Provided are a system, method, and computer program product for optimizing a manufacturing process. The method includes generating a time-sequenced data structure associated with a manufacturing process and transforming the time-sequenced data structure to a positionally-dimensioned data structure by identifying a zone for each parameter of a plurality of parameters, determining a time delay factor for each zone, and generating the positionally-dimensioned data structure using a data matrix transformation based on the time-sequenced data structure, each zone, and each time delay factor. The method also includes identifying a set of empty entries in the time-sequenced data structure or the positionally-dimensioned data structure and imputing data. The method further includes determining a new value for a process parameter value based on the positionally-dimensioned data structure and at least one algorithm and optimizing the manufacturing process based on the new value.

A method for optimizing a sequence of processes for manufacturing of product units, includes: associating measurement results of performance parameters (e.g., fingerprints) with the recorded process characteristics (e.g., context); obtaining a characteristic (e.g., context) of a previous process (e.g. deposition) in the sequence already performed on a product unit; obtaining a characteristic (e.g., context) of a subsequent process (e.g., exposure) in the sequence to be performed on the product unit; determining a predicted performance parameter (e.g., fingerprint) of the product unit associated with the sequence of previous and subsequent processes by using the obtained characteristics to retrieve measurement results of the performance parameters (e.g., fingerprints) corresponding to the recorded characteristics; and determining corrections to be applied to future processes (e.g. exposure, etch) in the sequence to be performed on the product unit, based on the determined predicted performance parameter.

Some embodiments include a server system including a first logic module executable by a processor for receiving a data communication from an industrial control system coupled to a communications network. In some embodiments, the data communication comprises data or data streams associated with the industrial process. The program logic of the first logic module includes a model configured to receive a variable and iterate and converge an optimization problem to an optimization solution to a first level of optimization based at least in part on the variable and the data or data streams. A second logic module executable by the processor is operatively data-linked to the first logic module and utilizes a model to iteratively process data values of the optimization solution to a second level of optimization with an increased level of optimization over the first level of optimization.

A platform for processing a workpiece includes a transmission mechanism, a plurality of workstations and a plurality of movable vehicles. The transmission mechanism transports the workpiece to be processed to one of the plurality of workstations. Each movable vehicle includes a processing module and a docking interface adapted to connect with a docking station of each workstation. The movable vehicles are each adapted to move in and out of each workstation, and each processing module is adapted to process the workpiece transmitted to the workstation after the movable vehicle is moved in and positioned in the workstation.

A control system for a central plant having subplants including devices operating to serve energy loads of a building. The system includes a high level optimization module that performs high level optimization of thermal loads subject to constraints to generate subplant load allocations. The control system includes a low level optimization module that performs low level optimization of the subplant load allocations to determine operating states for the devices. The control system includes a constraint modifier that modifies the constraints for the high level optimization module based on equipment schedules. The control system also includes a binary optimization modifier including a pruner module that receives the minimum off schedule to determine adjusted branches and a seeder module that receives the minimum on schedule to determine a starting node for use in binary optimization performed by the low optimization module.

A method (600) for configuring one or more components of a process plant includes receiving (602) a change request (226) and receiving (604) a system dependency graph (228) corresponding to the process plant. The method (600) further includes selecting (606) a subset of components (230) among the plurality of components based on configuration of the process plant and identifying (608) a subset of nodes (232) among the plurality of nodes by traversing a path in the system dependent graph (228). The method (600) also includes computing (610) an impact parameter (234) value based on a traversed path and computing (612) a plurality of change parameter values (236) based on the traversed path. The method (600) further includes determining (614) an effort estimate (210) based on the impact parameter (234) value and the plurality of change parameter values (236) using a machine learning technique.

A method for optimizing a sequence of processes for manufacturing of product units, includes: associating measurement results of performance parameters (e.g., fingerprints) with the recorded process characteristics (e.g., context); obtaining a characteristic (e.g., context) of a previous process (e.g. deposition) in the sequence already performed on a product unit; obtaining a characteristic (e.g., context) of a subsequent process (e.g., exposure) in the sequence to be performed on the product unit; determining a predicted performance parameter (e.g., fingerprint) of the product unit associated with the sequence of previous and subsequent processes by using the obtained characteristics to retrieve measurement results of the performance parameters (e.g., fingerprints) corresponding to the recorded characteristics; and determining corrections to be applied to future processes (e.g. exposure, etch) in the sequence to be performed on the product unit, based on the determined predicted performance parameter.

A method for selecting optimum operation performance criteria for a metal working process. The method includes the step of developing a process model relating process parameters for the operation with performance variables for said operation, wherein the process parameters and performance variables are retrievable via integrated multiple data sources, and selecting at least one optimization technique to define a function, said function including process parameters. Moreover, the method includes generating the function for optimization by using acceptable tolerances of a product to be machined as a basis to define ranges for performance variables along with ranges for process parameters, and applying the at least one optimization technique to said function, whereby optimum operation performance criteria are calculated for the process model including process parameters and performance variables to obtain a set of requirements to be used for controlling the metal working process.

A method for selecting optimum operation performance criteria for a metal working process. The method includes the step of developing a process model relating process parameters for the operation with performance variables for said operation, wherein the process parameters and performance variables are retrievable via integrated multiple data sources, and selecting at least one optimization technique to define a function, said function including process parameters. Moreover, the method includes generating the function for optimization by using acceptable tolerances of a product to be machined as a basis to define ranges for performance variables along with ranges for process parameters, and applying the at least one optimization technique to said function, whereby optimum operation performance criteria are calculated for the process model including process parameters and performance variables to obtain a set of requirements to be used for controlling the metal working process.

A production schedule creating method includes: a schedule information acquiring step of acquiring production schedule information including a production sequence for producing a plurality of models of products, a commenceable time point, and a production deadline time point; a preparation time calculating step of calculating a preparation time taken for arrangement work of arranging members on arrangement means, for each of a plurality of models; a production time point calculating step of calculating a production commencing time point and a production end time point for each of the plurality of models; a schedule satisfaction determining step of determining whether or not a schedule satisfying condition is satisfied, for each of the plurality of models; and a sequence changing step of changing a sequence for producing an unsatisfied model that does not satisfy the schedule satisfying condition, in a case where the unsatisfied model is present.