Fuzzy regression analysis can be thought of as a fuzzy variation of classical regression analysis. It has been widely studied and applied in diverse areas. In general, the analysis of fuzzy regression models can be roughly divided into two categories. The 0rst is based on Tanaka’s linear-programming approach. The second category is based on the fuzzy least-squares approach. In this paper, new t...

In a recent paper, Jiménez et al. (2007) propose a ‘general’ and ‘interactive’ method for solving linear programming problems with fuzzy parameters. In this study, we propose a revision to the optimal crisp value of the objective function to eliminate the restrictive constraints imposed by Jiménez et al. (2007). The revised approach can be generalised to solve many real-world linear programming...

This paper introduces the notion of fuzzy process as a formalism for the idea of fuzzy contact between a device and its environment. The notions of absolute correctness and relative correctness are defined. In order to work with concurrency it has been built an approach to manipulate the interactive processes as a single process and the resulted behavior has been observed.

In this research, we address the application of uncertaintyprogramming to design a multi-site, multi-product, multi-period,closed-loop supply chain (CLSC) network. In order to make theresults of this article more realistic, a CLSC for a case study inthe iron and steel industry has been explored. The presentedsupply chain covers three objective functions: maximization ofprofit, minimization of n...

One of the new trends in genetic fuzzy systems (GFS) is the use of evolutionary multiobjective optimization (EMO) algorithms. This is because EMO algorithms can easily handle two conflicting objectives (i.e., accuracy maximization and complexity minimization) when we design accurate and compact fuzzy rule-based systems from numerical data. Since the main advantage of fuzzy rule-based systems co...

In this paper, we extend TOPSIS (Technique for Order Preference by Similarity Ideal Solution) for solving Large Scale Multiple Objective Programming problems involving fuzzy parameters. These fuzzy parameters are characterized as fuzzy numbers. For such problems, the α-Pareto optimality is introduced by extending the ordinary Pareto optimality on the basis of the α-Level sets of fuzzy numbers. ...

This paper presents a multi-objective optimization model for the design of a waste management system consisting of customers, transfer stations, landfills and collection vehicles. The developed model aims to simultaneously minimize the total costs, greenhouse gas emissions and the rates of energy consumption. To tackle the multiple objectives in the problem, we utilize an interactive fuzzy prog...