Title

An investigation of the relationship between problem characteristics and algorithm performance: a case study of the GAP

Authors

Authors

M. C. Cario; J. J. Clifford; R. R. Hill; J. W. Yang; K. J. Yang;C. H. Reilly

Comments

Authors: contact us about adding a copy of your work at STARS@ucf.edu

Abbreviated Journal Title

IIE Trans.

Keywords

GENERALIZED ASSIGNMENT PROBLEM; KNAPSACK-PROBLEMS; SINGLE-MACHINE; Engineering, Industrial; Operations Research & Management Science

Abstract

We compare synthetic Generalized Assignment Problems (GAP) generated under two correlation-induction strategies: Implicit Correlation Induction (ICI) and Explicit Correlation Induction (ECI). We present computational results for two commercially-available solvers and four heuristics on 590 test problems. We conclude that the solvers' performances degrade as the population correlation between the objective function and capacity constraint coefficients decreases. However, the heuristics' performances improved as the absolute value of the same population correlation increases. We find that problems generated under ECI are more challenging than problems generated under ICI and may lead to a better understanding of the capabilities and limitations of the solution methods being evaluated. We recommend that one consider the purpose (s) of an experiment and types of solution procedures to be evaluated when determining what types of test problems to generate.

Journal Title

Iie Transactions

Volume

34

Issue/Number

3

Publication Date

1-1-2002

Document Type

Article

Language

English

First Page

297

Last Page

312

WOS Identifier

WOS:000171486000008

ISSN

0740-817X

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