The embodiments of the present invention provide a dynamic production scheduling method, apparatus and electronic device based on deep reinforcement learning, which relate to the technical field of Industrial Internet of Things, and can reduce the overall processing time of jobs on the basis of not exceeding the processing capacity of production device. The embodiments of the present invention includes: acquiring static characteristics, dynamic characteristics of each of jobs and system dynamic characteristics, inputting the static characteristics, dynamic characteristics of each of jobs to be scheduled and system dynamic characteristics into a scheduling model to obtain a job execution sequence or batch execution sequence of the jobs in each production stage, wherein, the static characteristics of the job include an amount of tasks and time required for completion, the dynamic characteristics of the job include reception moment, and the system dynamic characteristics include a remaining amount of tasks that can be performed by the device in each production stage. The scheduling model is a model obtained after training a first actor network based on static characteristics and dynamic characteristics of a sample job, system dynamic characteristics, and a first critic network.

A method includes identifying a set of wafers, wherein each wafer is associated with a respective start time of a set of start times, determining whether the set of wafers includes an idle wafer, in response to determining that the set of wafers includes an idle wafer that is idle for a duration that exceeds a predefined threshold value, generating a modified set of start times by modifying at least the start time for the idle wafer, and initiating a computer simulation forecasting processing of the set of wafers using a wafer modification chamber and a wafer movement chamber based on the modified set of start times. The computer simulation uses a machine learning model trained based on a first duration to perform a first manufacturing task using the wafer modification chamber and a second duration to perform a second manufacturing task using the wafer movement chamber.

A method for manufacturing a series of parts using a manufacturing machine of a production line and a supervision device configured for controlling in real time the manufacturing machine taking into account quality data, carbon footprint and cost. The method includes receiving real time data from the production line, running a first prediction algorithm to predict quality data on the series of parts in real time, running a second prediction algorithm to predict a carbon footprint of the series of parts in real time, running a third prediction algorithm to predict the cost of the series of parts in real time, determining a set of scenarios based on the predicted carbon footprint and the predicted cost, selecting at least one scenario in the set of scenarios based on the predicted carbon footprint and/or the predicted cost, manufacturing the series of parts according to the selected scenario.

To provide a technique capable of making a schedule with good time efficiency in schedule making for the transport of substrates. A schedule making device makes a judgment as to whether a batch carrying-out procedure can complete the transport earlier than a sequential carrying-out procedure or not, and selectively employs these procedures in accordance with a result of the judgment. The sequential carrying-out procedure is a procedure in which substrates are transported to a predetermined transport destination in chronological order of the times at which the transport process can start, and the batch carrying-out procedure is a procedure in which a substrate the transport process of which can start is not transported until a time at which the transport process of a succeeding substrate can start, and the substrates are transported together to a transport destination at the time when the transport process of the succeeding substrate can start.

Embodiments of the present disclosure provide a material scheduling method and a material scheduling device for semiconductor processing equipment. The method includes establishing a material list, establishing a first scheduling task list according to process recipes and the material list, and inputting the first scheduling task list into a solver to calculate and output a scheduling result with shortest time for performing all material scheduling tasks in the first scheduling task list and parsing the scheduling result to obtain a movement sequence of all materials. In the technical solutions of the material scheduling method and the material scheduling device for the semiconductor processing equipment of embodiments of the present disclosure, the overall scheduling result can be improved, and the calculation speed can be improved. Thus, the scheduling result can be obtained in real-time.

A method includes identifying, by at least one processing device from a set of wafers, an idle wafer that is idle for a duration that exceeds a predefined threshold value, wherein each wafer of the set of wafers is associated with a respective start time of a set of start times, and initiating, by the at least one processing device, a computer simulation forecasting processing of the set of wafers using a wafer modification chamber and a wafer movement chamber based on a modified set of start times, wherein the computer simulation uses a machine learning model trained to perform a first manufacturing task using the wafer modification chamber and to perform a second manufacturing task using the wafer movement chamber, and wherein the modified set of start times is obtained by modifying at least one start time of the set of start times.

A production schedule estimation method and a production schedule estimation system are provided. The production schedule estimation method includes the following steps. Current-day work-in-process data, machine group cycle time data of a machine group, and productivity data of the machine group are obtained. The current-day work-in-process data, the cycle time data of the machine group, and the productivity data of the machine group are inputted into a prediction model. Current-day cycle time data and a current-day move volume for each of multiple stations in the machine group are calculated through the prediction model. And, current-day move data is calculated according to the current-day cycle time data and the current-day move volume for each of the multiple stations in the machine group.

Provided is a cell controller for controlling an operation of a machining cell including two or more machines and one or more robots as work resources, the cell controller being configured to control the operation of the machining cell based on a production program recorded with one or more processes to be executed at the time of producing only one article of a corresponding item in the machining cell among production programs prepared corresponding to one or more items produced in the machining cell.

The present invention relates to the generation of scheduling data for machinery. More particularly, the present invention relates to modelling machinery capabilities and states, planned inputs, outputs and constraints in order to generate priorities for a schedule for manufacturing machinery. Aspects and/or embodiments seek to provide a method and system of generating scheduling data that can be used in complex manufacturing settings such as semiconductor wafer fabrication plants.

The present disclosure belongs to the field of flexible job shop scheduling, and relates to a dual-effect scheduling method for heterogeneous robots in a flexible job shop. In a case of strong coupling of processing and transferring, this method comprehensively considers such constraints on selection of flexible manufacturing cells (FMCs), transferring time by automatic guided vehicles (AGVs) and processing resource waste, and improves encoding schemes and genetic operators with order completion time and minimization of resource consumption as evaluation criteria. Additionally, this method can fully apply environmental characteristics of job shop to scheduling design, and automatically design more precise scheduling schemes, overcoming the deficiencies of slow response and prone to local optimal solutions existing in conventional scheduling schemes, and ensuring efficient and green operation.