Constraint Learning for Crew Scheduling Plans
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Solution Overview
Problem
Current methods for work planning, such as crew scheduling, face challenges in balancing and tuning priority and weight of soft constraint conditions, which often conflict with each other, and require extensive manual tuning and large amounts of expert-created case data, making them difficult to implement and maintain.
Innovation Solution
An information processing apparatus that includes a plan generator and an updater, which iteratively generates candidate plans and updates constraint information based on constraint violations, learning and adjusting weights and conditions to satisfy a wide variety of constraints dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual tuning of priority and weight for each constraint condition is performed, then the plan can satisfy specific workplace constraints, but the system complexity and maintenance burden increase significantly
Solution Approach 1:
The system automatically learns constraint conditions and their weights from past planning cases without requiring manual tuning. The learning unit acquires constraint information from historical data and automatically determines appropriate weights, enabling the system to adapt to specific workplace constraints while maintaining low operational complexity
Solution Approach 2:
The system pre-accumulates constraint conditions and their weights from historical planning cases before actual planning occurs. By learning from past cases in advance, the system prepares a knowledge base that can be directly applied to new planning tasks, eliminating the need for manual parameter setting when constraints change
2Adaptability or versatility
If all soft constraint conditions are extracted and reflected as constraint conditions, then the plan can comprehensively satisfy workplace requirements, but the data preparation burden and system complexity increase
Solution Approach 1:
The system accumulates constraint conditions from past planning cases in advance, building a comprehensive knowledge base before actual planning. This preliminary data collection and learning process enables the system to comprehensively capture workplace requirements without requiring intensive manual data preparation at implementation time
Solution Approach 2:
The system extracts and copies constraint conditions from historical planning cases, replicating successful constraint configurations from the past. By copying proven constraint patterns from historical data, the system comprehensively captures workplace requirements while avoiding manual re-specification of each constraint
3Measurement precision
If expert-created case data is prepared for case-based approach or learning methods, then accurate constraint conditions can be learned, but the barrier to practical use increases due to large data requirements
Solution Approach 1:
The system automatically learns constraint conditions and weights from available past planning cases without requiring expert annotation or manual creation of training data. The learning unit processes historical data autonomously, extracting meaningful constraint patterns and weights, thereby maintaining learning accuracy while eliminating the barrier of extensive expert data preparation
Data Source
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Figure 4A~4B
AI summary
According to one approach, an information processing apparatus includes: a plan generator to generate a first candidate plan for work performed by a plurality of workers based on constraint information including a first constraint condition for the work; and an updater to acquire a second constraint condition for the work based on the first candidate plan and update the constraint information based on the second constraint condition.