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Visual PROMETHEE User Manual (including tutorials)
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Complete Visual PROMETHEE software user manual with full description of the software, step by step tutorials and methodological notes.
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... The funding allocation from the SNAI is EUR 6.75 million. The Monti Dauni inner area is also located in the province of Foggia, and it includes the highest number of municipalities (29). It covers a surface of 1946.8 km 2 with a total population of 54,593 inhabitants. ...
... PROMETHEE is a family of MCDA outranking methods first developed by [25] and enables to rank a set of alternatives by performing their pairwise comparisons on each evaluation criterion [26]. PROMETHEE II [27] is applied in this research, because it enables the organisation of indicators into thematic dimensions; in this way, the inner areas are ranked according to an overall development score (net outranking flow) and are also evaluated with respect to each indicator and dimension separately (unicriterion net flow) [28,29]. PROMETHEE II is also very clear and simple in its structure based on users (e.g., researchers, decision-makers, opinion leaders, etc.), it enables to re-evaluate the results if new information becomes available (e.g., indicators and/or areas), it deals with uncertainty by the use of thresholds, and there is software available to implement the method, manage the information, and show the results in a clear and multi-perspective manner [30]. ...
... The application of PROMETHEE II is carried out by using the software "Visual PROMETHEE" Academic Edition [29]. PROMETHEE II starts from the following multiple criteria problem: ...
Inner areas are rural areas with low population density, distant from the main hotspots offering essential welfare services and endowed with significant environmental assets and cultural heritage. In Italy, their development is at the core of the national strategy for inner areas. Specific documents and program agreements were issued for each inner area and summarized the intervention themes and projects to implement.
However, when the inner areas are considered within a regional territorial ‘matrix’, further collective policy actions have to be identified, through comparison and in-depth analysis of their features and influence on development. Therefore, this research aims to identify and analyse the development needs and potentialities of the inner areas, for strengthening the national strategy and improving their recovery and resilience through collective policy actions.
The Multiple Criteria Decision Aiding (MCDA) method PROMETHEE II was applied as research methodology to four inner areas in Puglia region (southern Italy), using 43 indicators organized into seven thematic dimensions and setting specific parameters.
The results enabled to delineate the overall development score of the four inner areas, and the profile of each inner area based on every indicator and dimension. By analysing this profile, key thematic dimensions where to direct collective policy actions were identified, related mainly to contrast with the depopulation by improving specific essential services (e.g., digitalization, health, education) and to foster the development of agriculture, tourism, and cultural heritage.
This research can be considered a first step for future broader studies, to guide the process of policy making for the recovery and resilience of European and Italian inner areas with a multi-perspective approach.
... The FLAGs' CDPs should provide a description of the overall aims and objectives of the strategy through the application of the Axis 4 approach and project development and a description of the administrative and financial management, application, and approval procedures. It has to specify the project selection criteria that will be employed, detailed financial information concerning allocations, expenditures, etc., and the procedures for monitoring and evaluation (European Commission, 2014a;b). ...
... It provides support for decisionmakers to consider and evaluate, in a systematic way, different possibilities for resources allocation and to detect whether an optimal policy alternative (Turcksin et al., 2011). Promethee II application part has been carried out using the Visual Promethee Academic Edition software, version 1.4 (Mareschal, 2013). ...
... The weights assigned by the decision-maker are shown in Table 4. The weights are automatically normalised using the Visual Promethee software (Mareschal, 2013) so that their sum equals 1 (i.e. 100%). ...
Fishery planning and management require environmental, social, and economic assessment that should take into account multiple sustainability criteria as well as the preferences and priorities of decision-makers involved in these complex issues. Therefore, this research aims to identify in a straightforward way the most sustainable coastal development plan (CDP) among a set of several CDPs and to investigate in depth the effective sustainable use of the public fishery's fund among the considered CDPs. This is accomplished by implementing a multiple criteria decision aiding (MCDA) framework that has been tested by six fisheries’ local action groups (FLAGs) CDPs in a territorial system strongly affected by fishery activities in the Apulia region of Southern Italy. In particular, the framework was classified into 12 criteria grouped into three sustainability dimensions according to the actions identified by the CDPs; the data collection was carried out by consulting the CDPs. After collecting the data, the MCDA framework was applied by involving the regional consultant on fishery affairs. An analysis of the results clearly showed that the best performances in the distribution of the available funding were related to the coherence of the CDPs with specific environmental, social, and economic features. The proposed MCDA framework can represent support for decision-makers and stakeholders to consider and evaluate in a systematic and robust way different possibilities for funding distribution to detect optimal alternatives within European fishery policies.
... The application and description of the methods are presented in the following articles [22][23][24][25]. The method guide [26] was used in this article. ...
... There are several steps in the method [26], such as: defining a set of actions, defining the assessment criteria, creating a multicriteria table, determining preferences, performing calculations, and interpreting the results. It is assumed that A is a set of n actions A= {a1, a2, a3,… an} and a consistent family of criteria f1 to fk is F = {f1, f2, f3… fk}. ...
This paper presents a model for assessing the impact of various factors on maritime accidents. This paper discusses the issue of maritime transport and its risks. The taxonomy of causes and consequences used in maritime transport is explained. Two mathematical principles were used, i.e., multi-criteria decision analysis (MCDA) and principal component analysis (PCA). The analyses were carried out using the Promethee/Gaia method. The connection between causes and accidents constituted the decision problem. The evaluation criteria, a set of actions and preferences, were identified based on the method principles. The results of this research include rankings of causal chains. They were analysed in multi-criteria and single-criteria dimensions. The outcomes are presented numerically and graphically. Such research contributes to improving safety at sea. It allows us to understand how a particular transport system works. Conclusions can be drawn and measures can be initiated to change the situation in the future.
