Fuzzy vectors were introduced as a description of imprecise quantities whose uncertainty originates from vagueness, not from a probabilistic model. Support functions are a classical tool for representation and computation with compact convex sets. The combination of these two techniques—support functions of fuzzy vectors—has been proposed by Puri and Ralescu. Independently, Bobylev proposed ano...

Within the framework of fuzzy logic for pattern recognition, we propose a neural network based membership function procedure. The estimate is obtained as the output of a multilayer network trained to minimize a fuzziness measure of the concept to be learnt. This method is shown to be able to deal with probabilistic, resolutional and fuzzy uncertainty. Fuzziness estimation is also made possible ...

A study on fuzzy relational visualization in system development aspects is presented. The front-end enables dynamic and scalable changes in visualization according to user’s expertise and inspiration. Integrative management of various data and knowledge is handled by the back-end at any scale in cloud computing environment. Extended Logic Programming is used as the core of fuzzy relational mana...

Fuzzy relation-based models for handling uncertainty (in a non-probabilistic way) in diagnosis problems have been introduced by Sanchez about twenty years ago, and since then applied and developed by several researchers. The paper first reviews the existing proposals, also including the association-based abductive model proposed by Reggia and his co-workers, which uses non-fuzzy relations. A ne...

Fuzzy set theory is primarily concerned with how to quantitatively deal with imprec ision and uncertainty, and offers the decision maker another tool in addition to the classical deterministic and probabilistic mathematical tools that a re used in modeling real-world problems. The present study investigates a fuzzy economic order quantity model for deteriorating items in which demand increases ...

Environmental risk assessment is an essential element in any decision-making process in order to minimize the effects of human activities on the environment. Unfortunately, often environmental data tends to be vague and imprecise, so uncertainty is associated with any study related with these kind of data. Essentially, uncertainty in risk assessment may have two origins – randomness and incompl...

In this study, a two-stage fuzzy robust integer programming (TFRIP) method has been developed for planning environmental management systems under uncertainty. This approach integrates techniques of robust programming and two-stage stochastic programming within a mixed integer linear programming framework. It can facilitate dynamic analysis of capacity-expansion planning for waste management fac...

The objectives of this paper are two folds. The first one is to improve the time forecasting produced from the well known Earned Value Management (EVM), using the polynomial function. The time prediction observed from the polynomial model, which is compared against that observed from the most common method for time forecasting (critical path method), is a more accurate (mean absolute percentage...

Stochastic groundwater modeling involves the propagation of probabilistic uncertainty from model input parameters to model estimates, usually via a Monte Carlo method. With the increasing reliance upon expert knowledge to define model inputs, and both fuzzy set and possibility theories to characterize this expert knowledge, alternative means of executing model equations are needed. While the fu...

This paper is on Fuzzy Stochastic Optimisation, an area that is quickly coming to the forefront of mathematical programming under uncertainty. An even stronger motivating factor for the growing interest in this area can be found in the ubiquitous nature of decision problems involving hybrid undeterminacy. More precisely, we consider a range of situations in which random factors and fuzzy inform...