NDLI: Simulation optimization using response surfaces based on spline approximations

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Simulation optimization using response surfaces based on spline approximations

Content Provider	ACM Digital Library
Author	Turnquist, Mark A. Daughety, Andrew F.
Abstract	This paper presents an approach designed to increase the efficiency and utility of search for optima of simulation models. Specifically, spline functions (odd-order polynomials fitted between simulation run outputs that match curvature at the end points) are used to approximate the simulation along suitably chosen directions of search. The splines are used to generate “pseudo-experiments” which enrich the data base formed from actual simulation runs. An overall (grand) function is then fit to this data base, yielding new direction(s) of search for the next iteration. Several characteristics of this technique are examined, including its sensitivity to experimental budget, number of iterations allowed, and size of feasible region. This approach results in not only an estimate of the optimal response from the simulation, but also a response surface estimated over a larger domain useful both for sensitivity analysis and in some cases as an approximate representation of the simulation for use in other modeling efforts. The paper describes an application of the technique to a model of a railroad classification yard. The objective is to find the numbers and sizes of inbound and outbound trains, and the dispatching policy within the yard, which minimize total car delay.
Starting Page	183
Ending Page	193
Page Count	11
File Format	PDF
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	1978-01-01
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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