... There is a spectrum in this ranking model ranging from − 1 (or the lowest level of resiliency) to +1 (or the highest level of resiliency).As the score moves from − 1 to +1, the level of resilience improves and vice versa. PROMETHEE II is applied using the software "Visual PROMETHEE" Academic Edition (Mareschal, 2015). Many scholars have reported successful applications of the PROMETHEE II method, including in urban resilience (Carone et al., 2018;Ottomano Palmisano et al., 2022), vulnerability assessment (Peng, 2015) and seismic and flood risk assessments (Soldati et al., 2022). ...
... There is a spectrum in this ranking model ranging from − 1 (or the lowest level of resiliency) to +1 (or the highest level of resiliency).As the score moves from − 1 to +1, the level of resilience improves and vice versa. PROMETHEE II is applied using the software "Visual PROMETHEE" Academic Edition (Mareschal, 2015). Many scholars have reported successful applications of the PROMETHEE II method, including in urban resilience (Carone et al., 2018;Ottomano Palmisano et al., 2022), vulnerability assessment (Peng, 2015) and seismic and flood risk assessments (Soldati et al., 2022). ...
Assessing resilience at the community level is essential for evidence-based planning and policymaking. However, issues such as data availability and accessibility make community resilience assessment a challenging task. Development and implementation of resilience assessment models are particularly challenging in Global South cities that lack access to skilled personnel and updated databases. Using the Baseline Resilience Indicators for Communities (BRIC) model, developed for county-level resilience assessment in the United States, this article aims to create an urban district-level resilience index called District-based Baseline Resilience Indicators for Communities (DBRIC). We utilized DBRIC to measure the resilience of Tehran districts and examine how it has changed from 2006 to 2016. The selection of these years was primarily influenced by the data availability. The results show that 45.5% of the districts of Tehran experienced a decrease in the DBRIC index by up to -300%. The decline was particularly noteworthy in terms of social and community capital. Further, the results highlight relatively less resilient districts that require specific attention from planners and policymakers. The findings of the study can inform local planners and policymakers in their efforts towards enhancing resilience. They can also be of interest to policymakers in other Global South cities with similar conditions.
The objective of this paper is to develop a framework to overcome problems of soil nutrient legacies and poor quality of water bodies raised by agricultural practices. A multicriteria decision analysis (MCDA) approach is proposed to evaluate different alternatives to help to define sustainable vineyard agricultural solutions. The methodology developed was applied to a basin in the north-central part of Portugal, the São Lourenço basin. The land cover of the basin consists predominantly of vines. A number of alternatives based on different vineyard management practices and land use are evaluated against environmental and socioeconomic criteria by means of the soil and water assessment tool (SWAT) model and considering information included in some databases. The preference ranking organization method for enrichment evaluation (PROMETHEE) outranking method is used to rank the alternatives and to evaluate weight stability intervals assigned to criteria. A supplementary analysis is performed by a different MCDA method (technique for order of preference by similarity to ideal solution (TOPSIS)), belonging to the family of compensatory methods, to provide additional insights to the decision makers. The results show the best and the worst alternatives for vineyard agricultural practices according to the weights assigned to the criteria. The sensitivity analysis of the weights indicated most stable ranking cases and the criterion that presents the narrow stability intervals. This work offers the opportunity to discuss comparisons between different types of agricultural practices and give insights for future developments of other areas where intensive vineyard cultivation is implemented.
Abstract Following a request of the European Commission, EFSA and ANSES, beneficiary of the EFSA tasking grant on horizon scanning for plant pests (GP/EFSA/ALPHA/2017/02), developed a methodology to order by risk non‐regulated pests recently identified through the monitoring of media and scientific literature. The ranking methodology proposed at the end of the pilot phase was based on the scoring of pests under evaluation following 16 criteria related to the steps of the pest risk assessment scheme. The multicriteria matrix of scores obtained was then submitted to the multicriteria analysis method PROMETHEE. The pilot methodology was tested on a limited number of pests (14 pests identified during the monitoring activity, and 4 ‘control’ pests whose well‐known risk should be reflected either in a positive or negative score), then applied on all non‐regulated pests identified through the media and scientific literature monitoring in the first 2 years of the project. After having collected feedback from the targeted final users (EU risk managers), the methodology underwent a few refinements: (i) implementation of the methodology to a set of already assessed reference pests from EFSA opinions, (ii) exclusions of three criteria from the scoring phase, (iii) identification of pests proposed for further action (‘positive’ pests), using a threshold defined after scoring the reference pests.
Multicriteria decision-making methods are widely spread and used to assist the decision-makers to resolve problems. Many of the methods are simple to deploy (WSA, TOPSIS), which is an advantage and because of the computer boom, there is no problem with calculations. However, more sophisticated methods are evolving. The modelling of preferences is improved (from linear in WSA to Gaussian in PROMETHEE), multilevel decision-making (such as AHP) is extended to modelling of dependencies between individual criteria (ANP). The presented method, two-stage weighted PROMETHEE, combines the advantages of generalized preferential functions in PROMETHEE methods, unambiguous arrangement (PROMETHEE II) and hierarchical approach (AHP). In addition, this paper demonstrates the application of the method to evaluate the order of 14 regions of the Czech Republic in regard to economic indices such as the unemployment rate, economic activity, average age, wages, free working places, income, consumption and investments. Data are taken from the Czech Statistical Office web and include the years 2012–2019. In the first stage, the position of each region is calculated; in the second stage, all years mentioned are considered, including the aspect of the weighted time series. Result visualization is made possible using the Visual PROMETHEE software.
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