نتایج جستجو برای: topsis and linmap decision making
تعداد نتایج: 16881474 فیلتر نتایج به سال:
Selecting the appropriate manufacturing machine is a very important and complex problem for firms which usually have to deal with both qualitative and quantitative criteria and involve different decision makers whose knowledge is often vague and imprecise. This paper proposes a peer-based modification to intuitionistic fuzzy multi-criteria group decision making with TOPSIS method (peer IF-TOPSI...
Forthcoming wireless environment is a fusion of numerous networks with diverse technologies deployed by individual operators. In such an environment, innovative network selection methodologies are required not only to provide “always best connected” service to mobile users but also to maximize network operator’s revenue. To fulfill such requirements, multiple attributes from each network are to...
the expansion of major cities regardless of the scientific planning principles has led to theemergence of negative urban development impacts in iran such as the degradation of natural resources andvarious forms of pollution. undoubtedly, urban rivers can enhance the ecological value of the earth to anoptimal extent. however, to benefit from these potentials, appropriate planning looks inevitabl...
Due to the increasing competition of globalization, selection of the most appropriate supplier is one of the key factors for asupply chain management’s success. Due to conflicting evaluations and insufficient information about the criteria, Intuitionisticfuzzy sets (IFSs) considered as animpressive tool and utilized to specify the relative importance of the criteria. The aim of this paper is to...
abstract— in this paper, a new extended method of multi criteria decision making based on fuzzy-topsis theory is introduced. fuzzy mcdm algorithm for determining the best choice among all possible choices when the data are fuzzy is also presented. using a new index leads to procedure for choosing fuzzy ideal and negative ideal solutions directly from the fuzzy data observed alternatives.in this...
Fuzzy TOPSIS algorithm is one of the common decision making techniques which is applied in many different decision situations. Because of its high-effectiveness under uncertainty conditions, management scientists always try to improve the algorithm. Based on fuzzy TOPSIS, many algorithms have been developed for different decision situations. In fact, the efficiency of the algorithm is an import...
Abstract The quantitative multiple criteria decision making methods are practical to use in decision support systems. In this paper we analyses the quantitative multiple criteria decision making methods and sensitivity analysis methods usage in decision support systems. Both species of these methods are strongly mathematically based. We take notice of sensitivity of these methods to initial dat...
The management of wineries for industrial red winemaking is limited by the capacity and availability fermentation tanks over harvest season. winemakers aim to optimize wine quality, fermentative maceration length, tank’s productive cycle simultaneously. Maceration in varietal production carried out until a specific sugar content (digging-out point) attained, finishing before alcoholic fermentat...
Multiple criteria decision making (MCDM) is widely used in ranking one or more alternatives from a set of available alternatives with respect to multiple criteria. Inspired by MCDM which are methods to systematically evaluate alternatives, we propose a new Fuzzy TOPSIS for evaluating outsourcing software companies both using subjective and objective weights. While crisp data are inadequate to m...
The supplier selection process attracted a lot of attention in the business management literature. This process takes into consideration several quantitative and qualitative variables and is usually modeled as a multi-attribute decision making (MADM) problem. A recognized shortcoming in the literature of classical MADM methods is that they don’t permit the identification of interdependencies am...
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