Saparauskas (2019) An extended COPRAS model for multi-criteria decisionmaking problems and its application in web-based hotel evaluation and selection, Economic

This study proposes a hybrid multiple criteria decision making (MCDM) methodology for evaluating the performance of the Indian railway stations (IRS). Since the customers are heterogeneous and their requirements are often imprecise, the evaluation process is a critical step for prioritizing the IRS. To improve the existing approaches, an efficient evaluation technique has been proposed by integrating rough numbers, analytic hierarchy process (AHP) and multi-attribute border approximation area comparison (MABAC) methods in rough environment. The relative criteria weights based on their preferences given by experts is determined by rough AHP whereas evaluation of the alternatives based on these criteria are done by the modified rough MABAC method. A case study of prioritizing different railway stations in India is provided to demonstrate the efficiency and applicability of the proposed method. Among different criteria "proactively" is observed to be the most important criteria in our analysis, followed by 'Railfanning' and 'DMO' is found to be the best among the forty IRS in this study. Finally, a comparative analysis and validity testing of the proposed method are elaborated and the methodology provides a standard to select IRS on the basis of different criteria.

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Accommodation is one of the necessities of tourists and travel agencies' significant responsibilities. With the growing competition and profit-making various tour organising companies have started providing attractive accommodation options to the travellers to win their choices. Present research performs a case study on accommodation providing hotels through designing a strategy to enhance their profit earrings by welcoming more and more tourists. The methodology comprises rough set theory (RST) using the Dominance Based rough set theory (DRST) on the collected data of selected variables such as location, facility, value for money, etc., of hotels. Correspondingly, if and then decision rule has been used to classify these variables. The statistical methods regression analysis has also been used to define each variable's relationship and influence on concerned authorities' decision-making. The results show that hotels and tourists can benefit from the proposed strategy and help in decision making by understanding tourist behaviour, increasing profit, improving services, and quality of hotels.