SMG: a new simulation/optimization approach for large-scale problems

It typically can be difficult to create and solve optimization models for large-scale sequential decision problems, examples of which include applications such as communications networks, inventory problems, and portfolio selection problems. Monte Carlo simulation modeling allows for the creation an...

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Veröffentlicht in:Winter Simulation Conference, 1999 Jg. 1; S. 569 - 572 vol.1
Hauptverfasser: Zobel, C.W., Scherer, W.T.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 1999
Schlagworte:
Communication networks
Explosives
Information management
Information technology
Large-scale systems
Modeling
Portfolios
State-space methods
Stochastic systems
Technology management
ISBN:0780357809, 9780780357808
Online-Zugang:Volltext
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Zusammenfassung:It typically can be difficult to create and solve optimization models for large-scale sequential decision problems, examples of which include applications such as communications networks, inventory problems, and portfolio selection problems. Monte Carlo simulation modeling allows for the creation and evaluation of these large-scale models without requiring a complete analytical specification. Unfortunately, optimization of such simulation models is especially difficult given the large state spaces that often produce a combinatorially explosive number of potential solution policies. In this paper we introduce a new technique, Simulation for Model Generation (SMG), that begins with a simulation model of the system of interest and then automatically builds and solves an underlying stochastic sequential decision model of the system. Since construction and implementation of the created model requires approximation techniques, we also discuss several types of error that are induced into the decision process. Fortunately, the decision policies produced by the SMG approach can be directly evaluated in the original simulation model-thus the results of the SMG model can be compared against any other possible strategies, including any decision policies currently in use.
ISBN:0780357809
9780780357808
DOI:10.1109/WSC.1999.823135