Self-Schedule Recommendation System for Shift Scheduling
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Solution Overview
Problem
Shift scheduling is a time-intensive and computationally difficult process that is often impractical for automated systems, leading to inefficiencies and manual, error-prone methods that fail to accommodate real-time adjustments and employee preferences.
Innovation Solution
A system and method for generating schedule suggestions based on ranked open shift features and user preferences, allowing employees to accept, reject, or edit suggested schedules, using a score function to optimize shift assignments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated global schedulers are used, then schedule generation speed is improved, but computational requirements and system complexity increase significantly
Solution Approach 1:
The patent segments the scheduling problem into two distinct components: (1) a server-side global scheduler that generates initial schedules using automated algorithms, and (2) a client-side recommendation system that handles real-time adjustments and interactions. This segmentation allows the computationally intensive global scheduling to be performed periodically on the server, while real-time user interactions are handled locally with minimal computational overhead, thus resolving the contradiction between automation speed and computational complexity.
Solution Approach 2:
The system performs preliminary schedule generation on the server before transmitting to client devices. The server pre-computes base schedules using automated global scheduling algorithms, and then client devices perform only lightweight real-time adjustments based on user preferences and constraints. This preliminary action reduces the computational burden during real-time interactions while maintaining the benefits of automated scheduling.
2Adaptability or versatility
If manual scheduling methods are used, then flexibility for real-time adjustments is improved, but time consumption and error rates increase
Solution Approach 1:
The patent introduces a recommendation system as an intermediary between manual user input and the final schedule. When users make real-time adjustments or express preferences, the system automatically generates recommended schedule modifications using the preference data and constraints stored in the database. This intermediary automation handles the time-consuming aspects of schedule adjustment while preserving user flexibility and control, thus resolving the contradiction between adaptability and time consumption.
Solution Approach 2:
The system implements feedback mechanisms where user preferences, constraints, and historical scheduling data are continuously stored and used to improve future schedule recommendations. When users interact with the schedule in real-time, the system learns from these interactions and adjusts its recommendations accordingly. This feedback loop enables the system to become increasingly accurate and efficient at handling real-time adjustments, reducing time consumption while maintaining flexibility.
3Manufacturing precision
If automated scheduling is implemented, then consistency and accuracy of schedules are improved, but ability to accommodate employee preferences decreases
Solution Approach 1:
The patent implements a dynamic preference system where employee preferences, constraints, and weighting factors are stored in a database and can be updated in real-time. The automated scheduling algorithm dynamically adjusts its behavior based on these stored preferences, generating schedules that are both consistent with organizational requirements and tailored to individual employee preferences. This dynamic adaptation resolves the contradiction by allowing the system to maintain automated consistency while accommodating personalized preferences.
Solution Approach 2:
The system allows employees to modify parameters such as preference weights, constraint priorities, and availability windows. These parameter changes are stored and immediately reflected in subsequent schedule generations. The automated scheduler uses these modified parameters to generate schedules that maintain structural consistency and accuracy while adapting to changed employee preferences, thus resolving the contradiction between precision and adaptability.
Data Source
AI summary
Self-schedule suggestions are generated for a user. The suggestions are generated by scoring and selecting open shifts based on user preferences and selected shift features. Schedule days are scored and ranked according to criteria associated with each day. Scored open shifts are assembled into a suggested schedule by iteratively selecting the top open shifts for the top schedule days while verifying that the schedule meets regulatory or preference constraints. The self-schedule suggestions are presented to a user, and a user can accept or reject the suggestions. Rejecting suggestions can cause the regeneration of schedule suggestions using different shift features.