3results about How to "Improve scheduling" patented technology

This invention belongs to the field of airport public transportation allocation technology, and discloses an airport public transportation pre-allocation processing system and method based on a big data model. The method includes: acquiring historical data; using big data processing technology and machine learning and deep learning models to infer passenger demand and public transportation usage from historical data, generating a data projection model; inputting real-time weather, time, and passenger data into the data projection model, employing intelligent optimization algorithms, and dynamically adjusting public transportation resource allocation in conjunction with real-time traffic conditions; monitoring the public transportation resource allocation results in real time, continuously optimizing and dynamically adjusting the results using a feedback mechanism, and pushing the latest adjusted results to downstream third-party systems. This invention utilizes real-time data analysis and intelligent optimization algorithms to achieve a more efficient traffic scheduling scheme, and has significant practical implications and broad application prospects.

The application belongs to the field of workshop scheduling, and particularly discloses a mixed flow shop dynamic scheduling method and system considering machine predictive maintenance, which comprises the following steps: taking minimizing total completion time, maintenance cost and processing cost as the target, establishing a mixed flow shop scheduling problem as a multi-objective joint optimization model, setting the same number of intelligent agents as the number of processing stages, and constructing a Markov decision process; each intelligent agent has an independent scheduling network, including a workpiece scheduling network and a machine scheduling network; based on the Markov decision process, the intelligent agents are trained, the workshop is maintained at the operation and maintenance point, and the workpiece and machine selection are respectively performed by calling the workpiece scheduling network and the machine scheduling network at the scheduling point; after the training is completed, the trained intelligent agents are used to realize the dynamic scheduling of the workshop. The application effectively overcomes the mixed flow shop dynamic scheduling problem considering machine predictive maintenance by integrating the workshop scheduling of machine operation and maintenance, and has good dynamic and adaptability.

This invention discloses a dual-cycle collaborative scheduling method, system, equipment, and medium for sea-rail intermodal AGVs, comprising: constructing a multi-resource collaborative scheduling model with minimizing completion time as the objective, setting train arrival time as a dynamic trigger parameter; judging the execution timing of direct-fetch tasks based on this, splitting them into sub-tasks from shore to yard and then to train if the arrival time is earlier, otherwise maintaining direct-fetch; constructing dual-cycle constraints in time periods and matching import and export tasks to generate operation sequences; finally, through multi-layer coding mapping, using an improved whale algorithm to iteratively solve and output the optimal scheduling scheme. This invention significantly improves equipment utilization, operation continuity, and overall system scheduling efficiency under the sea-rail intermodal mixed operation mode.