Informatica

In order to survive in the present day global competitive environment, it now becomes essential for the manufacturing organizations to take prompt and correct decisions regarding effective use of their scarce resources. Various multi-criteria decision-making (MCDM) methods are now available to help those organizations in choosing the best decisive course of actions. In this paper, the applicability of weighted aggregated sum product assessment (WASPAS) method is explored as an effective MCDM tool while solving eight manufacturing decision making problems, such as selection of cutting fluid, electroplating system, forging condition, arc welding process, industrial robot, milling condition, machinability of materials, and electro-discharge micro-machining process parameters. It is observed that this method has the capability of accurately ranking the alternatives in all the considered selection problems. The effect of the parameter λ on the ranking performance of WASPAS method is also studied.

In multi criteria Decision Making, the decision maker wants to find the best alternative among a set of alternatives in order to satisfy a set of criteria. Traditionally, decision making models are based on crisp data. The shortcoming of these data in capturing the reality and lack of information persuaded researchers to develop decision making methods with uncertain data. In this paper, the ELECTRE method is extended with black numbers, under ambiguous environment. The proposed method is applied in a supplier selection problem. It's an outstanding method that can be used in real world problems with ill-defined and incomplete data.

The paper deals with a parallel processor scheduling problem with changeable job values. Contrary to other papers in this area, we assume that job values are characterized by a non-monotonic stepwise functions of job completion times (previously only non-increasing functions have been considered). We give examples of real-life systems that can be modelled in such a way, in order to show that the problem is interesting from practical point of view. The problem is shown to be NP-hard and a pseudo-polynomial time algorithm for its special case is constructed. Moreover, a number of heuristic algorithms is provided and experimentally tested.

Lithuanian vowel and semivowel phoneme modelling framework is proposed. Using this framework, the phoneme signal is described as the output of a linear multiple-input and single-output (MISO) system. The MISO system is a parallel connection of single-input and single-output (SISO) systems whose input impulse amplitudes vary in time. Within this framework two synthesis methods are proposed: harmonic and formant. The synthesized sounds obtained by the harmonic synthesis method are compared with those obtained by the formant method. Application of this modelling framework to all of Lithuanian vowel and semivowel synthesis gives naturally sounding result.

The comminution process, particularly grinding, is very important in the mineral processing industry. Some characteristics of ore particles, which occur as a product of grinding process, have a significant impact on the effects of further ore processing. At the same time, this process requires a significant amount of energy which significantly affects the overall processing costs. Therefore, in this paper, we propose new multiple criteria decision making model, based on the Ratio system part of the MOORA method, which should enable an efficient selection of the adequate comminution circuit design.

Nowadays data mining algorithms are successfully applying to analyze the real data in our life to provide useful suggestion. Since some available real data is multi-valued and multi-labeled, researchers have focused their attention on developing approaches to mine multi-valued and multi-labeled data in recent years. Unfortunately, there are no algorithms can discretize multi-valued and multi-labeled data to improve the performance of data mining. In this paper, we proposed a novel approach to solve this problem. Our approach is based on a statistical-based discretization metric and the simulated annealing search algorithm. Experimental results show that our approach can effectively improve the performance of the-state-of-art multi-valued and multi-labeled classification algorithm.

This paper presents an adaptive image-watermarking technique based on just-noticeable distortion (JND) profile and fuzzy inference system (FIS) optimized with genetic algorithm (GA). Here it is referred to as the AIWJFG technique. During watermark embedding, it embeds a watermark into an image by referring the JND profile of the image so as to make the watermark more imperceptible. It employs image features and local statistics in the construction of an FIS, and then exploits the FIS to extract watermarks without original images. In addition, the FIS can be further optimized by a GA to improve its watermark-extraction performance remarkably. Experimental results demonstrate that the AIWJFG technique not only makes the embedded watermarks further imperceptible but also possesses adaptive and robust capabilities to resist on image-manipulation attacks being considered in the paper.

Trust is an important factor for successful e-commerce and e-media applications. However, these media inherently disable many ordinary communication channels and means, and affect trust forming factors. Therefore cyber environment requires additional support when it comes to trust. This is also one key reason why computational trust management methods are being developed now for some fifteen years, while another key reason is to enable better decision making through mathematical modeling and simulations in other areas. These methods are grounded on certain premises, which are analyzed in this paper. On this basis, Qualitative assessment dynamics (QAD for short) is presented that complements the above methods. As opposed to other methods, it is aligned with certain principles of human reasoning. Therefore it further extends the scope of other computational trust management technologies that are typically concerned with artificial ways of reasoning, while QAD gives a basis also for applications in ordinary environments where humans are involved. By using this methodology, experimental work will be presented, applied to the area of organizations and human factor management.

In the paper we propose a genetic algorithm based on insertion heuristics for the vehicle routing problem with constraints. A random insertion heuristic is used to construct initial solutions and to reconstruct the existing ones. The location where a randomly chosen node will be inserted is selected by calculating an objective function. The process of random insertion preserves stochastic characteristics of the genetic algorithm and preserves feasibility of generated individuals. The defined crossover and mutation operators incorporate random insertion heuristics, analyse individuals and select which parts should be reinserted. Additionally, the second population is used in the mutation process. The second population increases the probability that the solution, obtained in the mutation process, will survive in the first population and increase the probability to find the global optimum. The result comparison shows that the solutions, found by the proposed algorithm, are similar to the optimal solutions obtained by other genetic algorithms. However, in most cases the proposed algorithm finds the solution in a shorter time and it makes this algorithm competitive with others.