scholarly journals The hierarchical SMAA-PROMETHEE method applied to assess the sustainability of European cities

2021 ◽  
Author(s):  
Salvatore Corrente ◽  
Salvatore Greco ◽  
Floriana Leonardi ◽  
Roman Słowiński
Keyword(s):  
Value Function ◽  
Specific Information ◽  
Economic Sustainability ◽  
Preference Model ◽  
Preference Information ◽  
Promethee Ii ◽  
Decision Aiding ◽  
European Cities ◽  
Single Instance ◽  
Promethee Method

AbstractMeasuring the level of sustainability taking into account many contributing aspects is a challenge. In this paper, we apply a multiple criteria decision aiding framework, namely, the hierarchical-SMAA-PROMETHEE method, to assess the environmental, social, and economic sustainability of 20 European cities in the period going from 2012 to 2015. The application of the method is innovative for the following reasons: (i) it permits to study the sustainability of the mentioned cities not only comprehensively but also considering separately particular macro-criteria, providing in this way more specific information on their weak and strong points; (ii) the use of PROMETHEE and, in particular, of PROMETHEE II, avoids the compensation between different and heterogeneous criteria, that is arbitrarily assumed in value function aggregation models; finally, (iii) thanks to the application of the Stochastic Multicriteria Acceptability Analysis, the method provides more robust recommendations than a method based on a single instance of the considered preference model compatible with few preference information items provided by the Decision Maker.

Supporting Collaborative Multi-Criteria Evaluation

2009 ◽  
Vol 1 (4) ◽  
pp. 1-15 ◽  
Author(s):  
João N. Clímaco ◽  
João A. Costa ◽  
Luis C. Dias ◽  
Paulo Melo
Keyword(s):  
Value Function ◽  
Multicriteria Analysis ◽  
Remote Access ◽  
Decision Makers ◽  
Progressive Reduction ◽  
Coherent Interface ◽  
Decision Aiding ◽  
Multi Criteria Evaluation ◽  
Vip Analysis ◽  
Collaborative Evaluation

This article presents the VIP Analysis plug-in of Decision Deck 1.1, a platform that hosts different evaluation methods to support decision makers in the collaborative evaluation of alternatives in a multi-criteria and multi-experts setting. VIP Analysis is a tool for aggregation of multicriteria performances by means of an additive value function under imprecise information. It allows conducting a multicriteria analysis for selecting an alternative when the decision makers are not able to (or do not wish to) fix precise values for the importance parameters. These parameters are seen as variables that may take several values subject to constraints. VIP Analysis incorporates different methods to support the progressive reduction of the number of alternatives, introducing a concept of tolerance that lets decision makers use some of the methods in a more flexible manner. When compared with the original standalone VIP Analysis (programmed in the late 1990s) the main innovation of the VIP Analysis plug-in is to allow several users working on the same problem under different roles: coordinator, evaluator, and decision-maker, thus defining a workflow process and enabling concurrent and remote access to the data over a network. By being included in the Decision Deck platform, VIP Analysis is now integrated with other decision aiding methods within a coherent interface. A final advantage is that the platform is open-source, which facilitates customization and collaborative improvement of the software.

scholarly journals Review of PROMETHEE method in transportation

2021 ◽  
Vol 27 (1) ◽  
pp. 69-74
Author(s):  
Laila Oubahman ◽  
Szabolcs Duleba
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Evaluation Method ◽  
Scale Effects ◽  
Decision Makers ◽  
Preference Ranking ◽  
Promethee Ii ◽  
Partial Ranking ◽  
Complete Ranking ◽  
Promethee Method

Abstract In recent decades, decision support system has been constantly growing in the field of transportation planning. PROMETHEE (Preference Ranking Organization METHod for Enrichment Evaluation) method is an efficient decision-making support deployed in case of a finite number of criteria. It provides a partial ranking through PROMETHEE I and a complete ranking with PROMETHEE II. This outranking methodology is characterized by the elimination of scale effects between criteria and managing incomparability with the comprehensive ranking. However, PROMETHEE does not provide guidance to assign weights to criteria and assumes that decision makers are able to allocate weights. This review presents an overview of PROMETHEE models applied in transportation and points out the found gaps in literature.

