approach to a reference control scheme (approximately corresponding to the current approach to district heating management), and conclude that it is possible to reduce the amount of resources produced while maintaining the quality of service. Finally, we describe a simulation experiment where the relation between the size of the clusters and the quality of service was studied.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Arvastsson, L.: Stochastic Modeling and Operational Optimization in District Heating Systems, Doctoral Thesis, Lund Institute of Technology, Sweden, 2001.
 
2
Arvastson. L., and Wollerstrand. J. (1997) On Sizing of Domestic Hot Water Heaters of Instantaneous Type. In proceedings of the 6th International Symposium on Automation of District Heating Systems.
 
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4
Canu, S., Duran, M., and Ding, X. (1994) District Heating Forecast Using Artificial Neural Networks, International Journal of Engineering, Vol. 2(4).
 
5
Holmberg. S. (1981) Norrköpingsprojektet --- en detaljerad pilotundersökning av hushållens vattenförbrukning, M81:5, Departement of Heating and Ventilation Technology, Royal Institute of Technology, Stockholm, Sweden. (in Swedish)
 
6
Malmström, B., Ernfors, P., Nilsson, D., and Vallgren, H.: Short-term forecasts of district heating load and outdoor temperature by use of on-line-connected computers. Värmeforsk, 1996:589, ISSN 0282-3772. (in Swedish)
 
7
Ohno, T.: Toyota Production System. Productivity P, US. 1988.
 
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Werner, S.:Dynamiska värmelaster från fiktiva värmebehov. Fjärrvämeföreningen, FOU 1997:10, ISSN 1402-5191. (in Swedish)


Collaborative Colleagues:
Paul Davidsson: colleagues
Fredrik Wernstedt: colleagues