The outranking approach and the foundations of electre methods

Roy, Bernard

doi:10.1007/bf00134132

Cited by 1,249 publications

(268 citation statements)

References 17 publications

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“…However, it may be mentioned that, when the number of evaluation criteria increase, pairwise comparisons through AHP method can be tedious. In such cases, several other alternate computer-based approaches have been developed to deliver MCDM, or elements thereof, in a range of forms; e.g., ELECTRE III (Opperhuizen and Hutzinger 1982;Roy 1991); DEFINITE (Janssen and Herwijnen 1994); routines in IDRISI GIS (Eastman and Jiang 1995); ASSESS (Veitch and Bowyer 1996;Hill et al 2005); GIWIN (Ren 1997); MULINO-DSS (Giupponi et al 2004); HERO for heuristic multi-objective optimization (Kangas et al 2000); FORM (Kazana et al 2003); and MEACROS (Mazzetto and Bonera 2003). On the other hand, decision makers may not necessarily be proficient in computer science and information technology, and they may need appropriate tools in order to easily participate in the discussion.…”

Section: Discussionmentioning

confidence: 99%

Fire risk evaluation using multicriteria analysis—a case study

Vadrevu

Eaturu

Badarinath

2009

Environ Monit Assess

154

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Forest fires are one of the major causes of ecological disturbance and environmental concerns in tropical deciduous forests of south India. In this study, we use fuzzy set theory integrated with decision-making algorithm in a Geographic Information Systems (GIS) framework to map forest fire risk. Fuzzy set theory implements classes or groupings of data with boundaries that are not sharply defined (i.e., fuzzy) and consists of a rule base, membership functions, and an inference procedure. We used satellite remote sensing datasets in conjunction with topographic, vegetation, climate, and socioeconomic datasets to infer the causative factors of fires. Spatial-level data on these biophysical and socioeconomic parameters have been aggregated at the district level and have been organized in a GIS framework. A participatory multicriteria decision-making approach involving Analytical Hierarchy Process has been designed to arrive at a decision matrix that identified the important causative factors of fires. These expert judgments were then integrated using spatial fuzzy decision-making algorithm to map the forest fire risk. Results from this study were quite useful in identifying potential "hotspots" of fire risk, where forest fire protection measures can be taken in advance. Further, this study also demonstrates the potential of multicriteria analysis integrated with GIS as an effective tool in assessing "where and when" forest fires will most likely occur.

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Section: Discussionmentioning

confidence: 99%

Fire risk evaluation using multicriteria analysis—a case study

Vadrevu

Eaturu

Badarinath

2009

Environ Monit Assess

154

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“…In Analytic Hierarchy Process (AHP, Saaty, 1980), the interpretation of the weights is less clear (Belton, 1986, Salo and Hämäläinen, 1997. Outranking methods, such as ELECTRE (Roy, 1991) and PROMETHEE (Brans et al, 1986), focus on pairwise comparisons of alternatives and on outranking relations. PROMETHEE does not provide any guidelines to determine the weights and, for instance, AHP has been combined with PROMETHEE (Macharis et al, 2004).…”

Section: Multi-criteria Decision Analysis (Mcda)mentioning

confidence: 99%

Are objectives hierarchy related biases observed in practice? A meta-analysis of environmental and energy applications of Multi-Criteria Decision Analysis

Marttunen

Belton

Lienert

2018

European Journal of Operational Research

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Procedural and behavioural biases have received little attention in recent Multi-Criteria Decision Analysis (MCDA) research. Our literature review shows that most research on biases was done 15-30 years ago.This study focuses on biases that are introduced at an early stage of MCDA when building objectives hierarchies and their effect on the weights. The main objective is to investigate whether prior findings regarding such biases, which were mostly based on laboratory experiments, can be found in real-world applications. We conducted a meta-analysis of the objectives hierarchies and weight elicitation procedures in 61 environmental and energy MCDA cases. Relationships between the structural characteristics of the objectives hierarchy and assigned objectives' weights were analysed with statistical tests. Our main research questions were: (i) how does hierarchy size and structure affect the objectives' weights? (ii) how are weights distributed across economic, social and environmental objectives? (iii) is there support for the equalising bias? Our findings are mostly aligned with earlier research and suggest that the hierarchy structure and content can substantially influence weight distributions. For example, hierarchical weighting seems to be sensitive to the asymmetry bias, which can occur when a hierarchy has branches that differ in the number of sub-objectives. We found no evidence for the equalising bias. We highlight issues deserving more attention when developing objectives hierarchies and eliciting weights.The research demonstrates the potential to use meta-analysis, which has not previously been used in this way in the MCDA field, to learn from a collection of applications.

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“…In the first stage, the available CP instances are shortlisted on the basis of the user's minimum QoS and cost criteria, and in the second stage, migration cost and time are evaluated. After completing these stages, they use the Technique for Order of Preference by Similarity to Ideal Solution (TOP-SIS) [2] and ELimination Et Choix Traduisant la REalit (ELimination and Choice Expressing REality), commonly known as ELECTRE [28], to find the most appropriate migration suggestion. They demonstrate their approach using a case study example.…”

Section: Post-deployment Decision Makingmentioning

confidence: 99%