An information processing device includes one or more memories; and one or more processors coupled to the one or more memories and the one or more processors configured to specify a certain order in which a plurality of objects is processed in a work line, generate child individuals by evolving generations from parent individuals by a genetic algorithm, parent individuals being a certain number of individuals to which a feeding order of the part of the plurality of objects to the work line is given as input variables, acquire evaluation functions according to a plurality of objective functions, extract the certain number of individuals of which the evaluation functions satisfy a certain condition, as solutions, and repeat the generating, the acquiring, and extracting.

An AAMP system includes a plurality of AAMP system stations disposed in an environment and configured to perform one or more AAMP processing routines, and a plurality of robots configured to autonomously travel within the environment, where one or more robots from among the plurality of robots include an auxiliary AAMP processing station configured to perform one or more auxiliary AAMP processing routines. The AAMP system includes a controller configured to select an AAMP system station from among the plurality of AAMP system stations to perform the one or more AAMP processing routines based on AAMP system operation data and select a robot from among the plurality of robots to initiate the one or more AAMP processing routines at the selected AAMP system station based on a digital model of the environment and robot operation data, where the robot operation data includes an auxiliary processing state.

An embodiment method of operating a cell-based mobility production system for producing various types of mobilities includes assigning works required in each cell of a plurality of cells connected in series or in parallel that vehicle bodies need to go through based on the type of the mobility to be produced, determining a feeding order of the vehicle bodies based on the assigned works, and reassigning the works required in each cell that the vehicle bodies to be fed need to go through based on the determined feeding order, wherein each of assigning the works, determining the feeding order, and reassigning the works is performed by a processor.

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.

The present disclosure relates to an Industrial Internet of Things (IoT) system for controlling a production line parameter and a control method thereof. The Industrial IoT system 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 group creation unit creates in accordance with predetermined conditions at least one group that includes at least one of a plurality of products to be manufactured. A schedule creation unit creates a schedule for manufacturing the plurality of products for each of the at least one group. A display control unit displays on a display unit the schedule for each of the at least one group, together with information on a task for manufacturing each of the at least one of the plurality of products included in the at least one group.

Techniques are disclosed for task priority scheduling and resource allocation of autonomous agents. The scheduling may utilize sensor data characteristics to facilitate scheduling decisions. The present disclosure also provides refinement of task priority scheduling utilizing fleet management information-based scene and environment information, such as by using information available to the fleet management controller.

A method for time constraint management at a manufacturing system is provided. The method includes receiving a request to initiate a set of operations to be run at a manufacturing system, wherein the set of operations comprises one or more operations that each have one or more time constraints. The method further includes obtaining current data relating to a current state of the manufacturing system. The method further includes applying a machine-learning model to the current data to determine a candidate set of substrates to be processed during the set of operations. The method further includes initiating the set of operations on the candidate set of substrates based on an output of the machine-learning model.

A scheduling method using a knowledge graph (KG) performs following steps by a processor: obtaining an initial scheduling solution of the production system; converting the initial scheduling solution into triples to form the KG, each triples includes two entities and a relationship of the production system, the two entities indicate two production resources; embedding triples into a vector space to generate embedded vectors by a KG embedding technique; generating embedded vector combinations according to the embedded vectors and computing a distance of each of embedded vector combinations; and performing a scheduling algorithm to generate a target scheduling solution according to the embedded vector combinations, and providing reference information when the scheduling algorithm generates a schedule of a station, wherein the reference information comprises at least one of the embedded vector combinations associated with the station, and the distance corresponding to said at least one embedded vector combinations.

Provided are a method, computer program product, and system for repairing a defective component of a 3D printer. The method comprises identifying a defective component in a 3D printer. The method further comprises determining one or more repair actions for the defective component. The method further comprises determining an order of priority for a set of print jobs for the 3D printer and the one or more repair actions. The method further comprises performing the set of print jobs and the one or more repair actions in accordance with the determined order of priority.