Dynamic Restaurant Booking Allocation System
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Solution Overview
Problem
Current online restaurant booking systems fail to dynamically optimize space and time usage, leading to inefficiencies in table allocation and revenue management, as they rely on static allocation methods that do not consider spatial characteristics, customer preferences, or dynamic pricing options.
Innovation Solution
A computing system that uses a decision tree logic structure to dynamically allocate bookings based on spatial awareness algorithms, allowing for the addition or removal of tables, and prioritizes bookings using CRM data and ambiance constraints, enabling flexible seating periods and dynamic pricing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static allocation methods are used for table booking, then system simplicity is maintained, but space utilization efficiency deteriorates
Solution Approach 1:
The patent implements dynamic table allocation that adapts to changing restaurant conditions such as current occupancy, booking patterns, and spatial characteristics. The system continuously adjusts table assignments based on real-time data rather than using fixed static rules, thereby improving space utilization while maintaining manageable system complexity through automated decision-making algorithms.
Solution Approach 2:
The system changes allocation parameters dynamically based on various factors including time of day, day of week, customer preferences, and restaurant occupancy. By adjusting these parameters in response to changing conditions, the system optimizes space utilization without requiring complex manual intervention for each scenario.
2Productivity
If dynamic optimization algorithms are implemented, then revenue management is improved, but computational complexity increases
Solution Approach 1:
The system performs preliminary calculations and pre-computes optimal allocation scenarios based on historical data and predicted demand patterns. By preparing allocation strategies in advance rather than computing them in real-time for each booking, the system achieves improved revenue management while keeping computational requirements at acceptable levels during actual operations.
Solution Approach 2:
The dynamic optimization system operates autonomously, making allocation decisions without requiring complex external computational resources or manual intervention. The self-contained nature of the algorithm reduces the need for external computational infrastructure, thereby managing complexity while maintaining revenue optimization capabilities.
3Productivity
If spatial awareness algorithms are used for table allocation, then space utilization is optimized, but processing time increases
Solution Approach 1:
The patent divides the restaurant space into distinct zones or regions with specific characteristics, and processes table allocation decisions for each segment separately. This segmentation allows the system to apply spatial awareness algorithms to smaller, more manageable subsets of tables, thereby optimizing space utilization while reducing the overall computational time required compared to processing all tables simultaneously.
4Reliability
If multiple constraints are considered in allocation, then customer satisfaction is improved, but allocation complexity increases
Solution Approach 1:
The system applies different allocation strategies and constraint weights to different spatial zones within the restaurant based on local characteristics. For example, certain tables may have specific constraints related to privacy, proximity to entrance, or suitability for different group sizes. By tailoring constraints to local conditions rather than applying uniform rules throughout, the system improves customer satisfaction while managing allocation complexity through localized decision-making.
Data Source
AI summary
In one aspect, the present invention provides a computing system for effecting an optimised condition for one or more booking requests in a venue having one or more spaces, comprising an allocation module executing on a processor and arranged to retrieve the booking requests from a database containing a plurality of booking requests, the booking requests including requestor constraint information regarding one or more constraints provided by the booking requestor including a predefined service period, and retrieve venue constraint information from a database, the venue constraint information including venue spatial information and furniture spatial information, wherein the allocation module executes an allocation algorithm that utilises the booking information and the venue constraint information to assess the capacity of the one or more venues and allocate a portion of space for each booking request to satisfy the optimised condition utilising the assessment, to derive an optimised allocation instruction set.