Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 10s (2025) 1302 https://internationalpubls.com A Study on Fuzzy Network Graph Used for Online Hotel Reservation Using MATLAB K.Bhadmavadhi [1] ; M.Kannan [2] ; K.Sakthivel [3]; Dr. A. Rajkumar [4] 1, 2 Department of Mathematics, Govt Arts College, Ariyalur-621713. 3 Department of Mathematics, Govt Arts and Science College, Veppanthattai, Perambalur - 621 116. 4 PG and Research Department of Mathematics, Annai Vailankanni Arts and Science College Thanjavur, Tamil Nadu, India. 1,2,3,4 (Affiliated to Bharathidasan University, Thiruchirappalli-24, Tamil Nadu, India) Email ID: padmavelu86@gmail.com ; kannan5018@gmail.com ; ksvgac@gmail.com ; rajkumar@avasctnj.edu.in Article History: Received: 12-01-2025 Revised: 15-02-2025 Accepted: 01-03-2025 Abstract: In this paper, customer preference, price, location, user ratings, amenities, and hotel suitability are represented as a fuzzy network graph. In the modern era, online hotel reservation is an essential part of first-come, first-served situations. So during booking, some traffic happened due to overpopulation, so these are handled by a fuzzy network graph to control the seasonal pricing, booking demand, and cancellation policy. By this proposed method, we find the hotel suitability score with the help of MATLAB implementation. This method allows us to make optimal hotel booking decisions. Keywords: Fuzzy network graph, Triangular fuzzy number, MATLAB, Fuzzy ranking, Defuzzification. 1. Introduction The hotel sector is in a competitive climate as a result of the new prospects brought about by technology. These days, more customers are enthusiastic about making hotel reservations online from the comfort of their own homes, wherever they may be in the world. Understanding the several elements impacting consumers' decisions when making online hotel reservations is the aim of this conceptual study. The study cites a number of aspects that affect consumers' decisions when making online hotel reservations, including privacy and safety, ease of use and convenience, transactional ease, price, information, website usability, and accessibility. By gaining more customers, the study will help marketers formulate their promotional plans for capturing and surviving the competitive market. Graph theory is an essential field to solve the real-world issues. So, using the fuzzy graph, we can manage the traffic during online booking. Here, both the edges and vertices are represented using a triangular fuzzy number. The goal of this study is to construct a fuzzy network graph-based model and implementation of MATLAB by representing criteria like price, location, ratings, and availability. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 10s (2025) 1303 https://internationalpubls.com 2. Methodology 2.1. Fuzzy Network Graph Model The price, location, and ratings are represented as triangular fuzzy numbers in the fuzzy network graph. These criteria are represented in the nodes and edges of the fuzzy network graph. 2.2. Steps in the Proposed Model: 3. Graph Model 4. MATLAB Implementation % Create a new Fuzzy Inference System (FIS) for Hotel Selection fis = newfis('HotelSelectionEnhanced'); % Define Input Variables fis = addvar(fis, 'input', 'Price', [0 200]); fis = addmf(fis, 'input', 1, 'Low', 'trapmf', [0 0 50 100]); fis = addmf(fis, 'input', 1, 'Medium', 'trapmf', [50 100 100 150]); fis = addmf(fis, 'input', 1, 'High', 'trapmf', [100 150 200 200]); fis = addvar(fis, 'input', 'Location', [0 10]); fis = addmf(fis, 'input', 2, 'Poor', 'trimf', [0 0 5]); fis = addmf(fis, 'input', 2, 'Average', 'trimf', [3 5 7]); fis = addmf(fis, 'input', 2, 'Good', 'trimf', [5 10 10]); Define Fuzzy Parameters Construct the Fuzzy Network Graph Apply Fuzzy Decision- Making Algorithm Implementatio n in MATLAB Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 10s (2025) 1304 https://internationalpubls.com fis = addvar(fis, 'input', 'UserRating', [1 5]); fis = addmf(fis, 'input', 3, 'Bad', 'trapmf', [1 1 2 3]); fis = addmf(fis, 'input', 3, 'Average', 'trimf', [2 3 4]); fis = addmf(fis, 'input', 3, 'Excellent', 'trapmf', [3 4 5 5]); fis = addvar(fis, 'input', 'Amenities', [0 10]); fis = addmf(fis, 'input', 4, 'Few', 'trimf', [0 2 4]); fis = addmf(fis, 'input', 4, 'Moderate', 'trimf', [3 5 7]); fis = addmf(fis, 'input', 4, 'Many', 'trimf', [6 8 10]); fis = addvar(fis, 'input', 'SeasonalPricing', [0 10]); fis = addmf(fis, 'input', 5, 'Low', 'trimf', [0 2 4]); fis = addmf(fis, 'input', 5, 'Moderate', 'trimf', [3 5 7]); fis = addmf(fis, 'input', 5, 'High', 'trimf', [6 8 10]); fis = addvar(fis, 'input', 'BookingDemand', [0 10]); fis = addmf(fis, 'input', 6, 'Low', 'trimf', [0 2 4]); fis = addmf(fis, 'input', 6, 'Medium', 'trimf', [3 5 7]); fis = addmf(fis, 'input', 6, 'High', 'trimf', [6 8 10]); fis = addvar(fis, 'input', 'CancellationPolicy', [0 