Ranking and Selection for Steady-State Simulation: Procedures and Perspectives

Goldsman, David; Kim, Seong‐Hee; Marshall, William; Nelson, Barry L.

doi:10.1287/ijoc.14.1.2.7710

Cited by 72 publications

(48 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are two basic approaches: (i) how to select, with high probability, the system, decision, or policy that is-for practical purposes-the best of the potential choices; and (ii) how to screen the potential systems, decisions, or policies to obtain a (random-size) subset of "good" ones. Many procedures have been developed specifically to address some of the characteristics of simulation experiments we discuss in §3 (Chick and Inoue 2001, Hsu 1996, Goldsman et al 2002, Nelson and Goldsman 2001. Some assume that all populations are compared with each other, whereas others assume comparisons with a standard.…”

Section: Finding Robust Decisions or Policiesmentioning

confidence: 99%

State-of-the-Art Review: A User’s Guide to the Brave New World of Designing Simulation Experiments

Kleijnen

Sanchez

Lucas

et al. 2005

INFORMS Journal on Computing

377

212

View full text Add to dashboard Cite

Many simulation practitioners can get more from their analyses by using the statistical theory on design of experiments (DOE) developed specifically for exploring computer models. We discuss a toolkit of designs for simulators with limited DOE expertise who want to select a design and an appropriate analysis for their experiments. Furthermore, we provide a research agenda listing problems in the design of simulation experiments-as opposed to real-world experiments-that require more investigation. We consider three types of practical problems: (1) developing a basic understanding of a particular simulation model or system, (2) finding robust decisions or policies as opposed to so-called optimal solutions, and (3) comparing the merits of various decisions or policies. Our discussion emphasizes aspects that are typical for simulation, such as having many more factors than in real-world experiments, and the sequential nature of the data collection. Because the same problem type may be addressed through different design types, we discuss quality attributes of designs, such as the ease of design construction, the flexibility for analysis, and efficiency considerations. Moreover, the selection of the design type depends on the metamodel (response surface) that the analysts tentatively assume; for example, complicated metamodels require more simulation runs. We present several procedures to validate the metamodel estimated from a specific design, and we summarize a case study illustrating several of our major themes. We conclude with a discussion of areas that merit more work to achieve the potential benefits-either via new research or incorporation into standard simulation or statistical packages.

show abstract

Section: Finding Robust Decisions or Policiesmentioning

confidence: 99%

State-of-the-Art Review: A User’s Guide to the Brave New World of Designing Simulation Experiments

Kleijnen

Sanchez

Lucas

et al. 2005

INFORMS Journal on Computing

377

212

View full text Add to dashboard Cite

show abstract

“…A third class are the statistical methods (Goldsman, Kim, Marshall, and Nelson 2002;Ho, Cassandras, Chen, and Dai 2000;Pichitlamken and Nelson 2001). These methods are mostly used when the optimisation process involves selecting the best of a finite number of alternatives and the parameters are discrete.…”

Section: List the Following Featuresmentioning

confidence: 99%

A simulation optimisation approach for inventory management decision support based on incomplete information

Ramaekers

2008

4OR-Q J Oper Res

View full text Add to dashboard Cite

“…These articles develop statistical procedures that identify the best system encountered in the search by using a variety of approaches including subset selection and indifference-zone. Nelson (2001, 2004) and Goldsman et al (2002) present efficient fully-sequential indifference-zone techniques that eliminate systems deemed inferior as sampling progresses.…”

Section: Discrete Decision Spacementioning

confidence: 99%

Simulation optimization: A comprehensive review on theory and applications

Tekin¹,

Sabuncuoğlu

2004

IIE Transactions

207

View full text Add to dashboard Cite

For several decades, simulation has been used as a descriptive tool by the operations research community in the modeling and analysis of a wide variety of complex real systems. With recent developments in simulation optimization and advances in computing technology, it now becomes feasible to use simulation as a prescriptive tool in decision support systems. In this paper, we present a comprehensive survey on techniques for simulation optimization with emphasis given on recent developments. We classify the existing techniques according to problem characteristics such as shape of the response surface (global as compared to local optimization), objective functions (single or multiple objectives) and parameter spaces (discrete or continuous parameters). We discuss the major advantages and possible drawbacks of the different techniques. A comprehensive bibliography and future research directions are also provided in the paper.

show abstract

Ranking and Selection for Steady-State Simulation: Procedures and Perspectives

Cited by 72 publications

References 22 publications

State-of-the-Art Review: A User’s Guide to the Brave New World of Designing Simulation Experiments

State-of-the-Art Review: A User’s Guide to the Brave New World of Designing Simulation Experiments

A simulation optimisation approach for inventory management decision support based on incomplete information

Simulation optimization: A comprehensive review on theory and applications

Contact Info

Product

Resources

About