Analysis of the potentials of multi criteria decision analysis methods to conduct sustainability assessment

Sustainability assessments require the management of a wide variety of information types, parameters and uncertainties. Multi criteria decision analysis (MCDA) has been regarded as a suitable set of methods to perform sustainability evaluations as a result of its flexibility and the possibility of facilitating the dialogue between stakeholders, analysts and scientists. However, it has been reported that researchers do not usually properly define the reasons for choosing a certain MCDA method instead of another. Familiarity and affinity with a certain approach seem to be the drivers for the choice of a certain procedure. This review paper presents the performance of five MCDA methods (i.e. MAUT, AHP, PROMETHEE, ELECTRE and DRSA) in respect to ten crucial criteria that sustainability assessments tools should satisfy, among which are a life cycle perspective, thresholds and uncertainty management, software support and ease of use. The review shows that MAUT and AHP are fairly simple to understand and have good software support, but they are cognitively demanding for the decision makers, and can only embrace a weak sustainability perspective as trade-offs are the norm. Mixed information and uncertainty can be managed by all the methods, while robust results can only be obtained with MAUT. ELECTRE, PROMETHEE and DRSA are non-compensatory approaches which consent to use a strong sustainability concept, accept a variety of thresholds, but suffer from rank reversal. DRSA is less demanding in terms of preference elicitation, is very easy to understand and provides a straightforward set of decision rules expressed in the form of elementary “if … then …” conditions. Dedicated software is available for all the approaches with a medium to wide range of results capability representation. DRSA emerges as the easiest method, followed by AHP, PROMETHEE and MAUT, while ELECTRE is regarded as fairly difficult. Overall, the analysis has shown that most of the requirements are satisfied by the MCDA methods (although to different extents) with the exclusion of management of mixed data types and adoption of life cycle perspective which are covered by all the considered approaches

Impute the Missing Data through Fuzzy Expert System for the Medical Data Diagnosis

Data Processing with missing attribute values based on fuzzy sets theory. By matching attribute-value pairs among the same core or reduce of the original data set, the assigned value preserves the characteristics of the original data set. Malaria represents major public health problems in the tropics. The harmful effects of malaria parasites to the human body cannot be underestimated. In this paper, a fuzzy expert system for the management of malaria (FESMM) was presented for providing decision support platform to malaria researchers, The fuzzy expert system was designed based on clinical observations, medical diagnosis and the expert�s knowledge. We selected 15 cases with Malaria and computed the missing results that were in the range of common attribute element by the domain experts

Dominance-based Rough Set Approach, basic ideas and main trends

Dominance-based Rough Approach (DRSA) has been proposed as a machine learning and knowledge discovery methodology to handle Multiple Criteria Decision Aiding (MCDA). Due to its capacity of asking the decision maker (DM) for simple preference information and supplying easily understandable and explainable recommendations, DRSA gained much interest during the years and it is now one of the most appreciated MCDA approaches. In fact, it has been applied also beyond MCDA domain, as a general knowledge discovery and data mining methodology for the analysis of monotonic (and also non-monotonic) data. In this contribution, we recall the basic principles and the main concepts of DRSA, with a general overview of its developments and software. We present also a historical reconstruction of the genesis of the methodology, with a specific focus on the contribution of Roman S{\l}owi\'nski.Comment: This research was partially supported by TAILOR, a project funded by European Union (EU) Horizon 2020 research and innovation programme under GA No 952215. This submission is a preprint of a book chapter accepted by Springer, with very few minor differences of just technical natur

A rule based approach to classification of EEG datasets: a comparison between ANFIS and rough sets

This paper compares two different rule based classification methods in order to evaluate their relative efficiacy with respect to classification accuracy and the caliber of the resulting rules. Specifically, the application of Adaptive Neuro-Fuzzy Inference System (ANFIS) and rough sets were deployed on a complete dataset consisting of electroencephalogram (EEG) data. The results indicate that both were able to classify this dataset accurately and the number of rules were similar in both cases, provided the dataset was pre-processed using PCA in the case of ANFIS

Criteria Uncertainty in Multiple Criteria Decision Analysis of Sustainable Manufacturing Systems

Multiple Criteria Decision Analysis (MCDA) is a discipline used by decision makers to evaluate conflicting features when choosing among alternatives. MCDA methods are applied in the field of sustainable manufacturing to weigh the importance of traditional criteria when compared to sustainability indicators. However, a recurring issue in MCDA is the uncertainty in the assessments of alternatives. In this project, a novel framework to deal with uncertainty in MCDA has been developed. It uses scenario planning to get optimistic and pessimistic assessments for the different alternatives. Then, assigning probabilities to the scenarios and applying COPRAS-N, an introduced modification of COPRAS-G, 11 weighted scenarios are calculated. Finally, the relative significance and ranking of each alternative are graphed according to the weighted scenarios so that their evolution and the different situations are represented. With the presented approach, internal and external uncertainties can be dealt with at the same time. The final decision is made by analysing the graphics and results and, if necessary, looking at the concepts of expected scenario and average performance introduced in this project. The framework has been applied to 3 case studies with a focus on sustainability found in the literature. The results show that providing a final ranking of alternatives without considering other likely scenarios may lead to wrong decisions. In fact, in Case study 1, the choice of the best alternative would have changed if the developed framework had been applied. Representing all the scenarios has proved to ensure the final decision and enable to evaluate all the possible outcomes, solving in this way the uncertainty.Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructur