IDS-Previous conferences                                  In memory of late Prof. Lotfi Zadeh   

Keynote speakers

Prof. M. M. Zahedi

Shahid Bahonar University, Kerman, Iran

Head of the High Commission on Education and Research of the Parliament.

He is the invited speaker for the opening of the conference.


Prof. Bernard De Baets

KERMIT, Ghent University, Belgium


 Monotonicity, an overlooked property in data science



In many modelling problems, there exists a monotone relationship between one or more of the input variables and the output variable, although this may not always be fully the case in the observed input-output data due to data imperfections. Monotonicity is also a common property of evaluation and selection procedures. In contrast to a local property such as continuity, monotonicity is of a global nature and any violation of it is therefore simply unacceptable. We explore several problem settings where monotonicity matters, including fuzzy modelling, machine learning and decision making. Central to the above three settings is the cumulative approach, which matches nicely with the monotonicity requirement.

By far the most popular fuzzy modelling paradigm, despite its weak theoretical foundations, is the rule-based approach of Mamdani and Assilian. In numerous applied papers, authors innocently assume that given a fuzzy rule base that appears monotone at the linguistic level, this will be the case for the generated input-output mapping as well. Unfortunately, this assumption is false, and we will show how to counter it. Moreover, we will show that an implication-based interpretation, accompanied with a cumulative approach based on at-least and/or at-most quantifiers, might be a much more reasonable alternative.

Next, we deal with a particular type of classification problem, in which there exists a linear ordering on the label set (as in ordinal regression) as well as on the domain of each of the features. Moreover, there exists a monotone relationship between the features and the class labels. Such problems of monotone classification typically arise in a multi-criteria evaluation setting. When learning such a model from a data set, we are confronted with data impurity in the form of reversed preference. We present the Ordinal Stochastic Dominance Learner framework, which permits to build various instance-based algorithms able to process such data.

Finally, we explore a pairwise preference setting where each stakeholder expresses his/her preferences in the shape of a reciprocal relation that is monotone w.r.t. a linear order on the set of alternatives. The goal is to come up with an overall monotone reciprocal relation reflecting `best' the opinions. We formulate the problem as an optimization problem, where the aggregated linear order is that for which the implied stochastic monotonicity conditions are closest to being satisfied by the distribution of the input monotone reciprocal relations. Interesting links with social choice will be pointed out.


Prof. Janusz Kacprzyk

Polish academy of science, Poland.


Prof. Mohammad-R. Akbarzadeh

University of Mashhad


Prof. R. Farzipoor Saen

Adjunct Professor, Maastricht School of Management, Netherlands

Visiting Professor of Nottingham Trent University

Full Professor of Islamic Azad University, Karaj Branch



How to assess sustainability of suppliers in the presence of dual-role factor and volume discounts? A data envelopment analysis approach



Sustainability factors play critical role for long-term achievement of a supply chain management and purchasing process becomes more complicated with social and environmental pressures. Managing supplier selection process is a necessary step for companies seeking to manage their corporate legitimacy and reputations. Data envelopment analysis (DEA) has been widely used for supplier selection problems. In this speech, we propose a new super-efficiency method for evaluating sustainability of suppliers in presence of dual-role factors and volume discounts. We show that enhanced Russell model (ERM) fails to present a complete ranking of suppliers. Our new model presents a complete ranking and also preserves properties of ERM. Capabilities of our proposed method are shown using a couple of examples. A case study is presented to illustrate our proposed approach. The proposed method is used to select the best sustainable suppliers.


Prof. R. A. Borzooei

Shahid Beheshti University, Iran


Prof. Javier Montero

Complutense University of Madrid, Spain.