Factor screening for simulation with multiple responses: Sequential bifurcation
•Our method finds the inputs with important effects on one or more outputs.•Our method is efficient because it is sequential.•Our method determines the number of replicates through an improved statistical test.•Our method includes a procedure to validate the assumptions of our method.•We illustrate...
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Published in | European journal of operational research Vol. 237; no. 1; pp. 136 - 147 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Amsterdam
Elsevier B.V
16.08.2014
Elsevier Sequoia S.A |
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Abstract | •Our method finds the inputs with important effects on one or more outputs.•Our method is efficient because it is sequential.•Our method determines the number of replicates through an improved statistical test.•Our method includes a procedure to validate the assumptions of our method.•We illustrate our method through a case study on a Chinese logistics network.
The goal of factor screening is to find the really important inputs (factors) among the many inputs that may be changed in a realistic simulation experiment. A specific method is sequential bifurcation (SB), which is a sequential method that changes groups of inputs simultaneously. SB is most efficient and effective if the following assumptions are satisfied: (i) second-order polynomials are adequate approximations of the input/output functions implied by the simulation model; (ii) the signs of all first-order effects are known; and (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 groups of inputs such that—within a group—all inputs have the same sign for a specific type of output, so no cancellation of first-order effects occurs. To obtain enough replicates (replications) for correctly classifying a group effect or an individual effect as being important or unimportant, MSB applies Wald’s sequential probability ratio test (SPRT). The initial number of replicates in this SPRT is also selected efficiently by MSB. Moreover, MSB includes a procedure to validate the three assumptions of MSB. The paper evaluates the performance of MSB through extensive Monte Carlo experiments that satisfy all MSB assumptions, and through a case study representing a logistic system in China; the results are very promising. |
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AbstractList | The goal of factor screening is to find the really important inputs (factors) among the many inputs that may be changed in a realistic simulation experiment. A specific method is sequential bifurcation (SB), which is a sequential method that changes groups of inputs simultaneously. SB is most efficient and effective if the following assumptions are satisfied: (i) second-order polynomials are adequate approximations of the input/output functions implied by the simulation model; (ii) the signs of all first-order effects are known; and (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 groups of inputs such that-within a group-all inputs have the same sign for a specific type of output, so no cancellation of first-order effects occurs. To obtain enough replicates (replications) for correctly classifying a group effect or an individual effect as being important or unimportant, MSB applies Wald's sequential probability ratio test (SPRT). The initial number of replicates in this SPRT is also selected efficiently by MSB. Moreover, MSB includes a procedure to validate the three assumptions of MSB. The paper evaluates the performance of MSB through extensive Monte Carlo experiments that satisfy all MSB assumptions, and through a case study representing a logistic system in China; the results are very promising. The goal of factor screening is to find the really important inputs (factors) among the many inputs that may be changed in a realistic simulation experiment. A specific method is sequential bifurcation (SB), which is a sequential method that changes groups of inputs simultaneously. SB is most efficient and effective if the following assumptions are satisfied: (i) second-order polynomials are adequate approximations of the input/output functions implied by the simulation model; (ii) the signs of all first-order effects are known; and (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 groups of inputs such that -- within a group -- all inputs have the same sign for a specific type of output, so no cancellation of first-order effects occurs. To obtain enough replicates (replications) for correctly classifying a group effect or an individual effect as being important or unimportant, MSB applies Wald's sequential probability ratio test (SPRT). The initial number of replicates in this SPRT is also selected efficiently by MSB. Moreover, MSB includes a procedure to validate the three assumptions of MSB. The paper evaluates the performance of MSB through extensive Monte Carlo experiments that satisfy all MSB assumptions, and through a case study representing a logistic system in China; the results are very promising. [PUBLICATION ABSTRACT] •Our method finds the inputs with important effects on one or more outputs.