The risk of decision making with incomplete criteria weight information

2006 ◽  
Vol 36 (1) ◽  
pp. 195-205 ◽  
Author(s):  
Annika Kangas
Keyword(s):  
Decision Problem ◽  
Value Function ◽  
Rank Order ◽  
Decision Support Tools ◽  
Preference Information ◽  
True Value ◽  
Support Tools ◽  
Criteria Weights ◽  
The Value Function

In many cases, it may be difficult to obtain explicit information on criteria weights for multicriteria decision analysis. Usually, however, at least the relevant criteria can be assumed to be known, even if their weights are not. In addition, complete or incomplete rank order of these criteria can be known, and it may be possible to obtain estimates for at least some of the value-function parameters. With some decision support tools, such as stochastic multicriteria acceptability analysis (SMAA), it is possible to use incomplete information. The main results of SMAA are the probabilities of certain alternative obtaining a given rank, given all the information available. These probabilities can be used for choosing the most recommendable alternative. However, recommendations are risky when the preference information is incomplete. In this study, the risks are studied through a simulation study based on a previous forestry decision problem with multiple criteria. (1) The probability that the best alternative is recommended and (2) the expected losses in the value of value function due to choosing the wrong alternative are modelled as a function of the characteristics of the true value function and the best alternative. The results show that the quality of decisions improves very quickly with improving information on weights. Determining at least the complete rank order of criteria is advisable, especially if the importances vary markedly among the criteria.

scholarly journals PROMETHEE-ROC Model for Assessing the Readiness of Technology for Generating Energy

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Danielle Costa Morais ◽  
Adiel Teixeira de Almeida ◽  
Luciana Hazin Alencar ◽  
Thárcylla Rebecca Negreiros Clemente ◽  
Ceres Zenaide Barbosa Cavalcanti
Keyword(s):  
Power Generation ◽  
Decision Problem ◽  
Decision Model ◽  
Significant Contribution ◽  
Strategic Decision ◽  
Relative Importance ◽  
Ranking Problem ◽  
Promethee Ii ◽  
Imprecise Information ◽  
Promethee Method

This paper puts forward a proposal for a multicriteria decision model for prioritizing technologies that are critical for power generation in the energy sector. It deals with the context of imprecise information regarding importance of criteria; then an integration of surrogate weights with the PROMETHEE method is undertaken in order to approach this context. In this type of strategic decision problem, how to deal with imprecise information is always a challenge. The use of surrogate weights presents a significant contribution and it can facilitate the assignment of weights in a decision ranking problem, which requires the decision-maker (DM) to order the criteria by their importance for the decision problem. Thus for this situation of assessing the readiness of technology for generating energy where the DM is able and feels comfortable to order all criteria by their relative importance, the proposed approach of surrogate weights in the PROMETHEE II method, the PROMETHEE-ROC model, is shown to be an adequate approach.

New hybrid AHP-QFD-PROMETHEE decision-making support method in the hesitant fuzzy environment: an application in packaging design selection

2021 ◽  
pp. 1-17
Author(s):  
Byanca Porto de Lima ◽  
Fernando Augusto Silva Marins ◽  
Aneirson Francisco da Silva
Keyword(s):  
Decision Making ◽  
Quality Function Deployment ◽  
Fuzzy Ahp ◽  
Decision Makers ◽  
Preference Ranking ◽  
Packaging Design ◽  
Promethee Ii ◽  
Promethee Method ◽  
Design Selection ◽  
Decision Making Support

This paper presents a new hybrid decision-making support method (New Hesitant Fuzzy AHP-QFD-PROMETHEE II Method), which jointly uses the Analytic Hierarchy Process (AHP), the Quality Function Deployment (QFD) and the Preference Ranking Method for Enrichment Evaluation (PRO-METHEE II), as well as the Hesitant Fuzzy Linguistic Term Sets (HFLTS) to capture hesitation and aggregate divergent opinions from different experts. A real application of the new method to a packaging design selection problem for an automotive company is described, finding that AHP assisted in determining the importance of QFD’s customer requirements (CRs) and PROMETHEE II was used to select the best packaging design. With this same problem, for the purpose of validating the proposed method, a comparative analysis was made with the use of the Hesitant Fuzzy AHP-QFD-TOPSIS method and also with the traditional AHP-QFD-PROMETHEE method, which makes it impossible to capture the hesitation of decision makers. The result showed similarity in the rankings of design alternatives found in the three methods application. The proposed method proved advantageous for solving problems that can generally be solved with the QFD House of Quality but have serious difficulties when decision makers have divergent opinions and hesitate in evaluating criteria and alternatives.

scholarly journals A new approach for criteria weight elicitation of the ARAS-H method

