Real-time intervention of an industrial process can include searching for a batch of candidate configurations for use by the industrial process, the batch of candidate configurations searched for by performing a batch Bayesian optimization (BBO). The batch of candidate configurations is transmitted to the industrial process to use in running the industrial process. A result of the run is received from the industrial process. Using the result in the BBO, a next batch of candidate configurations is searched. Whether a stopping criterion is met is determined, based on the next batch of candidate configurations and by applying a function to a BBO acquisition score. Responsive to determining that the stopping criterion is met, searching for the next batch of candidates is terminated.

Computer-based methods and systems provide automated batch data alignment for a batch production industrial process. An example embodiment selects a reference batch from batch data for a subject industrial process and configures batch alignment settings. In turn, a seed model configured to predict alignment quality given settings for one or more alignment hyperparameters is constructed. Collectively the selected reference batch, the configured batch alignment settings, the constructed seed model, and a set of representative batches, representative of the batch data for the industrial process, are used to perform at least one of: (i) automated active learning, (ii) interactive active learning, and (iii) guided learning to determine settings for the one or more alignment hyperparameters. Then, a batch alignment is performed using the determined settings for the one or more alignment hyperparameters and the configured batch alignment settings. The resulting aligned batch data of the subject industrial process enables improved modeling and control of batch productions by the subject industrial process.

Real-time intervention of an industrial process can include searching for a batch of candidate configurations for use by the industrial process, the batch of candidate configurations searched for by performing a batch Bayesian optimization (BBO). The batch of candidate configurations is transmitted to the industrial process to use in running the industrial process. A result of the run is received from the industrial process. Using the result in the BBO, a next batch of candidate configurations is searched. Whether a stopping criterion is met is determined, based on the next batch of candidate configurations and by applying a function to a BBO acquisition score. Responsive to determining that the stopping criterion is met, searching for the next batch of candidates is terminated.

Computer-based methods and systems provide automated batch data alignment for a batch production industrial process. An example embodiment selects a reference batch from batch data for a subject industrial process and configures batch alignment settings. In turn, a seed model configured to predict alignment quality given settings for one or more alignment hyperparameters is constructed. Collectively the selected reference batch, the configured batch alignment settings, the constructed seed model, and a set of representative batches, representative of the batch data for the industrial process, are used to perform at least one of: (i) automated active learning, (ii) interactive active learning, and (iii) guided learning to determine settings for the one or more alignment hyperparameters. Then, a batch alignment is performed using the determined settings for the one or more alignment hyperparameters and the configured batch alignment settings. The resulting aligned batch data of the subject industrial process enables improved modeling and control of batch productions by the subject industrial process.

A system and method relating to determining, by a processing device, a first number of parts waiting to be processed in a subsequent step by a plurality of machines housed in a plurality of buildings interconnected by rails, determining a second number of parts that the plurality of machines housed in the plurality of buildings is capable of processing in the subsequent step of the manufacture process over a determined period of time, calculating a capability occupancy ratio based on the first number and the second number for each one of the plurality of buildings, determining a target building of the plurality of buildings based on the capability occupancy ratio, and causing to dispatch the part to the target building via the rails.

A system and method relating to determining, by a processing device, a first number of parts waiting to be processed in a subsequent step by a plurality of machines housed in a plurality of buildings interconnected by rails, determining a second number of parts that the plurality of machines housed in the plurality of buildings is capable of processing in the subsequent step of the manufacture process over a determined period of time, calculating a capability occupancy ratio based on the first number and the second number for each one of the plurality of buildings, determining a target building of the plurality of buildings based on the capability occupancy ratio, and causing to dispatch the part to the target building via the rails.

A method and system for scheduling tasks using a counter constraint. A method may include identifying multiple tasks to be performed, receiving dependency data indicating that scheduling of at least one task is dependent on whether a counter satisfies a threshold in relation to an additional condition, and upon determining, during scheduling, that the counter satisfies the threshold in relation to the additional condition, triggering a scheduling action with respect to at least one task.