Nursing Service Scheduling With Demand Forecasting and Constraints

The patent replaces manual mechanical scheduling processes with an automated computer-based system that uses optimization algorithms. The system automatically processes scheduling constraints, generates schedules, and handles modifications, eliminating the need for manual calculation and reducing time consumption while improving scheduling efficiency.

The system transforms the scheduling problem from a manual process into a computational one by changing parameters such as processing speed, data handling capacity, and calculation precision. The computer system processes scheduling parameters much faster and more accurately than manual methods, achieving high productivity without proportionally increasing complexity.

The patent incorporates feedback mechanisms where the system evaluates generated schedules against multiple objectives (patient care quality, nurse satisfaction, operational cost) and iteratively refines the scheduling. The optimization algorithms use feedback from constraint violations and objective function values to improve scheduling precision while maintaining efficiency.

The system dynamically adjusts scheduling parameters and algorithms based on real-time hospital data such as patient admissions, nurse availability, and operational constraints. This dynamic adaptation allows the system to achieve high precision for multiple objectives without sacrificing productivity, as the system learns from changing conditions and optimizes accordingly.

The patent applies predictive analytics and machine learning models in advance to forecast nursing demand and identify scheduling patterns before actual scheduling occurs. By pre-processing historical data and training models beforehand, the system achieves high forecasting accuracy without requiring complex real-time computation during the actual scheduling process, thus managing complexity effectively.

The system segments the scheduling problem into separate functional modules: data collection, predictive analytics, optimization, and schedule generation. Each module handles specific tasks independently, allowing the complex machine learning components to be isolated and managed separately from the optimization algorithms, reducing overall system complexity while maintaining high accuracy.

The patent applies optimization algorithms that balance thoroughness with efficiency by using heuristic methods and guided search techniques. Instead of exhaustively checking all possible schedules (which would be extremely time-consuming), the system uses intelligent algorithms that explore promising solutions more efficiently, achieving high optimization quality within acceptable computational time limits.

The system performs preliminary actions by pre-calculating constraint violations, pre-ordering scheduling decisions, and using data-driven predictions to narrow down the search space before applying optimization algorithms. This preliminary processing reduces the computational burden on the main optimization algorithms, achieving high schedule quality without excessive computational time.