Provided are a system, method, and computer program product for optimizing a manufacturing process. The method includes receiving manufacturing data associated with a manufacturing process for manufacturing a product. The manufacturing data may include data from a plurality of data sources associated with a plurality of stages of the manufacturing process, and the manufacturing data may include values for a plurality of parameters including at least one process parameter value and at least one quality parameter value. The method includes generating a time-sequenced data structure including the manufacturing data and transforming the time-sequenced data structure to a positionally-dimensioned data structure based on timing data associated with the plurality of stages. The method includes determining a new value for the at least one 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 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 process control device includes a deadlock determination part that determines whether or not a deadlock occurrence situation occurs when a work process that is being executed and a work process scheduled to be executed next are executed simultaneously by referring to process constraint information in which a plurality of work processes and each work state of a plurality of process execution elements of a manufacturing device are associated with each other, and a process execution control part that delays an execution timing of the work process scheduled to be executed next when the deadlock determination part has determined that the deadlock occurrence situation occurs.

The production management support system analyzes, on the basis of one or more attentional perspectives on a plurality of different models of products, past record information that includes information as a past record which shows, for each product loaded in a production system in which the plurality of different models of products are loaded and a sequential order of two or more of a plurality of steps is different depending on the model, an execution time of each of the steps and that serves as a basis for a production management chart showing a production situation, to detect, among display objects displayed in the production management chart, a display object satisfying one or more requirements associated with the one or more attentional perspectives. The system performs accentuated display on at least one of the detected display objects.

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.

In one embodiment, a model predictive control system for an industrial process includes a processor to execute an optimization module to determine manipulated variables for the process over a control horizon based on simulations performed using an objective function with an optimized process model and to control the process using the manipulated variables, to execute model modules including mathematical representations of a response or parameters of the process. The implementation details of the model modules are hidden from and inaccessible to the optimization module. The processor executes unified access modules (UAM). A first UAM interfaces between a first subset of the model modules and the optimization module and adapts output of the first subset for the optimization module, and a second UAM interfaces between a second subset of the model modules and the first subset and adapts output of the second subset for the first subset.

A controller of a process includes a resource consumption optimizer which receives sets of control actions for a plurality of actuators from an advanced process controller performing independent data processing with respect to the resource consumption optimizer, each set of control actions resulting in a set-specific combined control action effect. The resource consumption optimizer optimizes resource consumption associated with the sets of control actions by modifying at least one control action on the basis of resource consumption associated with the actuator performing the control action while keeping a difference between a combined control action effect of a modified control action set and a combined control action effect a received control action set set-specifically within an accepted range.

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.

A process control device includes a deadlock determination part that determines whether or not a deadlock occurrence situation occurs when a work process that is being executed and a work process scheduled to be executed next are executed simultaneously by referring to process constraint information in which a plurality of work processes and each work state of a plurality of process execution elements of a manufacturing device are associated with each other, and a process execution control part that delays an execution timing of the work process scheduled to be executed next when the deadlock determination part has determined that the deadlock occurrence situation occurs.