15 results about "Turnaround time" patented technology

The invention relates to the technical field of intelligent manufacturing and supply chain collaborative management of woven garments, in particular to a garment production process management system based on artificial intelligence, and the system comprises a production network management center which is used for obtaining a node execution entropy value; the demand dynamic analysis unit is used for carrying out feature extraction analysis on the collected unstructured design data to obtain an order complexity coefficient; the strategy game matching unit is used for obtaining a steady-state scheduling signal or a recovery scheduling signal; when a steady-state scheduling signal is generated, the collaborative scheduling execution unit is used for generating a global optimal scheduling instruction, and minimizing turnover time is taken as an optimization target; when a recovery scheduling signal is generated, the collaborative scheduling execution unit is used for generating a risk recovery scheduling instruction, taking a minimum node execution entropy value as an optimization target, and actively inhibiting pursuit of turnover time; according to the method, the problem that the hidden risk of the node cannot be sensed in the prior art is effectively solved, and an accurate quantitative basis is provided for subsequent preventive scheduling.

The application discloses a process scheduling test method of an embedded operating system and a related device. The method comprises the following steps: obtaining a process scheduling algorithm to be tested of an embedded operating system to be tested, and writing the process scheduling algorithm to be tested into a pre-set process scheduling algorithm library; determining a corresponding typical test case set and a random test case set according to the process scheduling algorithm to be tested, and executing the typical test case set and the random test case set in a simulated embedded operating system by using the process scheduling algorithm in the process scheduling algorithm library to determine an average turnaround time set; taking the scheduling process algorithm with the minimum average turnaround time in the average turnaround time set as an optimal process scheduling algorithm, and recording first process scheduling information of the optimal process scheduling algorithm; executing the optimal process scheduling algorithm in the embedded operating system to be tested, recording second process scheduling information of the optimal process scheduling algorithm, and determining a test report according to the first process scheduling information and the second process scheduling information.

A system and a method directed to a monitoring system of semiconductor processing chambers is provided. In particular, monitoring of any chemical formation on a chamber and a wafer of a semiconductor processing chamber using in-situ laser induced fluorescence is provided. The monitoring system and method detect issues before they become a problem for the semiconductor processing chambers by providing diagnosis on chamber health and mechanisms for associated process shifts with a faster turnaround time.

A method performed by an electronic device for predicting a turnover time of a container is disclosed. The method includes obtaining shipping data associated with shipping of one or more containers. The method includes generating a turnaround time parameter indicative of a turnaround time of the container by applying a predictive model to the shipping data based on the shipping data. The method includes providing an output based on the turnaround time parameter.

A method that includes obtaining a minimum deactivation time for each processing device (203) of a processing system configured to produce one or more materials and operate in accordance with a deactivation schedule. The deactivation schedule includes a calendared deactivation period for each processing device (203). The method further includes obtaining a maximum turnaround time for the processing system, obtaining a set of conditions (311) for the deactivation schedule and determining, subject to the set of conditions (311), an optimum deactivation schedule (317) by optimizing a production objective. The conditions require that the calendared deactivation period of each processing device lasts at least the minimum deactivation time, and a total duration of the deactivation schedule does not last longer than the maximum turnaround time. The conditions also seek to reduce overlap between calendared deactivation periods. The method further includes performing a system maintenance (319) of the processing system according to the optimum deactivation schedule (317).

This application discloses a train timetable optimization method for urban rail transit networks considering transfer coordination, belonging to the field of urban rail transit technology. The method includes: acquiring network topology, transfer station information, train operation parameters, and passenger flow data; constructing a timetable optimization model and generating an initial timetable, considering constraints such as departure intervals, station dwell times, travel times, and turnaround times, with the objective of minimizing the total travel time and operating costs of passengers on the line; identifying the set of transfer stations and direction mappings based on the network topology, and extracting train arrival and departure events from the timetable; optimizing the train time windows at each transfer connection point with the objective of minimizing the total waiting time for transfers across the entire network, forming a time window contract; adding this contract to the optimization model and resolving it to obtain a new timetable; and outputting the final timetable by judging whether the improvement in transfer waiting time has converged. This method effectively improves the overall service level and economic benefits of multi-line networks while reducing computational complexity.

The present application relates to the technical field of intelligent manufacturing and supply chain collaborative management of clothing, in particular to a clothing production process management system based on artificial intelligence, comprising: a production network management center for obtaining node execution entropy; a demand dynamic analysis unit for performing feature extraction analysis on collected unstructured design data to obtain an order complexity coefficient; a strategy game matching unit for obtaining a steady-state scheduling signal or a recovery scheduling signal; when the steady-state scheduling signal is generated, a collaborative scheduling execution unit is used to generate a global optimal scheduling instruction with the optimization objective of minimizing the turnaround time; when the recovery scheduling signal is generated, the collaborative scheduling execution unit is used to generate a risk repair scheduling instruction with the optimization objective of minimizing the node execution entropy and actively suppressing the pursuit of turnaround time; the present application effectively solves the problem that the prior art cannot perceive the hidden risks of nodes, and provides accurate quantitative basis for subsequent preventive scheduling.

