How to treat strict preference information in multicriteria decision analysis

K. S. Park, ik rae Jeong

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

This paper addresses the use of incomplete information on both multi-criteria alternative values and importance weights in evaluating decision alternatives. Incomplete information frequently takes the form of strict inequalities, such as strict orders and strict bounds. En route to prioritizing alternatives, the majority of previous studies have replaced these strict inequalities with weak inequalities, by employing a small positive number. As this replacement closes the feasible region of decision parameters, it circumvents certain troubling questions that arise when utilizing a mathematical programming approach to evaluate alternatives. However, there are no hard and fast rules for selecting the factual small value and, even if the choice is possible, the resultant prioritizations depend profoundly on that choice. The method developed herein addresses and overcomes this drawback, and allows for dominance and potential optimality among alternatives, without selecting any small value for the strict preference information. Given strict information on criterion weights alone, we form a linear program and solve it via a two-stage method. When both alternative values and weights are provided in the form of strict inequalities, we first construct a nonlinear program, transform it into a linear programming equivalent, and finally solve this linear program via the same two-stage method. One application of this methodology to a market entry decision, a salient subject in the area of international marketing, is demonstrated in detail herein.

Original languageEnglish
Pages (from-to)1771-1783
Number of pages13
JournalJournal of the Operational Research Society
Volume62
Issue number10
DOIs
Publication statusPublished - 2011 Oct 1

Fingerprint

Decision theory
Mathematical programming
Linear programming
Marketing
Multi-criteria decision analysis

Keywords

  • dominance
  • incomplete information
  • market entry decision
  • multicriteria decision analysis
  • potential optimality
  • strict inequality

ASJC Scopus subject areas

  • Management Science and Operations Research
  • Management Information Systems
  • Marketing
  • Strategy and Management

Cite this

How to treat strict preference information in multicriteria decision analysis. / Park, K. S.; Jeong, ik rae.

In: Journal of the Operational Research Society, Vol. 62, No. 10, 01.10.2011, p. 1771-1783.

Research output: Contribution to journalArticle

@article{62360d53bf7a4e8e9a1dfe89e78711a7,
title = "How to treat strict preference information in multicriteria decision analysis",
abstract = "This paper addresses the use of incomplete information on both multi-criteria alternative values and importance weights in evaluating decision alternatives. Incomplete information frequently takes the form of strict inequalities, such as strict orders and strict bounds. En route to prioritizing alternatives, the majority of previous studies have replaced these strict inequalities with weak inequalities, by employing a small positive number. As this replacement closes the feasible region of decision parameters, it circumvents certain troubling questions that arise when utilizing a mathematical programming approach to evaluate alternatives. However, there are no hard and fast rules for selecting the factual small value and, even if the choice is possible, the resultant prioritizations depend profoundly on that choice. The method developed herein addresses and overcomes this drawback, and allows for dominance and potential optimality among alternatives, without selecting any small value for the strict preference information. Given strict information on criterion weights alone, we form a linear program and solve it via a two-stage method. When both alternative values and weights are provided in the form of strict inequalities, we first construct a nonlinear program, transform it into a linear programming equivalent, and finally solve this linear program via the same two-stage method. One application of this methodology to a market entry decision, a salient subject in the area of international marketing, is demonstrated in detail herein.",
keywords = "dominance, incomplete information, market entry decision, multicriteria decision analysis, potential optimality, strict inequality",
author = "Park, {K. S.} and Jeong, {ik rae}",
year = "2011",
month = "10",
day = "1",
doi = "10.1057/jors.2010.155",
language = "English",
volume = "62",
pages = "1771--1783",
journal = "Journal of the Operational Research Society",
issn = "0160-5682",
publisher = "Palgrave Macmillan Ltd.",
number = "10",

}

TY - JOUR

T1 - How to treat strict preference information in multicriteria decision analysis

AU - Park, K. S.

AU - Jeong, ik rae

PY - 2011/10/1

Y1 - 2011/10/1

N2 - This paper addresses the use of incomplete information on both multi-criteria alternative values and importance weights in evaluating decision alternatives. Incomplete information frequently takes the form of strict inequalities, such as strict orders and strict bounds. En route to prioritizing alternatives, the majority of previous studies have replaced these strict inequalities with weak inequalities, by employing a small positive number. As this replacement closes the feasible region of decision parameters, it circumvents certain troubling questions that arise when utilizing a mathematical programming approach to evaluate alternatives. However, there are no hard and fast rules for selecting the factual small value and, even if the choice is possible, the resultant prioritizations depend profoundly on that choice. The method developed herein addresses and overcomes this drawback, and allows for dominance and potential optimality among alternatives, without selecting any small value for the strict preference information. Given strict information on criterion weights alone, we form a linear program and solve it via a two-stage method. When both alternative values and weights are provided in the form of strict inequalities, we first construct a nonlinear program, transform it into a linear programming equivalent, and finally solve this linear program via the same two-stage method. One application of this methodology to a market entry decision, a salient subject in the area of international marketing, is demonstrated in detail herein.

AB - This paper addresses the use of incomplete information on both multi-criteria alternative values and importance weights in evaluating decision alternatives. Incomplete information frequently takes the form of strict inequalities, such as strict orders and strict bounds. En route to prioritizing alternatives, the majority of previous studies have replaced these strict inequalities with weak inequalities, by employing a small positive number. As this replacement closes the feasible region of decision parameters, it circumvents certain troubling questions that arise when utilizing a mathematical programming approach to evaluate alternatives. However, there are no hard and fast rules for selecting the factual small value and, even if the choice is possible, the resultant prioritizations depend profoundly on that choice. The method developed herein addresses and overcomes this drawback, and allows for dominance and potential optimality among alternatives, without selecting any small value for the strict preference information. Given strict information on criterion weights alone, we form a linear program and solve it via a two-stage method. When both alternative values and weights are provided in the form of strict inequalities, we first construct a nonlinear program, transform it into a linear programming equivalent, and finally solve this linear program via the same two-stage method. One application of this methodology to a market entry decision, a salient subject in the area of international marketing, is demonstrated in detail herein.

KW - dominance

KW - incomplete information

KW - market entry decision

KW - multicriteria decision analysis

KW - potential optimality

KW - strict inequality

UR - http://www.scopus.com/inward/record.url?scp=80051914893&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80051914893&partnerID=8YFLogxK

U2 - 10.1057/jors.2010.155

DO - 10.1057/jors.2010.155

M3 - Article

AN - SCOPUS:80051914893

VL - 62

SP - 1771

EP - 1783

JO - Journal of the Operational Research Society

JF - Journal of the Operational Research Society

SN - 0160-5682

IS - 10

ER -