Work Plan Constraint Violation Detection and Dynamic Adjustment
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Solution Overview
Problem
Existing work scheduling systems face challenges in automating shift scheduling for human resources due to numerous soft constraint conditions, which often result in mutual contradictions or unsatisfiable scenarios, requiring manual tuning of constraint priorities and weights, and struggle to adapt to changing work rules.
Innovation Solution
An information processing apparatus and method that includes a user interface for checking constraint violations and an update unit to dynamically adjust constraint information based on feedback, allowing for iterative generation and refinement of work plans to satisfy changing constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual tuning of constraint priorities and weights is used, then the system can handle soft constraint conditions, but the ease of operation deteriorates due to the complexity of manual adjustment
Solution Approach 1:
The system automatically learns and adjusts constraint priorities and weights through machine learning algorithms, eliminating the need for manual tuning. The planning support device performs self-service by continuously optimizing constraint parameters based on historical data and feedback, allowing the system to adapt to soft constraint conditions autonomously.
Solution Approach 2:
The system dynamically changes constraint parameters (priorities and weights) through automated learning processes. Instead of fixed manual settings, the parameters are continuously adjusted based on learned patterns from historical planning data, enabling the system to adapt to varying soft constraint conditions without manual intervention.
2Productivity
If conventional planning methods are used, then basic scheduling can be performed, but the productivity deteriorates due to the time-consuming manual maintenance of parameter settings
Solution Approach 1:
The system automatically maintains and updates constraint parameter settings through self-learning mechanisms, eliminating the time-consuming manual maintenance task. The planning support device continuously optimizes parameters based on historical data, freeing up time for actual plan generation and execution.
Solution Approach 2:
The system performs preliminary learning and parameter optimization in advance by continuously analyzing historical planning data. This preliminary action ensures that constraint parameters are already optimized before new planning tasks arise, reducing the time needed for parameter maintenance and improving overall productivity.
3Extent of automation
If case base or learning methods are used, then constraint conditions can be automatically determined, but the device complexity increases due to the need for large amounts of case data
Solution Approach 1:
The system uses feedback from planning outcomes and constraint violations to continuously improve constraint condition determination. Instead of requiring large external case databases, the system learns from its own operational feedback, reducing device complexity while maintaining high automation levels.
Solution Approach 2:
The planning support device performs self-learning by automatically determining constraint conditions from its own historical operational data and feedback. This self-service approach eliminates the need for external case base preparation and reduces system complexity while achieving automatic constraint determination.
Data Source
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AI summary
An information processing apparatus includes: a user interface that causes a user to check a constraint violation of a candidate plan of work to be performed by a plurality of workers; and an updater that updates constraint information including a first constraint condition related to the work based on the constraint violation of the candidate plan.