Predicting and planning: An intelligent system for demand-based hotel staff scheduling

Abstract

This study develops an intelligent staff scheduling system for hotels that integrates customer demand prediction with workforce planning to address contemporary challenges in hospitality management. Drawing on operational efficiency principles, the system anticipates guest demand using a long short-term memory (LSTM) network and generates optimal staff schedules by combining ant colony optimization (ACO) algorithm with variable neighborhood search (VNS) algorithm. The system is designed to balance operational efficiency with employee well-being while minimizing labor costs. Real-world testing demonstrates that the system produces schedules that outperform traditional manual methods. By automating the scheduling process, the approach aligns business objectives with employee satisfaction, resulting in more efficient operations and improved working conditions. This research contributes to the theoretical understanding of operational efficiency and its practical application in hotel staff scheduling by integrating demand forecasting with schedule planning, thereby meeting the needs of customers, managers, and employees