Factor Screening for Simulation with Multiple Responses

Sequential Bifurcation

W. Shi, Jack P.C. Kleijnen, Zhixue Liu

Research output: Working paperDiscussion paperOther research output

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Abstract

Abstract: Factor screening searches for the really important inputs (factors) among the many inputs that are changed in a realistic simulation experiment. Sequential bifurcation (or SB) is a sequential method that changes groups of inputs simultaneously. SB is the most e¢ cient and effective method if the following assumptions are satis ed: (i) second-order polynomials are adequate approximations of the input/output (I/O) functions implied by the simulation model; (ii) the signs of all first-order (or main) effects are known; (iii) if two inputs have no important first-order effects, then they have no important second-order effects either (heredity property). This paper examines SB for random simulation with multiple responses (outputs), called multi-response SB (MSB). This MSB selects "batches" of inputs such that within a batch all inputs have the same sign for a specific type of output, so no cancellation of main effects occurs. MSB also applies Wald's sequential probability ratio test (SPRT) to obtain enough replicates for correctly classifying a group effect or an individual effect as important or unimportant. MSB enables e¢ cient selection of the initial number of replicates in SPRT. The paper also proposes a procedure to validate the three assumptions of MSB. The performance of MSB is examined through extensive Monte Carlo experiments that satisfy all MSB assumptions, and through a case study representing a logistic system in China; MSB performance is very promising.
Original languageEnglish
Place of PublicationTilburg
PublisherInformation Management
Number of pages37
Volume2012-032
Publication statusPublished - 2012

Publication series

NameCentER Discussion Paper
Volume2012-032

Fingerprint

Screening
Simulation
Bifurcation
Factors
Batch
Order effects
Approximation
Cancellation
China
Monte Carlo experiment
Logistics system
Simulation model
Simulation experiment
Individual effects
Polynomials

Keywords

  • design of experiments
  • curse of dimensionality
  • sparse effects

Cite this

Shi, W., Kleijnen, J. P. C., & Liu, Z. (2012). Factor Screening for Simulation with Multiple Responses: Sequential Bifurcation. (CentER Discussion Paper; Vol. 2012-032). Tilburg: Information Management.
Shi, W. ; Kleijnen, Jack P.C. ; Liu, Zhixue. / Factor Screening for Simulation with Multiple Responses : Sequential Bifurcation. Tilburg : Information Management, 2012. (CentER Discussion Paper).
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Shi, W, Kleijnen, JPC & Liu, Z 2012 'Factor Screening for Simulation with Multiple Responses: Sequential Bifurcation' CentER Discussion Paper, vol. 2012-032, Information Management, Tilburg.

Factor Screening for Simulation with Multiple Responses : Sequential Bifurcation. / Shi, W.; Kleijnen, Jack P.C.; Liu, Zhixue.

Tilburg : Information Management, 2012. (CentER Discussion Paper; Vol. 2012-032).

Research output: Working paperDiscussion paperOther research output

TY - UNPB

T1 - Factor Screening for Simulation with Multiple Responses

T2 - Sequential Bifurcation

AU - Shi, W.

AU - Kleijnen, Jack P.C.

AU - Liu, Zhixue

N1 - Pagination: 37

PY - 2012

Y1 - 2012

N2 - Abstract: Factor screening searches for the really important inputs (factors) among the many inputs that are changed in a realistic simulation experiment. Sequential bifurcation (or SB) is a sequential method that changes groups of inputs simultaneously. SB is the most e¢ cient and effective method if the following assumptions are satis ed: (i) second-order polynomials are adequate approximations of the input/output (I/O) functions implied by the simulation model; (ii) the signs of all first-order (or main) effects are known; (iii) if two inputs have no important first-order effects, then they have no important second-order effects either (heredity property). This paper examines SB for random simulation with multiple responses (outputs), called multi-response SB (MSB). This MSB selects "batches" of inputs such that within a batch all inputs have the same sign for a specific type of output, so no cancellation of main effects occurs. MSB also applies Wald's sequential probability ratio test (SPRT) to obtain enough replicates for correctly classifying a group effect or an individual effect as important or unimportant. MSB enables e¢ cient selection of the initial number of replicates in SPRT. The paper also proposes a procedure to validate the three assumptions of MSB. The performance of MSB is examined through extensive Monte Carlo experiments that satisfy all MSB assumptions, and through a case study representing a logistic system in China; MSB performance is very promising.

AB - Abstract: Factor screening searches for the really important inputs (factors) among the many inputs that are changed in a realistic simulation experiment. Sequential bifurcation (or SB) is a sequential method that changes groups of inputs simultaneously. SB is the most e¢ cient and effective method if the following assumptions are satis ed: (i) second-order polynomials are adequate approximations of the input/output (I/O) functions implied by the simulation model; (ii) the signs of all first-order (or main) effects are known; (iii) if two inputs have no important first-order effects, then they have no important second-order effects either (heredity property). This paper examines SB for random simulation with multiple responses (outputs), called multi-response SB (MSB). This MSB selects "batches" of inputs such that within a batch all inputs have the same sign for a specific type of output, so no cancellation of main effects occurs. MSB also applies Wald's sequential probability ratio test (SPRT) to obtain enough replicates for correctly classifying a group effect or an individual effect as important or unimportant. MSB enables e¢ cient selection of the initial number of replicates in SPRT. The paper also proposes a procedure to validate the three assumptions of MSB. The performance of MSB is examined through extensive Monte Carlo experiments that satisfy all MSB assumptions, and through a case study representing a logistic system in China; MSB performance is very promising.

KW - design of experiments

KW - curse of dimensionality

KW - sparse effects

M3 - Discussion paper

VL - 2012-032

T3 - CentER Discussion Paper

BT - Factor Screening for Simulation with Multiple Responses

PB - Information Management

CY - Tilburg

ER -

Shi W, Kleijnen JPC, Liu Z. Factor Screening for Simulation with Multiple Responses: Sequential Bifurcation. Tilburg: Information Management. 2012. (CentER Discussion Paper).