A manufacturing system for a biopharmaceutical product includes first and second sets of biopharmaceutical manufacturing equipment located at a first and second enterprise sites, and memory configured to store an enterprise configuration and a process specification. The enterprise configuration includes records of one or more equipment parameters of the multiple pieces of equipment of the first and second sets of biopharmaceutical manufacturing equipment. At least one processor is configured to execute instructions determine whether at least one of the first enterprise site and the second enterprise site is capable of manufacturing the biopharmaceutical product, transmit a generated set of instructions to the determined at least one of the first enterprise site and the second enterprise site, and operate the multiple pieces of equipment at the determined at least one of the first enterprise site and the second enterprise site to manufacture the biopharmaceutical product.

The present disclosure relates to an Industrial Internet of Things (IoT) and a method for controlling a balance rate of a production line. The Industrial IoT includes an user platform, a service platform, a management platform, a sensor network platform, and an object platform interacting in sequence, the service platform adopts a centralized arrangement, the management platform adopts an independent arrangement, and the sensor network platform adopts a rear sub-platform arrangement.

A material processing path selection method includes calculating a plurality of candidate material processing paths, determining a bottleneck process tank, and for each of the plurality of candidate material processing paths, calculating a bottleneck process tank utilization rate to select a candidate material processing path with a highest bottleneck process tank utilization rate in the plurality of candidate material processing paths as a target material processing path. The bottleneck process tank is a process tank having a highest use frequency among all process tanks, A use frequency of the process tank is equal to a total process time length of all materials that need to be transferred to the process tank divided by a number of all the materials that need to be transferred to the process tank.

A manufacturing facility and a manufacturing method are provided herein to manufacture a product according to a specification in a digital manufacturing order. Manufacturing system units to be used are automatically scheduled (S2) and controlled (S3). While performing the manufacturing method, it is determined (S4) whether inspection data for a production result meet assessment criteria. Upon determining that a production result does not meet an assessment criterion, a derived manufacturing order is generated that specifies which production step(s) need to be repeated to obtain a production result that is more likely to meet the assessment criterion. The scheduling is updated in accordance with the derived manufacturing order and optionally newly received manufacturing orders resulting in a continuous manufacturing process.

The present invention discloses an adaptive-learning intelligent scheduling unified computing frame and system for industrial personalized customized production. Based on a deep neural network and reinforcement learning, a most suitable optimization algorithm is selected by automatic decision-making for a global customized production task with an industrial big data module at the bottom as an information basis, and a global optimal static scheduling plane is generated; a current dynamic event in a factory are monitored in real time; if no dynamic event requiring dynamic scheduling optimization is monitored, the global optimal static plan is executed sequentially; when a dynamic event impact requiring dynamic scheduling optimization is monitored, information of the current dynamic event is interpreted and classified, and corresponding optimization algorithms are automatically selected for dynamic scheduling optimization according to different types of dynamic events; and a dynamic scheduling scheme is evaluated by a subsequent module, an optimization scheme is regenerated or a most suitable optimization algorithm is automatically decided based on the scheme according to an evaluation result, and an equipment deployment sequence is generated for an automatic deployment. Considering the features of complicated procedures, a large amount of customization information and the high frequency of diversified dynamic events in personalized customized production, the present invention provides the adaptive-learning intelligent scheduling unified computing frame and system for industrial personalized customized production, that adopt two steps in the three aspects of static scheduling planning, dynamic scheduling planning and equipment deployment based on deep learning, that is, targeted optimization is performed after classification, thus improving the optimization efficiency and effect; and the system better fits the features of personalized customized production, and can effectively improve the efficiency of personalized customized production and minimize manual decision-making costs.

A system and method include dividing, by a processor of a manufacturing execution system (MES), a time axis associated with a time window into a plurality of time slots, assigning an integer value to an integer variable indexed by a slot identifier, a machine identifier, and a wafer lot identifier, specifying one or more constraints based on the integer variable, wherein the one or more constraints comprise a wafer quantity constraint and a Q-time constraint, executing an optimization solver under the one or more constraints to determine a time and a quantity of wafer lots to be provided to each machine associated with the time window, and issuing a request to a controller to cause provision of the quantity of wafer lots to each machine associated with each step in the time window.

An operation state display device displays an operation state of a board production facility in which a board is sequentially conveyed to multiple board work machines, which perform predetermined board work on the board, and the board work is sequentially performed to produce a board product, and includes an acquisition section and a display section. The acquisition section acquires an execution time zone in which setup changing work required for switching a type of the board product to be produced is performed, for each board work machine, based on production information acquired by the board production facility during production of the board product. The display section graphically displays at least one of the execution time zone for each of the multiple board work machines and the execution time zone for a predetermined board work machine of the board production facility in time series.

The present invention relates to a dynamic scheduling system and method of a dyeing process using a genetic algorithm, and more particularly, to technology which performs an optimized process corresponding to an ordered work command in a dyeing process by using process scheduling based on a genetic algorithm to increase production efficiency.

Provided is a schedule creation device for creating a conveyance schedule according to which a conveyance device conveys a product to work positions designated for each manufacturing process. Said schedule creation device sets a first score to the work positions and to a conveyance path of the conveyance device; sets a second score for products; and uses the first score and the second score to determine the product to be conveyed by the conveyance device from among products present at a plurality of work positions.

The present application relates to the technical field of semiconductors, and in particular, to a wafer scheduling method and a wafer scheduling apparatus for an etching equipment. The wafer scheduling method includes: obtaining a wafer processing request, where the wafer processing request includes at least process information of wafers and an equipment processing parameter of the etching equipment; responding to the wafer processing request, and determining a wafer scheduling parameter corresponding to the process information and the equipment processing parameter, based on the process information, the equipment processing parameter, and a preset wafer scheduling policy, where the wafer scheduling parameter is used to determine a transfer time for transferring the wafers to the etching equipment for processing; and performing wafer scheduling processing on the wafers by using the wafer scheduling parameter. In this way, the wafer processing productivity of the etching equipment can be improved.