Title

Input Models For Synthetic Optimization Problems

Abstract

In this paper, we describe and discuss alternative input models for the coefficients in synthetic optimization problems. Synthetic, or randomly generated, problems are often used in computational studies to establish the efficacy of solution methods or to facilitate comparative evaluations of solution methods. The selection of an input model for the coefficients in synthetic optimization problems is important because such a selection may affect the outcome of a computational study. Understanding how an assumed input model affects the characteristics of test problems can assist researchers in their efforts to accurately quantify and interpret the performance of solution methods.

Publication Date

1-1-1999

Publication Title

Winter Simulation Conference Proceedings

Volume

1

Number of Pages

116-121

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/324138.324175

Socpus ID

0033330359 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0033330359

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