MODELING AND ALGORITHMS: OPTIMIZATION OF HOTEL AND RESTAURANT BUSINESS OPERATIONS USING INFORMATION TECHNOLOGIES

Abstract

One of the main challenges in the modern hotel and restaurant business (HRB) is the low efficiency of resource management, leading to significant financial losses, inefficient use of staff, inaccurate demand forecasting, and ineffective pricing strategies. The lack of automated systems for process analysis and optimization further limits the competitiveness of enterprises in an increasingly dynamic and technology-driven market. In the HRB, where customer expectations are high, and competition is fierce, improving operational efficiency and adapting to new technologies are crucial for business survival and growth.

This paper explores modern approaches to addressing these issues through the implementation of information technologies. The study examines the use of machine learning algorithms for demand forecasting, dynamic pricing, inventory management, and workforce optimization. Machine learning enables businesses to analyze vast amounts of data and accurately predict customer behavior, ensuring better inventory management and timely staff allocation. Dynamic pricing systems, powered by artificial intelligence (AI), adjust prices in real-time based on demand fluctuations, competitor pricing, and other external factors. These technologies ensure that businesses maximize their revenue potential while maintaining competitive prices.

The integration of cloud technologies, CRM systems, blockchain solutions, and IoT for business process automation is also discussed. Cloud computing allows businesses to store and analyze data more efficiently while ensuring real-time updates, enhancing coordination across different departments. CRM systems help improve customer relationship management by offering personalized services and promotions. Blockchain can ensure the security and transparency of transactions, while IoT can provide real-time monitoring of facilities and resources, thus streamlining maintenance and operations.

Mathematical models and algorithms are proposed to enhance operational efficiency, reduce costs, and improve service quality. These models aim to optimize resource allocation, from energy consumption to staff scheduling, leading to cost savings and better customer service. The results indicate that the comprehensive application of digital solutions contributes to increased profitability, enhanced customer satisfaction, and improved competitiveness of HRB enterprises. The implementation of these technologies is crucial for businesses seeking to maintain their relevance in an industry that is rapidly embracing digital transformation.