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Interactive algorithms for a broad underlying family of preference functions

Interactive algorithms for a broad underlying family of preference functions
Interactive algorithms for a broad underlying family of preference functions
In multi-criteria decision making approaches it is typical to consider an underlying preference function that is assumed to represent the decision maker’s preferences. In this paper we introduce a broad family of preference functions that can represent a wide variety of preference structures. We develop the necessary theory and interactive algorithms for both the general family of the preference functions and for its special cases. The algorithms guarantee to find the most preferred solution (point) of the decision maker under the assumed conditions. The convergence of the algorithms are achieved by progressively reducing the solution space based on the preference information obtained from the decision maker and the properties of the assumed underlying preference functions. We first demonstrate the algorithms on a simple bi-criteria problem with a given set of available points. We also test the performances of the algorithms on three-criteria knapsack problems and show that they work well.
Multiple objective programming, Interactive algorithm, Search space reduction
0377-2217
248-262
Karakaya, G.
642830ea-7cab-4b6b-99ff-1eddb539b862
Koksalan, M.
c847a818-491b-4fa9-8537-fbe029f3740a
Ahipasaoglu, S. D.
d69f1b80-5c05-4d50-82df-c13b87b02687
Karakaya, G.
642830ea-7cab-4b6b-99ff-1eddb539b862
Koksalan, M.
c847a818-491b-4fa9-8537-fbe029f3740a
Ahipasaoglu, S. D.
d69f1b80-5c05-4d50-82df-c13b87b02687

Karakaya, G., Koksalan, M. and Ahipasaoglu, S. D. (2018) Interactive algorithms for a broad underlying family of preference functions. European Journal of Operational Research, 265 (1), 248-262. (doi:10.1016/j.ejor.2017.07.028).

Record type: Article

Abstract

In multi-criteria decision making approaches it is typical to consider an underlying preference function that is assumed to represent the decision maker’s preferences. In this paper we introduce a broad family of preference functions that can represent a wide variety of preference structures. We develop the necessary theory and interactive algorithms for both the general family of the preference functions and for its special cases. The algorithms guarantee to find the most preferred solution (point) of the decision maker under the assumed conditions. The convergence of the algorithms are achieved by progressively reducing the solution space based on the preference information obtained from the decision maker and the properties of the assumed underlying preference functions. We first demonstrate the algorithms on a simple bi-criteria problem with a given set of available points. We also test the performances of the algorithms on three-criteria knapsack problems and show that they work well.

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More information

e-pub ahead of print date: 12 July 2017
Published date: 16 February 2018
Keywords: Multiple objective programming, Interactive algorithm, Search space reduction

Identifiers

Local EPrints ID: 443189
URI: http://eprints.soton.ac.uk/id/eprint/443189
ISSN: 0377-2217
PURE UUID: 116912a4-1cf4-430d-a9eb-2d4209a9dec5
ORCID for S. D. Ahipasaoglu: ORCID iD orcid.org/0000-0003-1371-315X

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Date deposited: 13 Aug 2020 16:38
Last modified: 17 Mar 2024 04:03

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Contributors

Author: G. Karakaya
Author: M. Koksalan

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