Real-Time Room Allocation Using Predictive Check-In Models
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Solution Overview
Problem
Existing hotel management systems fail to effectively allocate preferred room types based on customer preferences, leading to unmet demands and inefficient turnaround times due to the lack of predictive check-in and check-out models and mechanisms to optimize room availability.
Innovation Solution
A processor-implemented method and system that receives customer preferences, creates an assignment matrix, predicts check-in and check-out delays using a random forest regression model, and calculates a cumulative time adjustment factor to allocate preferred room types in real-time, ensuring better alignment with customer preferences and optimizing room turnover.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual room allocation is used by hotel administrators, then flexibility in handling customer requirements is improved, but time consumption and operational efficiency deteriorate
Solution Approach 1:
The system enables automated self-service room allocation where the computer automatically matches customers with suitable rooms based on preferences and availability, eliminating the need for manual administrator intervention while maintaining allocation quality
Solution Approach 2:
The manual mechanical process of administrator review and allocation is replaced with an automated computer-based system that uses algorithms to perform room allocation, significantly reducing time consumption while maintaining or improving matching accuracy
2Device complexity
If room allocation is done without check-in and check-out prediction models, then system simplicity is improved, but customer wait times and turnaround time optimization deteriorate
Solution Approach 1:
The system performs preliminary predictions of check-in and check-out times using trained models before actual allocation decisions are made, allowing proactive preparation and optimization of room availability without adding significant system complexity
Solution Approach 2:
The system uses feedback from historical check-in and check-out data to continuously improve prediction accuracy, enabling better time management for room allocation while maintaining a relatively simple system architecture
3Adaptability or versatility
If preferred room types are not guaranteed at booking, then hotel operational flexibility is improved, but customer satisfaction and preference fulfillment deteriorate
Solution Approach 1:
The system performs preliminary identification and reservation of preferred room types at the time of booking based on customer preferences and predicted arrival times, ensuring preference fulfillment while maintaining operational flexibility through automated management
Solution Approach 2:
The system dynamically adjusts allocation parameters such as room type, availability time windows, and customer preference weights to balance operational flexibility with reliable preference fulfillment, using multiple configurable parameters to optimize both aspects
Data Source
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AI summary
Embodiments herein provide a method and system for a real-time allocation of a preferred room type based on one or more preferences of a customer. Every hotel maintains its own reservation database, the system gathers some of property management system (PMS) attributes of that day from the hotel administrator. The PMS attributes includes customer's booked room type, number of rooms booked, number of days customers will stay. Further, the system verifies all the preferences of the hotel and the different room type that are available for offering to customers on the day of check-in. A Check-in Check-Out model takes the attributes such as customers' reservation details, weather, arrival, and departure patterns of the customers from the transportation data store to predict the check-in and check-out time of the customers using pre-trained machine learning prediction models. A looping technique maps the rooms to the customers according to the preferred room types.