An extreme-distance approach to multiple criteria ranking

Chen, Ye; Kilgour, D. Marc; Hipel, Keith W.

doi:10.1016/j.mcm.2010.10.001

Cited by 29 publications

(11 citation statements)

References 26 publications

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“…• Normalize the performance matrix: Apply a normalization process to the performance matrix to convert the original consequence data to values. Assume that ∀ ∈ ℝ + , three widely used normalization functions, mapping from to (0 ≤ ≤ 1), are listed below [5,6,23]:…”

Section: The Topsis Methodsmentioning

confidence: 99%

“…For example, in business analysis various benchmarks have been proposed for company performance evaluations [1]. The current TOPSIS method does not provide a flexible mechanism to allow a DM to specify + and − in the consequence data space directly, but the DM may feel much easier and more meaningful compared to discussions in the value data space [5,6].…”

Section: Flexible Settings Of + and −mentioning

confidence: 99%

“…To improve the setting flexibility, this article allows a DM to define + and − in the consequence data space directly as long as the following conditions are satisfied [5]:…”

Section: Flexible Settings Of + and −mentioning

confidence: 99%

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An OWA-TOPSIS method for multiple criteria decision analysis

Chen

Liu

2011

Expert Systems with Applications

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A hybrid approach integrating OWA (Ordered Weighted Averaging) aggregation into TOPSIS (technique for order performance by similarity to ideal solution) is proposed to tackle multiple criteria decision analysis (MCDA) problems. First, the setting of extreme points (ideal and anti-ideal points) in TOPSIS is redefined and extended for handling the multiple extreme points situation where a decision maker (DM) or multiple DMs can provide more than one pair of extreme points. Next, three different aggregation schemes are designed to integrate OWA into the TOPSIS analysis procedure. A numerical example is provided to demonstrate the proposed approach and the results are compared for different aggregation settings and confirm the robustness of rankings from different scenarios.

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Section: The Topsis Methodsmentioning

confidence: 99%

Section: Flexible Settings Of + and −mentioning

confidence: 99%

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An OWA-TOPSIS method for multiple criteria decision analysis

Chen

Liu

2011

Expert Systems with Applications

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“…Taking into account the fact that during the negotiation process the negotiator can introduce new alternatives (outside of the predefined template) we propose to define subjectively the ideal and anti-ideal solutions in the form of aspiration and reservation packages, which expands the initial negotiation space defined on the basis of TOPSIS-based max and min solutions. This will make our negotiation problem stable, which means that the new offer will not change the scoring system obtained before this offer was introduced into the predefined set of alternatives, nor will it result in rank reversal which can appear in the classic TOPSIS procedure [2,6]. To obtain criteria weights the AHP method may be used, the pairwise comparisons of which would allow the negotiator to define their preferences in a natural and intuitive way.…”

Section: Topsismentioning

confidence: 99%

Supporting Ill-Structured Negotiation Problems

Roszkowska

Brzostowski²,

Wachowicz

2013

Human-Centric Decision-Making Models for Social Sciences

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The negotiation is a complex decision-making process in which two or more parties talk with one another in afford to resolve their opposing interests. It can be divided into consecutive stages, namely: pre-negotiation phase involving structuring the problem and the analysis of preferences, the intention phase involving the iterative exchange of offers and counter-offers, and the postoptimization phase aiming at the improvement of the agreement obtained in the intention phase. In this chapter, we focus on the analysis of negotiators preferences in ill-structured negotiation problems. We employ the modified FTOPSIS approach and the AHP method for determining the negotiation offers scoring system, which allows for the easy evaluation of both the incoming offers as well as the packages under preparation. The imprecision and vagueness of the packages and options descriptions is modeled by the fuzzy triangular numbers. The Analytic Hierarchy Process is used to derive the negotiation issue weights instead of directly assigning such values to the issues (a classic approach). The FTOPSIS method is used to build the final scoring system allowing for the evaluation of any potential negotiation package. The whole process of negotiation supported by the approach we proposed is illustrated with an numerical example. E. Roszkowska (B)

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“…MCDA aims to furnish a set of decision analysis techniques to help decision makers (DMs) that logically identify, compare, and evaluate alternatives according to diversity, usually conflicting, criteria arising from social, economic, and environmental considerations [1][2][3][4][5][6][7]. In MCDA, a decision maker (DM) must evaluate alternatives with regard to each criterion, address criteria weights, and select the best result from the generated set of alternatives.…”

Section: Introductionmentioning

confidence: 99%

A new DEA approach to rank alternatives in MCDA

Darehmiraki¹,

Behdani²

2013

Data Envelopment Analysis and Decision Science

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One of the principal subjects in multiple criteria decision analysis is ranking alternatives. Here, we present a new method to rank alternatives by using data envelopment analysis. In this paper, one ranking method is proposed based on applying an artificial alternative called aggregate alternative. The method is based on the fact that one efficient alternative with a better performance has stronger effects on the group of other alternatives. That means its deletion forces the remaining alternatives to get smaller efficiency. The described idea in this paper is inspired of Lotfi and et al. (2011). One feature of the proposed method is that it does not need to determine the weight of the prior. Two examples are used to illustrate how the proposed method works in actual practices, and the results are compared with those obtained from the TOPSIS method.

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An extreme-distance approach to multiple criteria ranking

Cited by 29 publications

References 26 publications

An OWA-TOPSIS method for multiple criteria decision analysis

An OWA-TOPSIS method for multiple criteria decision analysis

Supporting Ill-Structured Negotiation Problems

A new DEA approach to rank alternatives in MCDA

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