A survey of simulation optimization techniques and procedures

Swisher, James R.; Hyden, Paul; Jacobson, Sheldon H.; Schruben, Lee W.

doi:10.1109/wsc.2000.899706

Cited by 143 publications

(84 citation statements)

References 102 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Simulation optimization provides a structured approach to determine optimal input parameter values, where optimal is measured by a function of output variables (steady state or transient) associated with a simulation model (Swisher, 2000).…”

Section: Definition Of Problemmentioning

confidence: 99%

See 1 more Smart Citation

Basic Overview of Simulation Optimization

Hrčka

Važan

Šútova

2014

Research Papers Faculty of Materials Science and Technology Slovak University of Technology

View full text Add to dashboard Cite

The paper gives a basic overview of simulation optimization as a significant simulation technology. The computing requirements of simulation optimization cause that the practical usage of simulation optimization without software support is impossible. Therefore, the paper demonstrates typical software approach to simulation optimization and additionally presents the most important algorithms used in simulation optimization. The authors explain basic steps of implementing simulation optimization and present their own procedure. The advantages and disadvantages of simulation optimization are emphasized at the end of this paper.

show abstract

Section: Definition Of Problemmentioning

confidence: 99%

“…Selected important commercial packages are presented in the Table 1 (Fu, 2001;Swisher, 2000). The software available today does not guarantee locating the optimal solution in the shortest time for all possibly occurring problems.…”

Section: Fig 1 Black-box Approach To Simulation Optimizationmentioning

confidence: 99%

Basic Overview of Simulation Optimization

Hrčka

Važan

Šútova

2014

Research Papers Faculty of Materials Science and Technology Slovak University of Technology

View full text Add to dashboard Cite

show abstract

“…In addition, Sanchez [19] discusses the value of robust design and stresses the importance of simulation sensitivity analysis, together with model verification and validation. Whereas, Carson et al [5] and Swisher et al [24] cover the field of simulation optimization.…”

Section: Related Workmentioning

confidence: 99%

Adapting discrete-event simulation tools to support tactical forecasting in the automotive industry

et al. 2013

View full text Add to dashboard Cite

show abstract

“…These include various types of myopic policies (such as the logit investment rule in SEDS), rolling horizon policies (which use a point forecast of the future to make decisions now), simulation-optimization where we optimize the parameters of a myopic policy (Swisher et al (2000)), stochastic programming (Higle & Sen (1996), Birge & Louveaux (1997), Wallace & Fleten (2003)), and dynamic programming. We adopt the framework of dynamic programming in section 6.1, which introduces a number of computational hurdles.…”

Section: Algorithmic Strategymentioning

confidence: 99%

SMART: A Stochastic Multiscale Model for the Analysis of Energy Resources, Technology, and Policy

Powell

George

Simão

et al. 2012

INFORMS Journal on Computing

View full text Add to dashboard Cite

We address the problem of modeling long-term energy policy and investment decisions while retaining the important ability to capture fine-grained variations in intermittent energy and demand, as well as storage. In addition, we wish to capture sources of uncertainty such as future energy policies, climate, and technological advances, in addition to the variability as well as uncertainty in wind energy, demands, prices and rainfall. Accurately modeling the value of all investments such as wind and solar requires handling fine-grained temporal variability and uncertainty in wind and solar, as well as the use of storage. We propose a modeling and algorithmic strategy based on the framework of approximate dynamic programming (ADP) that can model these problems at hourly time increments over a multidecade horizon, while still capturing different types of uncertainty. This paper describes initial proof of concept experiments for an ADP-based model, called SMART, by describing the modeling and algorithmic strategy, and providing comparisons against a deterministic benchmark as well as initial experiments on stochastic datasets.

show abstract

A survey of simulation optimization techniques and procedures

Cited by 143 publications

References 102 publications

Basic Overview of Simulation Optimization

Basic Overview of Simulation Optimization

Adapting discrete-event simulation tools to support tactical forecasting in the automotive industry

SMART: A Stochastic Multiscale Model for the Analysis of Energy Resources, Technology, and Policy

Contact Info

Product

Resources

About