RANKING ALTERNATIVES USING PIPRECIA METHOD: A CASE OF HOTELS’ WEBSITE EVALUATION

The article presents the application of the PIvot Pairwise RElative Criteria Importance Assessment (PIRECIA) method for ranking alternatives. The PIPRECIA method is primarily intended for determining the significance (weight) of criteria, but it can also be used for completely solving decision-making problems. So far, this method has not been used for completely solving multiple criteria decision-making problems, which is why this article presents the application of the PIPRECIA method for the evaluation and ranking alternatives. The process of evaluating alternatives using the PIPRECIA method is shown on an example of evaluating the websites of some hotels located in Southern and Eastern Serbia.

An integrated SWOT–extended PIPRECIA model for identifying key determinants of tourism development: The case of Serbia

This paper proposes a new integrated model based on SWOT and extended PIvot Pairwise RElative Criteria Importance Assessment (PIPRECIA) that offers a systematic approach to strategic planning in tourism. The applicability of the proposed integrated model is demonstrated through a case study defining the main determinants of tourism development in Serbia. The result emphasizes the strategy Improving the organization, management, and enhancement of tourism development as the highest priority for implementation. The model facilitates decision-making in tourism, and its key advantages are its suitability for application in group decision-making and its simplicity.

REGIONAL AIRCRAFT SELECTION WITH FUZZY PIPRECIA AND FUZZY MARCOS: A CASE STUDY OF THE TURKISH AIRLINE INDUSTRY

Aircraft selection is an important issue in achieving long-term goals in the airline industry. For this issue in which multiple conflicting criteria are involved, the extant literature points to the use of multi-criteria decision-making (MCDM) methods. In this respect, this study aims to propose a systematic and comprehensive framework with a focus on the regional aircraft selection perspective. To achieve this, an integrated fuzzy Pivot Pairwise Relative Criteria Importance Assessment (F-PIPRECIA) and fuzzy Measurement Alternatives and Ranking according to the Compromise Solution (F-MARCOS) approach was employed. In this study, in which six regional aircraft alternatives were evaluated according to 14 criteria, data were collected from five decision experts. As a result, it was found that the most pivotal criterion is C33 (Operational Cost), and the least important criterion is C12 (NOx). In addition, CRJ1000 was identified as the most promising regional aircraft alternative. The results of the application were further validated by applying a three-stage sensitivity analysis. The proposed structure is anticipated to assist airline managers in aircraft selection decisions under uncertainty by offering a robust and systematic tool.

Understanding technical cleanliness: importance, assessment, maintenance

Development of the automotive industry, including so-called dowsizing and rightsizing, entails the need to ensure, and hence to verify, the stringent cleanliness levels of an increasing number of car components. The assurance of proper cleanliness level requires knowledge of the entire production process and precise information on the contaminants that will allow to identify their sources. Obtaining reliable cleanliness information requires establishing of many different factors, i. a. selection of proper contamination extraction and collection technique, proper parameters of extraction, validation of extraction procedure, methods of particles analysis. Analytical techniques which are implemented include gravimetric, optical microscopy and advanced techniques like scanning electron microscopy. This work present’s actual state of knowledge regarding technical cleanliness analysis of components. Important aspects of QA&QC in technical cleanliness assessment are also discussed.

Classification of Full Text Biomedical Documents: Sections Importance Assessment

The exponential growth of documents in the web makes it very hard for researchers to be aware of the relevant work being done within the scientific community. The task of efficiently retrieving information has therefore become an important research topic. The objective of this study is to test how the efficiency of the text classification changes if different weights are previously assigned to the sections that compose the documents. The proposal takes into account the place (section) where terms are located in the document, and each section has a weight that can be modified depending on the corpus. To carry out the study, an extended version of the OHSUMED corpus with full documents have been created. Through the use of WEKA, we compared the use of abstracts only with that of full texts, as well as the use of section weighing combinations to assess their significance in the scientific article classification process using the SMO (Sequential Minimal Optimization), the WEKA Support Vector Machine (SVM) algorithm implementation. The experimental results show that the proposed combinations of the preprocessing techniques and feature selection achieve promising results for the task of full text scientific document classification. We also have evidence to conclude that enriched datasets with text from certain sections achieve better results than using only titles and abstracts.

Evaluation of rapeseed varieties using novel integrated fuzzy PIPRECIA – Fuzzy MABAC model

Decision making is constantly present in agriculture. Choosing the wrong variety carries the risk that the investment in terms of sowing does not pay off at all. Therefore, it is necessary to choose the variety that gives the best results. In order to achieve this, it is necessary to apply multi-criteria decision-making of available varieties, which is, in this paper, done on the example of hybrid varieties of rapeseed that were created by selection at the Institute of Field and Vegetable Crops in Novi Sad. By applying fuzzy logic, a novel integrated Multi-Criteria Decision-Making (MCDM) model is developed and rapeseed varieties were evaluated. For determining four main and 20 subcriteria, fuzzy PIPRECIA (PIvot Pairwise RElative Criteria Importance Assessment) method has been applied based on fuzzy Bonferroni operator, while for ranking alternatives fuzzy MABAC (Multi-Attributive Border Approximation area Comparison) method has been used. The results obtained using the novel integrated fuzzy MCDM model showed that the variety A2 – Zorica has the best results, followed by A1 - NS Ras, while the worst results were seen by the variety A5 - Zlatna. These results were confirmed using other five fuzzy MCDM methods. Sensitivity analysis—changing criteria weights showed the worst results in the variety A6 - Jovana, which took last place in the application of 18 scenarios. The presented model and the results of this research will help farmers to solve this decision problem.

A Novel Integrated Interval Rough MCDM Model for Ranking and Selection of Asphalt Production Plants

Asphalt production plants play an important role in the field of civil engineering, but also in the entire economic system since the construction of roads enables uninterrupted functioning within it. In this paper, the ranking of asphalt production plants on the territory of the Autonomous Province of Vojvodina has been performed. The modern economy needs contemporary models and methods to solve complicated MCDM problems and, for these purposes, it has been developed an original Interval Rough Number (IRN) Multi-criteria decision-making (MCDM) model that implies an extension of two methods belonging to the field with interval rough numbers. After forming a list of eight most significant criteria for assessing the efficiency of asphalt production plants, the Interval Rough Number PIvot Pairwise RElative Criteria Importance Assessment (IRN PIPRECIA) method was developed to determine the significance of the criteria. A total of 21 locations with asphalt mixture installation were considered. For that purpose, seven asphalt production plants were included, and for their ranking, the IRN EDAS (Evaluation based on Distance from Average Solution) method was created. The aim of this paper is to develop a novel interval rough model that can be useful for determining the efficiency of asphalt production plants. Averaging in group decision-making (GDM) for both methods was performed using an IRN Dombi weighted geometric averaging (IRNDWGA) aggregator. The obtained results show that (A15) Ruma (SP)–Mačvanska Mitrovica–Zasavica has the best characteristics out of the set of locations considered in this study. However, Alternatives A6 and A19 are also variants with remarkably good characteristics since there is very little difference in values compared to the first-ranked alternative. Also, the obtained results have shown that the developed model is applicable, which is proven through a comparative analysis.