Cloud Queue Service Predictive Load Balancing
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Solution Overview
Problem
Current virtual queuing systems fail to efficiently manage multiple queues, address punctuality concerns, and utilize modern data advantages, limiting their ability to optimize wait times and user experience.
Innovation Solution
A cloud-based queue service that uses predictive analysis and 'Big Data' to manage and balance users across multiple queues, providing periodic updates and incentives, and employing load balancing mechanisms to minimize wait times and optimize queue management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional virtual queuing systems are used, then users can receive queue status updates, but wait times remain long and multiple queues cannot be efficiently managed
Solution Approach 1:
The system segments the queue management into multiple independent virtual queues, each tracked separately with unique identifiers. Users can be assigned to specific queues based on their preferences or system optimization, allowing parallel management of multiple queues rather than a single monolithic queue structure.
Solution Approach 2:
The system transitions from physical queue positioning to a virtual dimension using mobile devices and GPS technology. Users receive digital queue tickets with unique identifiers and can be located and updated remotely through wireless communication, adding a digital layer that eliminates the need for physical queue monitoring infrastructure.
2Ease of operation
If users are required to remain within range of announcements or pagers, then they can receive updates, but their physical movement is restricted
Solution Approach 1:
The system replaces the mechanical acoustic field limitation of traditional announcements and pagers with electronic wireless communication through mobile devices. Queue updates are transmitted digitally via push notifications, SMS, or data networks, eliminating the need for users to remain within acoustic range while ensuring reliable delivery of information.
3Reliability
If current virtual queuing systems are used, then basic queue tracking is possible, but they fail to address punctuality concerns and no-shows
Solution Approach 1:
The system implements bidirectional communication where users can check-in at their current location using their mobile device. The system receives this feedback, updates the user's status, and adjusts queue positioning accordingly. This feedback loop enables the system to distinguish between genuine delays and no-shows, improving punctuality tracking without requiring complex additional hardware.
Solution Approach 2:
The system performs preliminary actions by sending proactive notifications to users about their queue position, estimated wait times, and required arrival times. Users receive advance warning if they are at risk of missing their queue slot, allowing them to take corrective action before becoming a no-show, thereby improving overall punctuality.
4Productivity
If predictive analysis and Big Data are implemented, then queue optimization improves, but data processing requirements increase
Solution Approach 1:
The system uses the mobile devices that users already possess for multiple functions: as queue tickets, as communication endpoints, as GPS locators, and as notification receivers. This universal use of existing hardware eliminates the need for dedicated data collection infrastructure, reducing data processing requirements while maintaining high queue management efficiency through predictive analytics.
Data Source
AI summary
A system and method for managing virtual queues. A cloud-based queue service manages a plurality of queues hosted by one or more entities. The queue service is in constant communication with the entities providing queue management, queue analysis, and queue recommendations. The queue service is likewise in direct communication with queued persons. Sending periodic updates while also motivating and incentivizing punctuality and minimizing wait times based on predictive analysis. The predictive analysis uses “Big Data” and other available data resources, for which the predictions assist in the balancing of persons across multiple queues for the same event or multiple persons across a sequence of queues for sequential events.