9 results about "STAFFING SCHEDULING" patented technology

The invention provides an integrated energy system multi-target scheduling method and device guided by a rule language. The method comprises the following steps: establishing a mathematical model for multi-target scheduling of an integrated energy system IES; converting a mathematical model of IES multi-target scheduling optimization into a Markov decision process MDP process by combining a large language model LLM, and constructing an LLM agent for multi-target guidance interaction; and solving an MDP process of IES multi-target scheduling optimization by using a multi-target depth deterministic policy gradient algorithm (DDPG) through the LLM intelligent agent to obtain an IES multi-target scheduling decision scheme. According to the method, the LLM and the deep reinforcement learning model are combined, so that multi-target preference guidance of human beings in the IES scheduling optimization process can be well realized, and theoretical and technical support is provided for scheduling departments and personnel to arrange an actual power generation plan of a power grid.

A dynamic AI-based resource planning system and calculator for optimizing fieldwork in renewable energy construction projects, the system comprising: (a) a data collection module configured to collect real-time and historical data from sources such as worker attendance records, weather data, equipment usage logs and project management software; b) a predictive analytics module configured to process the data using machine learning algorithms to predict work needs, potential project delays, and task prioritization; (c) a resource allocation module configured to dynamically allocate on-site workers to tasks based on forecast results, worker availability, skill levels and project schedules; (d) an interactive planning interface suitable for the visualisation, modification and simulation of personnel plans and project activities in real time; (e) a Smart Operational Resource Dashboard (SORD) configured to monitor and display key performance indicators, detect anomalies, and issue automatic alerts; and f) a reporting module configured to generate work, productivity and compliance reports, The system continuously adjusts labor deployment in response to changing on-site conditions to improve work efficiency and project results.

The invention relates to the technical field of employee scheduling, and particularly discloses an employee scheduling method and system based on supply chain order data, and the method comprises the following steps: S1, determining to-be-delivered goods in a logistics warehouse, and determining a target function; s2, determining constraint conditions of the target function, and solving the target function; and S3, calculating the scheduling proportion of the logistics warehouse according to the number m of vehicles required for the distribution of the to-be-distributed goods, and reminding workers to schedule employees of the logistics warehouse according to the scheduling proportion. According to the invention, the employees can be scheduled according to the to-be-delivered orders in the logistics warehouses, it is ensured that each logistics warehouse has enough employees to process the to-be-delivered goods, and the problems of resource waste and insufficient manpower are avoided.

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 invention discloses a water environment monitoring task scheduling method and device, equipment and a storage medium. The method comprises the steps of 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; wherein the task scheduling model is constructed based on a constraint rule and an optimization target related to a water environment monitoring task, in the operation process of the task scheduling model, the constraint rule is used for limiting the feasibility of a scheduling result, and the optimization target is used for optimizing the scheduling result on the premise that the constraint rule is met; the scheduling result comprises personnel arrangement information, vehicle configuration information, route planning information, task execution time information and material configuration information. Therefore, the scheduling efficiency of the water environment monitoring task can be improved.

The invention provides a clinical nursing personnel management method and system, and relates to the technical field of clinical nursing personnel scheduling management, and the method comprises the steps: obtaining clinical nursing personnel data; acquiring clinical nursing demand data of the next scheduling period in the current database; newly added clinical nursing demand data in the next scheduling period are predicted through the to-be-nursed object change prediction model; according to the clinical nursing personnel data, the clinical nursing demand data and the newly added clinical nursing demand data, obtaining clinical nursing personnel arrangement of a next scheduling period through a scheduling suggestion model; and periodically optimizing the scheduling suggestion model through the dynamic feedback module according to the working condition after scheduling. The method has the advantage of realizing more flexible and reliable nursing personnel scheduling management with strong adaptive capability.

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.