Randomization methods for healthcare scheduling optimization using perioperative stages. Poor scheduling of surgical appointments and procedures in operating rooms can lead to unnecessary downtime, and therefore loss of efficiency. The randomization methods include various probability models, Monte Carlo simulations, and stochastic optimization is used to optimize procedure scheduling in operating rooms. The optimized schedule may be based on estimated procedure duration, estimated turn-around-time, estimated cancellation frequency, forecasted emergency operating room usage, estimated surgeon utilization, and hospital site configuration. A probabilistic machine learning model may be trained based on historic data and ongoing performance data to automate the optimization process and increase accuracy based on up-to-date information and statistics.

A memory includes interface circuitry for receiving at least one write command. Multiple memory banks are organized into multiple memory bank groups and operate according to a corresponding memory bank group timing sequence. Each of the multiple memory banks includes a memory cell array. Multiple write buffers temporarily store write data associated with at least one write command. Write buffer selection circuitry egresses the temporarily stored write data from a selected buffer among the multiple write buffers based on the memory bank group timing sequence and directs the egressed write data to a given array of memory cell arrays.

A system includes an intermediate mortgage integration platform as a service (MiPaas). Specifically, an MiPaas layer may facilitate integration between a loan origination system (LOS) and supporting services. The MiPaas layer may provide integration flexibility by enabling integration of the supporting services with each other and with different LOSs. Indeed, a number of MiPaas integration formats (e.g. REST API, OpenAPI, GRPC, GraphQL, Industry Standard interface, etc. may be provided to facilitate ease of integration between the LOS and the supporting services. Further, the MiPaas may provide processing efficiencies by adapting workflows based upon particular retrieved data. In this manner, efficient processing may be performed in an intermediate data processing layer, resulting in more efficient turnaround times and reduced processing requirements at traditionally over-burdened systems.

The application discloses an airport flight scheduling method and device based on big data, and a medium, to solve the technical problem that the existing airport flight scheduling is greatly different from the actual scheduling. It includes: determining the transit duration of each transfer of the to-be-scheduled flight of the flight schedule according to the historical scheduling information of the flight schedule, and determining the average duration corresponding to the to-be-scheduled flight according to the transit duration of each transfer, taking the average duration as the reference duration of the to-be-scheduled flight; determining the influence coefficient corresponding to each influence factor that has an influence on the to-be-scheduled flight according to the reference duration and the historical scheduling information, and determining the expected turnaround time corresponding to the to-be-scheduled flight according to each influence coefficient and the reference duration; arranging the flight time corresponding to all to-be-scheduled flights of the day based on the expected turnaround time corresponding to all to-be-scheduled flights of the day, and scheduling the flight schedule of the to-be-scheduled flight in the airport according to the arranged flight time.

Systems and methods to receive a computing job from an Electronic Design Automation (EDA) software application, and dynamically determine at least one precedence or successor job constraint for the received computing job, are described herein. Further, an edge inference algorithm is used to determine edges of a Dynamic Acyclic Graph (DAG) representing the EDA software application computing jobs, along with jobs that are dependent on the received computing job. In this way, job dependencies are discovered and scheduled dynamically, reducing turnaround time, and increasing efficiency of computing resources.

A riserless marine package enables post-BOP utilization of both drill centers simultaneously using an offset riser system to significantly reduce turnaround time between drilling and casing operations and reduce associated costs. A sealing system of the riserless marine package, disposed above the subsea BOP, separates the drilling mud below the sealing system from the seawater above. Drillstrings and casing strings are run through the seawater and transit through the sealing device of the riserless marine package. The rig may assemble and operate a drillstring from the first drilling position and assemble and operate a casing or liner string in the second drilling position. Upon reaching total depth using the offset riser, the rig may trip the drillstring until the BHA clears the subsea BOP. Once the BHA clears the subsea BOP, the rig may then move to insert a casing string hanging from the second drilling position into the well.

The present invention discloses a cluster-based strategy-based joint planning method and system for container port berth and yard allocation. The method comprises: collecting information on ships, container trucks, and yards during a target time period, including the planned arrival time of ships, the number and type of containers on board, the stockpiling status of the yard, and the planned arrival time of external container trucks; inputting the collected information into a pre-established multi-objective mixed integer programming model to calculate an optimal planning strategy, including a berthing plan for all ships and a loading and unloading plan for container batches during the specified time period; wherein the pre-established multi-objective mixed integer programming model includes three objective functions, namely, minimizing the expected turnaround time for ship berthing, minimizing the total container transport distance between berths and container areas, and minimizing the workload imbalance in the container areas of the yard; and executing ship and yard operations according to the optimal planning strategy. The present invention can improve the overall operational efficiency of the terminal.

A method for reducing pre-authorization turnaround time is disclosed. The method includes, at a database, receiving historical data including a historical pre-authorization request and clinical information associated with a historical pre-authorization request. The method further includes receiving real-time data using an API gateway including real-time pre-authorization requests wherein the real-time data includes the real-time pre-authorization procedure and a clinical document category. The method further includes removing irrelevant data from real-time data and historical data to produce clean historical data and clean real-time data. The method further includes extracting data features required to train a machine learning model from the clean historical data and clean real-time data. The method further includes training the machine learning model by applying the extracted data features from the clean historical data and clean real-time data. The method further includes identifying prediction data results by applying the trained machine learning model.