Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Discrete event simulation for shop floor control
WSC '94 Proceedings of the 26th conference on Winter simulation
New advances and applications of combining simulation and optimization
WSC '96 Proceedings of the 28th conference on Winter simulation
Parametric inference for generalized semi-Markov processes
WSC '93 Proceedings of the 25th conference on Winter simulation
Simulation system for real-time planning, scheduling, and control
WSC '96 Proceedings of the 28th conference on Winter simulation
Proceedings of the 29th conference on Winter simulation
SLX: the X is for extensibility
Proceedings of the 32nd conference on Winter simulation
Real-time adaptive control of multi-product multi-server bulk service processes
Proceedings of the 33nd conference on Winter simulation
Value Function Based Production Scheduling
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Optimizing Production Manufacturing Using Reinforcement Learning
Proceedings of the Eleventh International Florida Artificial Intelligence Research Society Conference
Simulation optimization: simulation-based optimization
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Simulation optimization: simulation optimization
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Equipment interface: the relationship between simulation and emulation
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
WSC '05 Proceedings of the 37th conference on Winter simulation
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Based on a discrete-event simulation model, Simulationbased Real-time Decision-Making (SRDM) is an innovative approach to real-time, goal-directed decision-making. When applied to a flexible manufacturing system, SRDM makes better decisions than most fixed policies, such as deterministic, stochastic and manual. SRDM even improved over fixed policies optimized within a class of policies by OptQuest, in our numerical experiments. Compared to these fixed policies, SRDM shows greater improvement for more complex systems and is quite robust with respect to modeling errors. SRDM provides an improvement over fixed policies by its ability to implement adaptive policies. Since most real-time decisions in currently deployed manufacturing systems are made either manually or by using fixed policies, our results suggest that using SRDM instead could lead to significant improvement in operating performance.