Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Operations Research
Probabilistic inference and influence diagrams
Operations Research
The simulation metamodel
Proceedings of the 30th conference on Winter simulation
A survey of ranking, selection, and multiple comparison procedures for discrete-event simulation
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Simulation optimization: simulation optimization
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Optimization by simulation metamodelling methods
WSC '04 Proceedings of the 36th conference on Winter simulation
Simulation optimization: a review, new developments, and applications
WSC '05 Proceedings of the 37th conference on Winter simulation
Learning Bayesian Networks
Simulation Modeling and Analysis (McGraw-Hill Series in Industrial Engineering and Management)
Simulation Modeling and Analysis (McGraw-Hill Series in Industrial Engineering and Management)
Decision Analysis
Analyzing air combat simulation results with dynamic Bayesian networks
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Influence diagrams with multiple objectives and tradeoff analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Multiple input and multiple output simulation metamodeling using Bayesian networks
Proceedings of the Winter Simulation Conference
Simulation metamodeling in continuous time using dynamic Bayesian networks
Proceedings of the Winter Simulation Conference
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In this paper, influence diagrams (IDs) are used as simulation metamodels to aid simulation based decision making. A decision problem under consideration is studied using discrete event simulation with decision alternatives as simulation parameters. The simulation data are used to construct an ID that presents the changes in simulation state with chance nodes. The decision alternatives and objectives of the decision problem are included in the ID as decision and utility nodes. The solution of the ID gives the optimal decision alternatives, i.e., the values of the simulation parameters that, e.g., maximize the expected value of the utility function measuring the attainment of the objectives. Furthermore, the constructed ID enables the analysis of the consequences of the decision alternatives and performing effective what-if analyses. The paper illustrates the construction and analysis of IDs with two examples from the field of military aviation.