Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
Scheduling of production processes
Scheduling of production processes
Intelligent scheduling
Foundations of distributed artificial intelligence
Foundations of distributed artificial intelligence
Multiagent systems: a modern approach to distributed artificial intelligence
Multiagent systems: a modern approach to distributed artificial intelligence
What's in a node: nodes and agents in logistic networks
Enterprise information systems
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Resource Allocation in Distributed Factory Scheduling
IEEE Expert: Intelligent Systems and Their Applications
Agent Communication for Scheduling in the Extended Enterprise
PRO-VE '99 Proceedings of the IFIP TC5 WG5.3 / PRODNET Working Conference on Infrastructures for Virtual Enterprises: Networking Industrial Enterprises
The Virtual Enterprise Concept
PRO-VE '99 Proceedings of the IFIP TC5 WG5.3 / PRODNET Working Conference on Infrastructures for Virtual Enterprises: Networking Industrial Enterprises
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In this article we describe a multi-agent dynamic scheduling environment where autonomous agents represent enterprises and manage the capacity of individual macro-resources in a production-distribution context. The agents are linked by client-supplier relationships and interagent communication must take place. The model of the environment, the appropriate agent interaction protocol and a cooperative scheduling approach, emphasizing a temporal scheduling perspective of scheduling problems, are described. The scheduling approach is based on a coordination mechanism supported by the interchange of certain temporal information among pairs of client-supplier agents involved. This information allows the agents to locally perceive hard global temporal constraints and recognize non over-constrained problems and, in this case, rule out non temporally-feasible solutions and establish an initial solution. The same kind of information is then used to guide re-scheduling to repair the initial solution and converge to a final one.