Informatica

This paper presents an optimization based mathematical modelling approach for a single source single destination crude oil facility location transshipment problem. We began by formulating a mixed-integer nonlinear programming model and use a rolling horizon heuristic to find an optimal location for a storage facility within a restricted continuous region. We next design a hybrid two-stage algorithm that combines judicious facility locations resulting from the proposed model into a previously developed column generation approach. The results indicate that improved overall operational costs can be achieved by strategically determining cost-effective locations of the transshipment facility.

The main contributions of this paper are shown as: (1) we define dual hesitant fuzzy t-norms and t-conorms; (2) based on dual hesitant fuzzy t-norms and t-conorms, we introduce a family of prioritized dual hesitant fuzzy operators to aggregate dual hesitant fuzzy information of alternatives with regard to the prioritized attributes; (3) we propose a method to handle the dual hesitant fuzzy multi-attribute decision making (MADM) problems with prioritized attributes; (4) we show that compared to other relevant studies, the developed prioritized aggregation operators take full advantage of the given decision information, avoid the loss of original information, and thus yield better final decision results.

In this paper, by unifying the dual roles of order-inducing variables, a PF weighted induced generalized weighted averaging (PFWIGOWA) operator is presented to facilitate the PF information. The key feature of the proposed operator is that it can improve the existing aggregation operators by the dual roles of its order-inducing variables. In addition, the PFWIGOWA’s desirable properties and different families are also discussed. Furthermore, an approach based on the developed operator is presented for solving multi-attribute group decision making (MAGDM) problems with PF information. Finally, the usefulness of the proposed method is illustrated in a research and development (R&D) projects selection problem.

For this article, we shall expand the TODIM model to the MADM with the picture fuzzy numbers (PFNs). Firstly, the concept, comparative method and distance of PFNs are introduced and the traditional TODIM model is presented. Then, the expanded TODIM model is developed to solve MADM problems with PFNs. Finally, a numerical example is given to verify the proposed approach.

This paper presents a simple differential speech signal coding algorithm, based on backward adaptation. The considered algorithm is executed in frame by frame manner, by implementing predictive and adaptive quantization techniques. Both prediction and adaptation are performed backward, based on the previously quantized input signal frame. This enables us to obtain high quality output signal, without increasing the bit rate. This research puts emphasis on the quantizer design, with the optimal support limit determination, and theoretical performance evaluation. Objective quality of the output signal is evaluated through signal to quantization noise ratio (SQNR). We perform theoretical and experimental analysis of the algorithm performance and provide comparative results of implementing speech signal coding techniques with similar complexity. Experimental results show that our simple differential speech coding algorithm satisfies the G.712 Recommendation for high-quality speech coding at the bit rate of 6 bits per sample. This indicates that the algorithm can be successfully implemented in high quality speech signal coding.

Quantitative and qualitative fuzzy information measures have been proposed to solve multi-attribute decision making (MADM) problems with interval–valued hesitant fuzzy information from different points. We analyse the existing fuzzy information measures of the interval-valued hesitant fuzzy sets (IVHFSs) in detail and classify them into two categories. One is based on the closeness of the data, such as the distance, and the other is based on the linear relationship or variation tendency, such as the correlation coefficient. These two kinds of information measures are actually partial measures which pay attention to only one factor of the data. Therefore, we construct a novel synthetic grey relational degree by considering both the closeness and the variation tendency factors of the data to improve the existing information measures and enhance the grey relational analysis (GRA) theory for IVHFSs. However, the notion of the synthetic grey relational degree is not only restricted to the IVHFSs but can be extended to other sets. Furthermore, we employ two practical MADM examples about emergency management evaluation and pattern recognition to validate and compare the proposed synthetic grey relational degree with other information measures, which demonstrate its superiorities in discrimination and accuracy.

Crossover operators play a very important role by creation of genetic algorithms (GAs) which are applied in various areas of computer science, including combinatorial optimization. In this paper, fifteen genetic crossover procedures are designed and implemented using a modern C# programming language. The computational experiments have been conducted with these operators by solving the famous combinatorial optimization problem – the quadratic assignment problem (QAP). The results of the conducted experiments on the characteristic benchmark instances from the QAP instances library QAPLIB illustrate the relative performance of the examined crossover operations.

The problem of speech corpus for design of human-computer interfaces working in voice recognition and synthesis mode is investigated. Specific requirements of speech corpus for speech recognizers and synthesizers were accented. It has been discussed that in order to develop above mentioned speech corpus, it has to consist of two parts. One part of speech corpus should be presented for the needs of Lithuanian text-to-speech synthesizers, another part of speech corpus – for the needs of Lithuanian speech recognition engines. It has been determined that the part of speech corpus designed for speech recognition engines has to ensure the availability to present language specificity by the use of different sets of phonemes. According to the research results, the speech corpus Liepa, which consists of two parts, was developed. This speech corpus opens possibilities for cost-effective and flexible development of human-computer interfaces working in voice recognition and synthesis mode.

The study is dictated by the need to interpret and justify the solutions of classification problems. In this context, a method of logical analysis of data is considered along with its modifications based on the specifically developed algorithmic procedures, the use of which can increase the interpretability and generalization capability of classifiers. The article confirms in an empirical way that the suggested optimization models are suitable for building informative patterns and that the designed algorithmic procedures are efficient when used for the method of logical analysis of data.

Ontology Driven Software Development (ODSD) combines traditional Model Based Software Development (MBSD) techniques with ontology technology in order to provide extensions to and advantages over MBSD. The goal of the paper is to identify current ODSD approaches and to provide qualitative and comparative analysis of the collection of identified approaches. Main research questions of the paper concern the ways of how ontologies are integrated to MBSD process and how their usage advances MBSD. Benefits and challenges of each of the discussed approaches are presented. The analysis is based on literature and projects reviews in the fields of ontology engineering, MBSD and ODSD. The result of the analysis provides understanding of what is the role of ontologies in ODSD and shows whether application of ontology technologies to the MBSD process gives rise to a new paradigm called consistency preserving software development or not.