10]); fis = addmf(fis, 'input', 7, 'Flexible', 'trimf', [0 2 4]); fis = addmf(fis, 'input', 7, 'Moderate', 'trimf', [3 5 7]); fis = addmf(fis, 'input', 7, 'Strict', 'trimf', [6 8 10]); % Define Output Variable fis = addvar(fis, 'output', 'HotelSuitability', [0 10]); fis = addmf(fis, 'output', 1, 'NotRecommended', 'trimf', [0 2 4]); fis = addmf(fis, 'output', 1, 'Fair', 'trimf', [3 5 6]); fis = addmf(fis, 'output', 1, 'Good', 'trimf', [5 7 8]); fis = addmf(fis, 'output', 1, 'HighlyRecommended', 'trimf', [7 9 10]); % Define Fuzzy Rules ruleList = [ 1 3 3 3 2 1 1 4 1 1; % Low Price, Good Location, Excellent Rating, Many Amenities, Moderate Season, Low Demand, Flexible Cancellation -> Highly Recommended Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 10s (2025) 1305 https://internationalpubls.com 3 1 1 1 3 3 3 1 1 1; % High Price, Poor Location, Bad Rating, Few Amenities, High Season, High Demand, Strict Cancellation -> Not Recommended 2 2 2 2 2 2 2 2 1 1; % Medium Price, Average Location, Average Rating, Moderate Amenities, Moderate Season, Medium Demand, Moderate Cancellation -> Fair 3 3 3 3 1 1 1 4 1 1 % High Price, Good Location, Excellent Rating, Many Amenities, Low Season, Low Demand, Flexible Cancellation -> Highly Recommended ]; fis = addrule(fis, ruleList); % Evaluate the Fuzzy Model with Sample Inputs inputValues = [120 7 4 5 3 6 2]; % Medium Price, Good Location, Excellent Rating, Moderate Amenities, Moderate Season, High Demand, Moderate Cancellation hotelSuitability = evalfis(inputValues, fis); % Display the Suitability Score disp(['Enhanced Hotel Suitability Score: ', num2str(hotelSuitability)]); 4.1. Output: Enhanced Hotel Suitability Score: 5 5. Conclusion This paper mainly deals with how effectively fuzzy network graph is used in online hotel reservations. The usage of fuzzy network graphs helps in decision-making and, further, the proposed method is implemented in MATLAB to get better results. The above MATLAB code enhanced the hotel suitability score for customer preference, cost, location and user-friendliness. References [1] Miroslav Ciric and Stojan Bogdanovic, Fuzzy social network analysis, Research supported by ministry of Science and Technological Development, Republic of Serbia, Grant No. 144011. [2] Anita Pal, Introduction to Fuzzy Graph Theory, National Institute of Technology Durgapur. [3] A. Rosenfeld, Fuzzy graphs, in: L. A. Zadeh, K. S. Fu, M. Shimura (Eds.), Fuzzy Sets and Their Applications, Academic Press, New York (1975), 77-95. [4] C. Eslahchi and B. N. Onaghe, Vertex strength of fuzzy graphs, International Journal of Mathematics and Mathematical Sciences, Volume 2006, Article ID 43614, Pages 1-9, DOI 10.1155/IJMMS/2006/43614. [5] J. N. Mordeson and P. S. Nair, Fuzzy Graphs and Hypergraphs, Physica Verlag, 2000. [6] Kenneth Falconer, Fractal Geometry, Mathematical Foundations and Applications, 1990. [7] B. B. Mandelbrot, The Fractal Geometry of Nature, W H Freeman, San Francisco, 1982. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 10s (2025) 1306 https://internationalpubls.com [8] L. A. Zadeh, Fuzzy sets, Information and Control 8 (1965), 338-353. [9] Dr. A. Rajkumar, and J. Sharmila Jessie Ignatia, “A Method for Solving Bottleneck-Cost Transportation Problem Using Fuzzy Optimization Trapezoidal Fuzzy Numbers With λ-Cut and Ranking Method”, Advances and Applications in Mathematical Sciences Mili Publications, India. UGC care, Vol.21, no.8, pp.4563-4574, (2022). [10] Dr. A. Rajkumar, J. Sharmila Jessie Ignatia, and A. Ezhilarasi, “A MATLAB-Based Method for Solving Fuzzy Transportation Problems”, Indian Journal of Natural Sciences, Vol.14, no.79, pp.58613 – 58620, (2023). [11] Dr. A. Rajkumar, A. Ezhilarasi, and J. Sharmila Jessie Ignatia, “Analysis of MCDM using Promethee II Techniques in the Case of River Water Quality Monitoring”, Indian Journal of Natural Sciences, Vol.14, no.79, pp.59140 – 59145, (2023), [12] Dr. A. Rajkumar, R. Archana, J. Sharmila Jessie Ignatia, andA. Ezhilarasi, “An Evaluation of Irrigation system of performance in metacriteria decision making using fuzzy logic”, Indian Journal of Natural Sciences, Vol.14, no.79, pp.58696 – 58703, (2023). [13] Dr. A. Rajkumar, J. Sharmila Jessie Ignatia, and A. Ezhilarasi, “A Study on Solving Least- Cost Fuzzy Transportation Problem using MATLAB”, Indian Journal of Natural Sciences, Vol.14, no.82, pp. 67473 – 67481, (2024). [14] Dr. A. Rajkumar, A. Ezhilarasi, and J. Sharmila Jessie Ignatia, “Application of MCDM using PROMETHEE II Techniques in the case of fertilizer selection for agriculture”, Indian Journal of Natural Sciences, Vol.15, no.83, pp.73269 – 73275, (2024). [15] Dr. A. Rajkumar, A. Ezhilarasi, and J. Sharmila Jessie Ignatia, “A study on the comparison between topsis and saw methods in the selection of soil for agriculture”, AIP Conference Proceedings, Vol.3193, no.1, pp.020282(1-7), (2024).