Article history: Received November 4 2015 Received in Revised Format December 21 2015 Accepted February 25 2016 Available online February 25 2016 In this paper, a novel multi-objective robust possibilistic programming model is proposed, which simultaneously considers maximizing the distributive justice in relief distribution, minimizing the risk of relief distribution, and minimizing the total ...

Possibility theory offers either a qualitative, or a numerical framework for representing uncertainty, in terms of dual measures of pos­ sibility and necessity. This leads to the ex­ istence of two kinds of possibilistic causal graphs where the conditioning is either based on the minimum, or on the product opera­ tor. Benferhat et al. [3] have investigated the connections between min-based grap...

The problem of merging multiple-source uncertain information is a crucial issue in many applications. This paper proposes an analysis of possibilistic merging operators where uncertain information is encoded by means of product-based (or quantitative) possibilistic networks. We first show that the product-based merging of possibilistic networks having the same DAG structures can be easily achie...

Possibilistic logic is a weighted logic introduced and developed since the mid-1980s, in the setting of arti(cial intelligence, with a view to develop a simple and rigorous approach to automated reasoning from uncertain or prioritized incomplete information. Standard possibilistic logic expressions are classical logic formulas associated with weights, interpreted in the framework of possibility...

The analysis and processing of large data are a challenge for researchers. Several approaches have been used to model these complex data, and they are based on some mathematical theories: fuzzy, probabilistic, possibilistic, and evidence theories. In this work, we propose a new unsupervised classification approach that combines the fuzzy and possibilistic theories; our purpose is to overcome th...

Ranking functions are qualitative degrees of uncertainty ascribed to events charged by uncertainty and taking as their values non-negative integers in the sense of ordinal numbers. Introduced are ranking functions induced by real-valued possibilistic measures and it is shown that different possibilistic measures with identical ranking functions yield the same results when applied in decision pr...

This paper presents a novel approach for performance appraisal and ranking of decision-making units (DMUs) with two-stage network structure in the presence imprecise vague data. In order to achieve this goal, data envelopment analysis (DEA) model, adjustable possibilistic programming (APP), chance-constrained (CCP) are applied propose new fuzzy (FNDEA) approach. The main advantages proposed FND...

In standard possibilistic logic, prioritized in­ formation are encoded by means of weighted knowledge bases. This paper proposes an exten­ sion of possibilistic logic to deal with partially ordered information which can be viewed as a family of possibilistic knowledge bases. We show that all basic notions of possibilis­ tic logic have natural counterparts when dealing with partially ordered inf...