•Our method is efficient because it is sequential.•Our method determines the number of replicates through an improved statistical test.•Our method includes a procedure to validate the assumptions of our method.•We illustrate our method through a case study on a Chinese logistics network. The goal of factor screening is to find the really important inputs (factors) among the many inputs that may be changed in a realistic simulation experiment. A specific method is sequential bifurcation (SB), which is a sequential method that changes groups of inputs simultaneously. SB is most efficient and effective if the following assumptions are satisfied: (i) second-order polynomials are adequate approximations of the input/output functions implied by the simulation model; (ii) the signs of all first-order effects are known; and (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 groups of inputs such that—within a group—all inputs have the same sign for a specific type of output, so no cancellation of first-order effects occurs. To obtain enough replicates (replications) for correctly classifying a group effect or an individual effect as being important or unimportant, MSB applies Wald’s sequential probability ratio test (SPRT). The initial number of replicates in this SPRT is also selected efficiently by MSB. Moreover, MSB includes a procedure to validate the three assumptions of MSB. The paper evaluates the performance of MSB through extensive Monte Carlo experiments that satisfy all MSB assumptions, and through a case study representing a logistic system in China; the results are very promising. |
Author | Liu, Zhixue Shi, Wen Kleijnen, Jack P.C. |
Author_xml | – sequence: 1 givenname: Wen surname: Shi fullname: Shi, Wen email: shi3wen@163.com organization: Research Center of Hubei Logistics Development, Hubei University of Economics, Wuhan 430205, China – sequence: 2 givenname: Jack P.C. surname: Kleijnen fullname: Kleijnen, Jack P.C. email: kleijnen@uvt.nl organization: Department of Information Management/CentER, Tilburg University, 5000 LE Tilburg, The Netherlands – sequence: 3 givenname: Zhixue surname: Liu fullname: Liu, Zhixue email: lsy868@mail.hust.edu.cn organization: School of Management, Huazhong University of Science and Technology, Wuhan 430074, China |
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Planning and Inference doi: 10.1016/j.jspi.2012.02.005 contributor: fullname: De – volume: 57 start-page: 45 issue: 1 year: 2010 ident: 10.1016/j.ejor.2014.02.021_b0065 article-title: A hybrid method for simulation factor screening publication-title: Naval Research Logistics (NRL) doi: 10.1002/nav.20382 contributor: fullname: Shen – year: 2009 ident: 10.1016/j.ejor.2014.02.021_b0085 contributor: fullname: Wu – volume: 198 start-page: 511 issue: 2 year: 2009 ident: 10.1016/j.ejor.2014.02.021_b0060 article-title: Controlled sequential factorial design for simulation factor screening publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2008.09.005 contributor: fullname: Shen – volume: 54 start-page: 743 issue: 4 year: 2006 ident: 10.1016/j.ejor.2014.02.021_b0075 article-title: Controlled sequential bifurcation: A new factor-screening method for discrete-event simulation publication-title: Operations Research doi: 10.1287/opre.1060.0311 contributor: fullname: Wan – volume: 96 start-page: 180 issue: 1 year: 1997 ident: 10.1016/j.ejor.2014.02.021_b0005 article-title: Searching for important factors in simulation models with many factors: Sequential bifurcation publication-title: European Journal of Operational Research doi: 10.1016/S0377-2217(96)00156-7 contributor: fullname: Bettonvil – year: 1987 ident: 10.1016/j.ejor.2014.02.021_b0030 contributor: fullname: Kleijnen – ident: 10.1016/j.ejor.2014.02.021_b0090 doi: 10.1109/WSC.2010.5679088 – volume: 229 start-page: 411 issue: 2 year: 2013 ident: 10.1016/j.ejor.2014.02.021_b0025 article-title: Optimal average sample number of the SPRT chart for monitoring fraction nonconforming publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2013.03.026 contributor: fullname: Haridy – ident: 10.1016/j.ejor.2014.02.021_b0010 doi: 10.1145/268437.268491 – year: 2007 ident: 10.1016/j.ejor.2014.02.021_b0050 contributor: fullname: Montgomery – volume: 229 start-page: 695 issue: 3 year: 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Snippet | •Our method finds the inputs with important effects on one or more outputs.•Our method is efficient because it is sequential.•Our method determines the number... The goal of factor screening is to find the really important inputs (factors) among the many inputs that may be changed in a realistic simulation experiment. A... |
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SubjectTerms | Approximation Bifurcations Computer simulation Design of experiments Effectiveness studies Heredity Input output analysis Logistics Mathematical models Monte Carlo simulation Operations research Polynomials Probability distribution Replication Screening Simulation Statistical analysis |
Title | Factor screening for simulation with multiple responses: Sequential bifurcation |
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