this paper proposes a family of robust counterpart for uncertain linear programs (lp) which is obtained for a general definition of the uncertainty region. the relationship between uncertainty sets using norm bod-ies and their corresponding robust counterparts defined by dual norms is presented. those properties lead us to characterize primal and dual robust counterparts. the researchers show t...

This paper considers a portfolio selection problem with type-2 fuzzy future returns involving ambiguous and subjectivity. Since this proposed problem is not well-defined due to fuzziness, introducing the fuzzy goal for the total future return and the degree of possibility, the main problem is transformed into the standard fuzzy programming problem including the secondary fuzzy numbers. Furtherm...

The handling of exceptions in multiclass problems is a tricky issue in inductive logic programming (ILP). In this paper we propose a new formalization of the ILP problem which accounts for default reasoning, and is encoded with first-order possibilistic logic. We show that this formalization allows us to handle rules with exceptions, and to prevent an example to be classified in more than one c...

Nowadays, the design of a strategic supply chain network under the incidence of disruption is regarded as one of the important priorities of governments. Supplying sustainable petrochemical products is considered as a strategic goal by managers who require reliable infrastructure design. Crisis conditions such as natural disasters and sanctions have a destructive effect on the raw materials and...

Classical stochastic Markov Decision Processes (MDPs) and possibilistic MDPs ( -MDPs) aim at solving the same kind of problems, involving sequential decision making under uncertainty. The underlying uncertainty model (probabilistic / possibilistic) and preference model (reward / satisfaction degree) change, but the algorithms, based on dynamic programming, are similar. So, a question maybe rais...

Optimization is a procedure of finding and comparing feasible solutions until no better solution can be found. It can be divided into several fields, one of which is the Convex Optimization. It is characterized by a convex objective function and convex constraint functions over a convex set which is the set of the decision variables. This can be viewed, on the one hand, as a particular case of ...

Abstract This paper addresses a multi-objective blood supply chain network design, considering economic and environmental aspects. The objective of this model is to simultaneously minimize operational cost its logistical carbon footprint. In order embed the uncertainty transportation costs, demand, capacity facilities emission, novel robust possibilistic-necessity optimization used regarding hy...