Cognitive Scheduling via Vector-Based Constraint Tuning
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Solution Overview
Problem
Manually creating employee work schedules that account for numerous constraints, such as varying shifts, locations, and performance indicators is a time-consuming and error-prone process, often resulting in unimplemented or unenforced constraints, especially in large organizations.
Innovation Solution
A cognitive tuning method using machine-learning algorithms that parse user schedule requests, assemble data into search vectors, and utilize nearest neighbor algorithms to identify historical vectors for generating optimized employee schedules based on enhanced constraint vectors, incorporating historical data and business KPIs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual scheduling methods are used, then flexibility in schedule adjustments is maintained, but time consumption and error rates increase significantly
Solution Approach 1:
The patent replaces manual mechanical scheduling processes with an automated optimization system that uses algorithms to generate schedules. The system substitutes human managers' manual work with computer-based optimization algorithms that automatically process scheduling constraints and generate optimal schedules, thereby improving productivity while reducing time loss.
Solution Approach 2:
The scheduling system performs self-service by automatically generating optimized schedules without requiring manual intervention. The optimization algorithm independently processes scheduling constraints, employee preferences, and business requirements to produce schedules, eliminating the need for managers to manually create and adjust schedules repeatedly.
2Reliability
If multiple scheduling constraints are enforced, then schedule quality improves, but system complexity increases
Solution Approach 1:
The patent transforms the scheduling problem by changing parameters from manual constraint checking to algorithmic optimization. The system uses mathematical optimization algorithms that handle multiple constraints simultaneously through parameter-based processing, allowing complex constraint enforcement without proportionally increasing system complexity. The optimization algorithm processes constraints as adjustable parameters rather than rigid rules.
Solution Approach 2:
The optimization algorithm serves as an intermediary between scheduling constraints and final schedule generation. Rather than directly implementing each constraint individually, the algorithm mediates by processing all constraints through a unified optimization framework, simplifying the overall system architecture while maintaining reliable constraint enforcement.
3Measurement precision
If historical data is incorporated, then scheduling accuracy improves, but data processing requirements increase
Solution Approach 1:
The system performs preliminary action by pre-processing and storing historical scheduling data in an optimized format before actual schedule generation. Historical data including past schedules, constraint violations, and performance metrics are prepared in advance and structured for efficient retrieval, allowing the optimization algorithm to quickly leverage historical patterns without processing raw data during schedule creation.
Solution Approach 2:
The patent uses copying by creating simplified representations of historical scheduling patterns rather than processing complete historical datasets. The system extracts essential patterns and constraints from historical data and uses these condensed copies to inform current scheduling decisions, reducing data processing requirements while maintaining accuracy improvements from historical learning.
Data Source
AI summary
An embodiment includes parsing form data into a plurality of form values received with a schedule request and assembling the form values into a search vector. The embodiment searches historical data using a nearest neighbor algorithm that inputs the search vector and identifies first and second sets of historical vectors comprising that are closest by Euclidean distance to the search vector. The embodiment calculates an enhanced constraint vector comprising an average value based at least in part on the first set of historical vectors and a standard deviation value based at least in part on the second set of historical vectors. The embodiment generates an employee schedule using an optimization algorithm subject to a plurality of schedule constraints that include the enhanced constraint vector. The embodiment then transmit data for initiating a notification regarding availability of the employee schedule.