Developing multi-agent systems with a FIPA-compliant agent framework
Software—Practice & Experience
The Gaia Methodology for Agent-Oriented Analysis and Design
Autonomous Agents and Multi-Agent Systems
A Framework to Control Emergent Survivability of Multi Agent Systems
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Using cooperative mobile agents to monitor distributed and dynamic environments
Information Sciences: an International Journal
A Multi-Agent System Based Approach to Intelligent Process Automation Systems
PRIMA '09 Proceedings of the 12th International Conference on Principles of Practice in Multi-Agent Systems
Information Sciences: an International Journal
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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.