Interval-valued intuitionistic fuzzy number uses membership degree and non-member ship degree to express a decision-maker’s hesitation, which make decision-maker express his/her opinion in uncertainty decision-making environment easily. The comparison between some intervalvalued intuitionistic fuzzy numbers can not be undertaken because of its limitation. In application such as weak consistency...

This paper proposes a compromise model, based on a new method, to solve the multi-objective large-scale linear programming (MOLSLP) problems with block angular structure involving fuzzy parameters. The problem involves fuzzy parameters in the objective functions and constraints. In this compromise programming method, two concepts are considered simultaneously. First of them is that the optimal ...

disasters can cause many casualties and considerable destruction mainly because of ineffective preventive measures, incomplete preparedness, and weak relief logistics systems. after catastrophic events happen, quick and effective response is of great importance, so as to having an efficient logistic plan for distributing needed relief commodities efficiently and fairly among affected people. in...

Cloud computing is a technology that uses the internet and central remote servers to keep up data and applications. As becomes more mature, many organizations and individuals are attracted in storing more accessible data e.g. personal data files, company related information in the cloud. This technology allows for much more efficient computing by centralizing storage, memory, processing and ban...

in this paper, the researchers present a multi-objective linear fractional inventory model using fuzzy pro-gramming. the proposed method in this paper is applied to a problem with two objective functions. the re-sulted fuzzy model is transformed into an ordinary linear programming. the implementation of the develop-ing model presented in this paper is demonstrated through the use of some numeri...

Multi-label classification has attracted much attention in the machine learning community to address problem of assigning single samples more than one (not necessarily non-overlapping) class at same time. We propose an evolving multi-label fuzzy classifier (EFC-ML) which is able self-adapt and self-evolve its structure with new incoming incremental, single-pass manner. It based on a multi-outpu...

On the problem of distributed multi-sensor multi-target tracking, a new optimal assignment model on the basis of fuzzy data correlation is presented. The new algorithm calculates the fuzzy marginal correlation probability between measurement and target track by using fuzzy integrated similarity degree under multiple-to-multiple feasible rule, and determines the point-track correlation matches b...

System reliability optimization is often faced with imprecise and conflicting goals such as reducing the cost of the system and improving the reliability of the system. The decision making process becomes fuzzy and multi-objective. In this paper, we formulate the problem as a fuzzy multi-objective nonlinear program (FMOOP). A fuzzy multiobjective genetic algorithm approach (FMGA) is proposed fo...

We present a new novel method of automatically generating a multi-variable fuzzy inference system from given sample sets. We rst decompose the sample set, say , into a cluster of sample sets associated with the given input variables, then compute the associated fuzzy rules and membership functions for each variable, independent of the other variables, by solving a single input multiple outputs ...