Planning in distributed artificial intelligence
Foundations of distributed artificial intelligence
Distributed rational decision making
Multiagent systems
Multi-Agent coordination based on tokens: reduction of the bullwhip effect in a forest supply chain
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
A key-based coordination algorithm for dynamic readiness and repair service coordination
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Productivity improvement: shifting bottleneck detection
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Proceedings of the 35th conference on Winter simulation: driving innovation
An integrated token-based algorithm for scalable coordination
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Simulation-based advanced WIP management and control in semiconductor manufacturing
WSC '04 Proceedings of the 36th conference on Winter simulation
The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver
IEEE Transactions on Computers
Robust coordination to sustain throughput of an unstable agent network
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Coordination to avoid starvation of bottleneck agents in a large network system
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Multi-agent coordination using local search
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
| Hi-index | 12.05 |
We present a multiagent coordination technique to maintain throughput of a large-scale manufacturing system in the face of machine failures. Failures do not just deteriorate throughput of the system but also create and change bottlenecks in the system. Since loss of bottleneck's capacity degrades the overall system performance, the system should identify bottlenecks dynamically and keep their utilization at a high level. In our system, CABS, information about a machine's urgency of jobs to fulfill demanded throughput and maintain its utilization is passed to upstream machines in the product routes. Upstream machines utilize this information to identify bottleneck machines and coordinate their actions to provide the bottlenecks with necessary and sufficient jobs for preventing their starvation and congestion. We empirically evaluate CABS and show its effectiveness using a benchmark problem of the semiconductor fabrication process in comparison with CONWIP, which is a well-known traditional manufacturing control method.