4 results about "STAFFING SCHEDULING" patented technology

The application relates to the technical field of medical staff scheduling, in particular to a medical staff scheduling system and method based on a prediction model, which comprises an information module, a scheduling module and a management module; the information module acquires patient and medical staff information of a current department; the scheduling module predicts short-term workload through a prediction model and schedules through a self-adaptive optimization algorithm; and the management module is used for receiving scheduling results and adjusting the same; the application statistically classifies patient data and medical staff data, predicts short-term workload according to the prediction model, thereby reasonably scheduling and guaranteeing the balance of medical staff and patient demands.

The application discloses a water environment monitoring task scheduling method and device, equipment and a storage medium. The method comprises the following steps: obtaining input data related to water environment monitoring task scheduling; inputting the input data into a pre-constructed task scheduling model; and outputting a scheduling result of the water environment monitoring task by the task scheduling model based on the input data. The task scheduling model is constructed based on constraint rules and optimization objectives related to the water environment monitoring task. During the operation of the task scheduling model, the constraint rules are used to limit the feasibility of the scheduling result, and the optimization objectives are used to optimize the scheduling result under the premise that the constraint rules are met. The scheduling result comprises personnel arrangement information, vehicle configuration information, route planning information, task execution time information and material configuration information. In this way, the scheduling efficiency of the water environment monitoring task can be improved.

This invention discloses an intelligent forecasting and decision-making optimization method and system for the catering supply chain, addressing issues such as insufficient demand forecasting accuracy, imbalanced inventory management, and disconnect between forecasting and execution. The method includes: collecting and fusing multi-source heterogeneous data containing real-time inventory and historical sales; extracting trend and periodic components based on the multi-source heterogeneous data, synthesizing a comprehensive adjustment factor for external events and applying inventory truncation constraints, outputting sales forecast results including uncertainty intervals; decomposing the forecast results into time-segmented customer flow forecasts and pressure forecasts; combining inventory and operational constraints for multi-objective decision optimization, generating dynamic menu strategies, ordering suggestions, and staff scheduling plans; executing the above strategies and feeding back actual execution data to achieve model closed-loop calibration. This invention achieves automated linkage from forecasting to execution, effectively reducing food waste rates and preventing service disruptions during extreme peak periods.

The invention relates to the field of data analysis, in particular to a logistics management intelligent analysis method for a smart campus, and the method comprises the steps: carrying out the collection and preprocessing of order transaction records in a peak business period, and obtaining a time sequence load sequence; carrying out coupling analysis on the geometrical morphology abrupt change characteristics in the local time window and the global passenger flow scale weight to obtain a transient topology saliency divergence factor; performing comparative analysis on the local cross minimum error and the global error baseline to obtain a time sequence phase shift adaptive attenuation mask; performing combined correction based on a transient topology saliency divergence factor and a time sequence phase shift adaptive attenuation mask on the basic Euclidean distance to obtain comprehensive distance measurement; the logistics personnel scheduling and meal preparation scheduling strategy is obtained through time sequence clustering calculation based on comprehensive distance measurement, and the problem that the Euclidean distance in existing time sequence clustering cannot identify passenger flow transient concurrency difference and tolerate tiny time sequence dislocation at the same time is solved.