Automated Scheduling with Predicted User Setting Acceptance
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Solution Overview
Problem
Existing automated scheduling systems face challenges in handling complex scheduling scenarios with diverse user preferences and constraints, particularly when users do not provide complete or accurate information about their flexibility, leading to suboptimal schedules.
Innovation Solution
A method that predicts acceptance probabilities for alternative user settings, allowing for the calculation and evaluation of alternative schedules based on user behavior history, previous acceptance rates, and domain knowledge, to suggest changes that improve schedule quality without relying solely on initial user inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If automated scheduling systems rely solely on initial user inputs to generate schedules, then the system operation is simple and fast, but the schedule quality is suboptimal when users do not provide complete or accurate information about their flexibility
Solution Approach 1:
The system performs preliminary actions by predicting user acceptance probabilities for alternative settings before generating the final schedule. This allows the system to proactively identify and resolve potential conflicts or suboptimal arrangements by simulating user responses and adjusting schedules in advance, thereby improving schedule quality without requiring complex real-time negotiations with users.
Solution Approach 2:
The scheduling system serves itself by using predicted user acceptance probabilities to automatically refine and optimize schedules without requiring direct user intervention. The system independently evaluates alternative schedules, predicts user responses, and selects optimal arrangements, reducing the need for manual user input while improving schedule quality.
2Manufacturing precision
If the system requests users to adapt their settings frequently to improve schedules, then the schedule quality improves, but the user inconvenience increases
Solution Approach 1:
The system implements feedback by using predicted user acceptance probabilities to determine whether and how to request setting adaptations from users. Instead of frequently requesting changes, the system selectively presents only those alternative schedules that have high predicted acceptance probabilities, thereby improving schedule quality while minimizing user inconvenience by avoiding unnecessary requests.
Solution Approach 2:
The system changes parameters by adjusting the threshold for requesting user adaptations based on predicted acceptance probabilities. By dynamically modifying the criteria for when to request user input, the system balances schedule quality improvement with user convenience, requesting changes only when the predicted acceptance indicates minimal inconvenience.
3Manufacturing precision
If the system collects complete user preferences and constraints upfront, then the schedule quality can be optimized, but the information collection burden on users increases
Solution Approach 1:
The system reduces information collection burden by using predicted user acceptance probabilities to infer user preferences and flexibility without requiring explicit user input. Instead of collecting complete user preferences upfront, the system independently predicts how users would respond to various schedule alternatives, thereby obtaining necessary information without burdening users with extensive data entry.
Solution Approach 2:
The predicted user acceptance probability acts as an intermediary that bridges the gap between incomplete user inputs and comprehensive schedule optimization. Rather than requiring direct user input for all preferences and constraints, the system uses this predictive intermediary to infer missing information, reducing the information collection burden while maintaining schedule quality.
4Manufacturing precision
If the system evaluates multiple alternative schedules, then the schedule quality improves, but the computational time and resources increase
Solution Approach 1:
The system changes parameters by using predicted user acceptance probabilities to prioritize and filter alternative schedules for evaluation. Instead of evaluating all possible alternative schedules, the system focuses computational resources on alternatives with high predicted acceptance probabilities, thereby improving schedule quality while reducing computational time and resources.
Solution Approach 2:
The system applies partial action by evaluating only a subset of alternative schedules—specifically those with high predicted user acceptance probabilities—rather than exhaustively evaluating all possible alternatives. This selective evaluation approach achieves sufficient schedule quality improvement without the excessive computational time and resources that would be required for complete enumeration.
Data Source
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AI summary
A method, scheduling server and system for improving automated scheduling, wherein the automated scheduling is at least partially based on user settings input by a plurality of users includes: obtaining user settings for a plurality of users, the settings defining user preferences and personal constraints of each user, obtaining system boundary conditions derived from a schedule goal and technical conditions of the technical equipment involved to achieve a desired goal that shall be achieved by applying the schedule to be determined and calculating a schedule by solving a scheduling problem specified from a combination of the user specific settings and the system boundary conditions. Then, acceptance probabilities for alternative user settings are predicted, which are settings differing from the obtained user settings. At least one alternative user setting is selected and an alternative schedule for each selected alternative setting is calculated. The alternative schedules are compared to the initially calculated schedule with respect to quality measure, and, based on the result of the evaluation and the predicted acceptance probabilities, an alternative setting to be suggested to the user whose setting is concerned by the alternative setting is determined. After reading in the user's response to the suggested alternative setting another alternative setting is selected in case the user declined, or the systems proceeds with accepting the alternative setting as obtained user specific setting and repeats calculation of a schedule, selection of alternative settings, calculation of alternative schedules, evaluation and select and suggest an alternative setting and read in of the users response until a stop criterion is met, and apply the last calculated schedule.