Bayesian analysis for simulation input and output
Proceedings of the 29th conference on Winter simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Accounting for input model and parameter uncertainty in simulation
Proceedings of the 33nd conference on Winter simulation
Bayesian methods for discrete event simulation
WSC '04 Proceedings of the 36th conference on Winter simulation
DSS to manage ATM cash under periodic review with emergency orders
WSC '05 Proceedings of the 37th conference on Winter simulation
Bayesian Simulation and Decision Analysis: An Expository Survey
Decision Analysis
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In order to postpone production planning based on information obtained close to the time of sale, decision support systems for supply chain management often include demand forecasts based on little historical data and/or subjective information. Particularly, when simulation models for analyzing decisions related to safety inventories, lot sizing or lead times are used, it is convenient to model (daily) demand by considering historical data, as well as information (often subjective) of the near future. This article presents an approach for modeling a random input (e.g., demand) in simulation experiments. Under this approach, the family of distributions proposed for modeling demand should include two types of parameters: the ones that capture information of historical data and the ones that depend on the particular scenario that is to be simulated. The approach is extended to the case where uncertainty on the appropriate family of distributions is present.