2021 ◽  
Vol 16 ◽  
pp. 89-109
Author(s):  
Maroua Ghram ◽  
◽  
Hela Moalla Frikha ◽  
Keyword(s):  
Mathematical Programming ◽  
Decision Making Process ◽  
Linear Programs ◽  
Programming Technique ◽  
New Approach ◽  
Preference Information ◽  
Decision Aiding ◽  
Criteria Weights ◽  
Preference Disaggregation ◽  
Elicitation Procedure

Criteria weight inference is a crucial step for most of multi-criteria methods. However, criteria weights are often determined directly by the decision-maker (DM) which makes the results unreliable. Therefore, to overcome the imprecise weighting, we suggest the use of the preference programming technique. Instead of obtaining criteria weights directly from the DM, we infer them in a more objective manner to avoid the subjectivity and the unreliability of the results. Our aim is to elicit the ARAS-H criteria weights at each level of the hierarchy tree via mathematical programming, taking into account the DM’s preferences. To put it differently, starting from preference information provided by the DM, we proceed to model our constraints. The ARAS-H method is an extension of the classical ARAS method for the case of hierarchically structured criteria. We adopt a bottom-up approach in order to elicit ARAS-H criteria weights, that is, we start by determining the elementary criteria weights (i.e. the criteria at the lowest level of the hierarchy tree). The solution of the linear programs is obtained using LINGO software. The main contribution of our criteria weight elicitation procedure is in overcoming imprecise weighting without excluding the DM from the decision making process. Keywords: Multiple Criteria Decision Aiding, preference disaggregation, ARAS-H, criteria weights, mathematical programming.

scholarly journals SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN FINALIS DUTA BAHASA SUMATERA UTARA DENGAN MENGGUNAKAN METODE PROMETHEE II (STUDI KASUS: BALAI BAHASA SUMATERA UTARA)

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Cristella Sitinjak ◽  
Nelly Astuti Hasibuan ◽  
Rian Syahputra
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Selection Process ◽  
Promethee Ii ◽  
The North ◽  
Promethee Method ◽  
Final Score ◽  
North Sumatra ◽  
Selection Of

The process of selecting the North Sumatra Ambassador Language Finalists at the North Sumatra Language Hall is still subjective in that the assessment may not be based on established criteria or without looking objectively so that the selection process is carried out less precisely and the resulting decision is less satisfactory for the participants of the Ambassador Finalists North Sumatra Language. In this study, the authors used a decision support system with the Promethee II method to find the value of weights and criteria, and to find the final score or to find the finalist ranking of the North Sumatra Language Ambassador. Thus the Decision Support System is needed in order to help the North Sumatra Language Office in determining the Finalists of the North Sumatra Language Ambassador. To overcome these problems, it is necessary to build a decision support system application for the selection of North Sumatra Language Ambassador Finalists using the Promethee II method so that it can help and facilitate the North Sumatra Language Hall in making a decision to choose the North Sumatra Language Ambassador and the resulting decision is satisfactory.Keywords: Decision Support System, Language Ambassadors, Promethee Method ii

scholarly journals Multicriteria Correlation Preference Information (MCCPI)-Based Ordinary Capacity Identification Method

Mathematics ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 300 ◽  
Author(s):  
Jian-Zhang Wu ◽  
Yi-Ping Zhou ◽  
Li Huang ◽  
Jun-Jie Dong
Keyword(s):  
Decision Making ◽  
Decision Aid ◽  
Higher Order ◽  
Decision Criteria ◽  
Preference Information ◽  
Decision Aiding ◽  
Identification Method ◽  
Explicit Information ◽  
Dominance Relationships ◽  
Decision Alternatives

Multicriteria correlation preference information (MCCPI) refers to a special type of 2-dimensional explicit information: the importance and interaction preferences regarding multiple dependent decision criteria. A few identification models have been established and implemented to transform the MCCPI into the most satisfactory 2-additive capacity. However, as one of the most commonly accepted particular type of capacity, 2-additive capacity only takes into account 2-order interactions and ignores the higher order interactions, which is not always reasonable in a real decision-making environment. In this paper, we generalize those identification models into ordinary capacity cases to freely represent the complicated situations of higher order interactions among multiple decision criteria. Furthermore, a MCCPI-based comprehensive decision aid algorithm is proposed to represent various kinds of dominance relationships of all decision alternatives as well as other useful decision aiding information. An illustrative example is adopted to show the proposed MCCPI-based capacity identification method and decision aid algorithm.

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