Informatica

In order to compete in the global environment, a manufacturing company has to keep developing new technologies. Selection of a right technology is a critical stage in a successful technology transfer process. However, technology selection is a complex multi-dimensional problem including both qualitative and quantitative factors, such as human resources, operational and financial dimensions, which may be in conflict and may also be uncertain. In addition, interdependent relationships exist among various dimensions as well as criteria of technology selection. The identified problems could be solved by combining multiple criteria decision making (MCDM) methods of different nature and fuzzy set theory. The objective of the current paper is to develop a complex approach to evaluate technologies and to rank their appropriateness for a company. A hybrid model is proposed, based on Fuzzy Analytic Network Process (FANP) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS). A real-life case study is presented to validate the proposed model.

The purpose of this study is to apply the method of hybrid multiple criteria decision making (MCDM) to select public relations personnel for tourism industry in Taiwan. Fuzzy Delphi method, which can lead to better criteria selection, is used to modify criteria. Considering the interdependence among the selection criteria, analytic network process (ANP) is then used to obtain the weights of them. To avoid calculation and additional pairwise comparisons of ANP, technique for order preference by similarity to ideal solution (TOPSIS) is used to rank the alternatives. The use of a combination of fuzzy Delphi method, ANP and TOPSIS, proposing a MCDM model for public relations personnel selection, and applying these to a real case is the unique features of this study.

The concept of stratified graph introduces some method of representation which can be embedded with an interpretation mechanism in order to obtain objects from some knowledge domain based on the considered symbolic graph-based representations. As it was defined in the literature, the inference process uses the paths of the stratified graphs, an order between the elementary arcs of a path and some results of universal algebras. The order is defined by considering a structured path instead of a regular path. In a previous paper the concept of system of knowledge representation was defined. It includes a stratified graph G, a partial algebra Y of objects, an injective mapping that embeds the nodes of G into objects of Y and a set of algorithms that takes pairs of objects from Y to get some other object of Y. In this paper the inference process defined for such a system of knowledge considers the interpretation of the symbolic elements of a stratified graph as formal language constructions. The concepts introduced in this paper can initiate a possible research line concerning the automatic generation mechanism for formal languages.

A secure and high-quality operation of power grids requires frequency to be managed to keep it stable around a reference value. The deviation of the frequency from this reference value is caused by the imbalance between the active power produced and consumed. In the Smart Grid paradigm, the balance can be achieved by adjusting the demand to the production constraints, instead of the other way round. In this paper, an swarm intelligence-based approach for frequency management is proposed. It is grounded on the idea that a swarm is composed of decentralised individual agents (particles) and that each of them interacts with other ones via a shared environment. Three swarm intelligence-based policies ensure a decentralised frequency management in the smart power grid, where agents of swarm are making decisions and acting on the demand side. Policies differ in behaviour function of agents. Finally, these policies are evaluated and compared using indicators that point out their advantages.

An effective way for managing and controlling a large number of inventory items or stock keeping units (SKUs) is the inventory classification. Traditional ABC analysis which based on only a single criterion is commonly used for classification of SKUs. However, we should consider inventory classification as a multi-criteria problem in practice. In this study, a new method of Evaluation based on Distance from Average Solution (EDAS) is introduced for multi-criteria inventory classification (MCIC) problems. In the proposed method, we use positive and negative distances from the average solution for appraising alternatives (SKUs). To represent performance of the proposed method in MCIC problems, we use a common example with 47 SKUs. Comparing the results of the proposed method with some existing methods shows the good performance of it in ABC classification. The proposed method can also be used for multi-criteria decision-making (MCDM) problems. A comparative analysis is also made for showing the validity and stability of the proposed method in MCDM problems. We compare the proposed method with VIKOR, TOPSIS, SAW and COPRAS methods using an example. Seven sets of criteria weights and Spearman’s correlation coefficient are used for this analysis. The results show that the proposed method is stable in different weights and well consistent with the other methods.

Recently, XML has achieved the leading role among languages for data representation and, thus, the amount of related technologies and applications exploiting them grows fast. However, only a small percentage of applications is static and remains unchanged since its first deployment. Most of the applications change with newly coming user requirements and changing environment. In this paper we describe a framework and a methodology for management of evolution and change propagation throughout XML applications. We also describe its proof-of-concept implementation called eXolutio, which has been developed and improved in our research group during last few years. The text should help the reader to get acquainted with the target area of XML evolution and the approach we have proposed and implemented.

In this paper, we propose a new aggregation operator under uncertain pure linguistic environment called the induced uncertain pure linguistic hybrid averaging aggregation (IUPLHAA) operator. Some of the main advantages and properties of the new operator are studied. Moreover, in the situations where the given arguments about all the attribute weights, the attribute values and the expert weights are expressed in the form of linguistic labels variables, we develop an approach based on the IUPLHAA operator for multiple attribute group decision making with uncertain pure linguistic environment. Finally, an illustrative example is given to verify the developed approach and to demonstrate its feasibility and practicality.

This paper shows a few novel calculations for wind speed estimation, which is focused around soft computing. The inputs of to the estimators are picked as the wind turbine power coefficient, rotational rate and blade pitch angle. Polynomial and radial basis function (RBF) are applied as the kernel function of Support Vector Regression (SVR) technique to estimate the wind speed in this study. Instead of minimizing the observed training error, SVR_poly and SVR_rbf attempt to minimize the generalization error bound so as to achieve generalized performance. The results are compared with the adaptive neuro-fuzzy (ANFIS) results.

The Generalized Traveling Salesman Problem is one of a well known complex combinatorial optimization problems. Equality-Generalized Traveling Salesman Problem is a particular case of it. The main objective of the problem it is to find a minimum cost tour passing through exactly one node from each cluster of a large-scale undirected graph. Multi-agent approaches are successfully used nowadays for solving real life complex problems. The aim of the current paper is to illustrate some agent-based algorithms, including particular ant-based models and virtual robots-agents with specific properties for solving Equality-Generalized Traveling Salesman Problem.

This paper investigates group decision making problems in which the criterion values take the form of interval-valued intuitionistic uncertain linguistic numbers (IIULNs). First, some additive operational laws of IIULNs are defined. Subsequently, some new arithmetic aggregation operators, such as the interval-valued intuitionistic uncertain linguistic weighted averaging (IIULWA) operator, interval-valued intuitionistic uncertain linguistic ordered weighted averaging (IIULOWA) operator and interval-valued intuitionistic uncertain linguistic hybrid aggregation (IIULHA) operator, are proposed which are based on the operational laws. Furthermore, an approach to group decision making with interval-valued intuitionistic uncertain linguistic information is developed, which is based on the IIULWA and IIULHA operators. Finally, an illustrative example is provided to demonstrate the feasibility and effectiveness of the proposed method.