A storage medium storing a work plan specifying program that causes a computer to execute a process that includes specifying a certain order under conditions that, in a work line that generates a plurality of objects in the certain order, a certain amount of a material is partially cut from a raw material for each of the plurality of objects, the cut material is processed, the raw material is switched to a next raw material when the raw material lacks, and a changeover is performed to change the settings of the cutting device and the proceeding; and optimizing an objective function that reflects the changeover and an excess amount of each raw material that are determined according to an input order of the plurality of objects to the work line.

A Multi-Purpose Dynamic Simulation and run-time Control platform includes a virtual process environment coupled to a physical process environment, where components/nodes of the virtual and physical process environments cooperate to dynamically perform run-time process control of an industrial process plant and/or simulations thereof. Virtual components may include virtual run-time nodes and/or simulated nodes. The MPDSC includes an I/O Switch which delivers I/O data between virtual and/or physical nodes, e.g., by using publish/subscribe mechanisms, thereby virtualizing physical I/O process data delivery. Nodes serviced by the I/O Switch may include respective component behavior modules that are unaware as to whether or not they are being utilized on a virtual or physical node. Simulations may be performed in real-time and even in conjunction with run-time operations of the plant, and/or simulations may be manipulated as desired (speed, values, administration, etc.). The platform simultaneously supports simulation and run-time operations and interactions/intersections therebetween.

The present application generally pertains to scaling of a production process to produce a chemical, pharmaceutical and/or biotechnological product and/or of a production state of a respective production equipment. Particularly, there is provided a computer-implemented method of scaling a production process to produce a chemical, pharmaceutical and/or biotechnological product, the scaling being from a source scale to a target scale, wherein the production process is defined by a plurality of steps specified by one or more process parameters controlling an execution of the production process, the method comprising: (a) retrieving: parameter evolution information that describes the time evolution of the process parameter(s); a plurality of recipe templates, wherein a recipe comprises the plurality of steps defining the production process, and wherein a recipe template is a recipe in which at least one of the process parameters specifying the plurality of steps is a parameter being variable and having no predetermined value at the outset; (b) receiving: a source setup specification of a source setup to be used for executing the production process at the source scale, the source setup specification comprising the source scale value; a target setup specification of a target setup to be used for executing the production process at the target scale, the target setup specification comprising the target scale value; a source recipe defining the production process at the source scale; at least one acceptability function defining conditions for the values of the process parameter(s) at the source scale and/or at the target scale; (c) simulating the execution of the production process at the source scale using the source setup specification, the source recipe and the parameter evolution information; (d) determining, from the simulation, one or more source trajectories for the process parameter(s), wherein a trajectory corresponds to a time-based profile of values recordable during the simulated execution of the production process; (e) performing a target determination step comprising: selecting a recipe template pertinent to the production process out of the plurality of recipe templates; providing an input value for the at least one variable parameter in the selected recipe template; simulating the execution of the production process at the target scale using the target setup specification, the selected recipe template, the input value for the at least one variable parameter and the parameter evolution information; determining, from the simulation, one or more target trajectories for the process parameters; comparing the source trajectory(ies) and the target trajecto

The disclosure relates to digital models of a production apparatus. The digital models can generate simulations of production sequences of the production apparatus, and a controller can access the simulation to improve operations of the production apparatus. The digital model uses data of a locating system to create the simulation. The locating system monitors carriers for transporting components. The controller can compare parameters of the simulation results with corresponding parameters of earlier simulation results and/or actually obtained parameters of earlier production sequences, which can be stored in a model library. The disclosure further relates to corresponding production control methods.

Systems and methods for optimizing factory scheduling, layout or both which represent active factory elements (human and machine) as computational objects and simulate factory operation to optimize a solution. This enables the efficient assembly of customized products, accommodates variable demand, and mitigates unplanned events (floor blockages, machines/IMRs/workcell/workers downtime, variable quantity, location, and destination of supply parts).

The present application generally pertains to scaling of a production process to produce a chemical, pharmaceutical and/or biotechnological product and/or of a production state of a respective production equipment. Particularly, there is provided a computer-implemented method of scaling a production process to produce a chemical, pharmaceutical and/or biotechnological product, the scaling being from a source scale to a target scale, wherein the production process is defined by a plurality of steps specified by one or more process parameters controlling an execution of the production process, the method comprising: (a) retrieving: parameter evolution information that describes the time evolution of the process parameter(s); a plurality of recipe templates, wherein a recipe comprises the plurality of steps defining the production process, and wherein a recipe template is a recipe in which at least one of the process parameters specifying the plurality of steps is a parameter being variable and having no predetermined value at the outset; (b) receiving: a source setup specification of a source setup to be used for executing the production process at the source scale, the source setup specification comprising the source scale value: a target setup specification of a target setup to be used for executing the production process at the target scale, the target setup specification comprising the target scale value; a source recipe defining the production process at the source scale: at least one acceptability function defining conditions for the values of the process parameter(s) at the source scale and/or at the target scale; (c) simulating the execution of the production process at the source scale using the source setup specification, the source recipe and the parameter evolution information: (d) determining, from the simulation, one or more source trajectories for the process parameters), wherein a trajectory corresponds to a time-based profile of values recordable during the simulated execution of the production process; (e) performing a target determination step comprising: selecting a recipe template pertinent to the production process out of the plurality of recipe templates; providing an input value for the at least one variable parameter in the selected recipe template; simulating the execution of the production process at the target scale using the target setup specification, the selected recipe template, the input value for the at least one variable parameter and the parameter evolution information; determining, from the simulation, one or more target trajectories for the process parameters; comparing the source trajectory(ies) and the target trajector

A Multi-Purpose Dynamic Simulation and run-time Control platform includes a virtual process environment coupled to a physical process environment, where components/nodes of the virtual and physical process environments cooperate to dynamically perform run-time process control of an industrial process plant and/or simulations thereof. Virtual components may include virtual run-time nodes and/or simulated nodes. The MPDSC includes an I/O Switch which delivers I/O data between virtual and/or physical nodes, e.g., by using publish/subscribe mechanisms, thereby virtualizing physical I/O process data delivery. Nodes serviced by the I/O Switch may include respective component behavior modules that are unaware as to whether or not they are being utilized on a virtual or physical node. Simulations may be performed in real-time and even in conjunction with run-time operations of the plant, and/or simulations may be manipulated as desired (speed, values, administration, etc.). The platform simultaneously supports simulation and run-time operations and interactions/intersections therebetween.

A method and system relate to executing, by a processing device, a first simulation of operations of a semiconductor manufacture plant without imposing a Q-time constraint on a Q-zone, determining a kanban capacity value associated with the Q-zone based on results from the first simulation, executing a second simulation of operations of the semiconductor manufacture plant using the kanban capacity value under the Q-time constraint, determining whether results of the second simulation meet performance indices, and responsive to determining that the results of the second plant simulation meet the performance indices, providing the kanban capacity value to a manufacture execution system to operate the semiconductor manufacture plant using the kanban capacity value.

A production utilization planner (PUP) core for a manufacturing cell has a simulation manager configured to simulate the processing of workpieces arranged in a workpiece order, by performing the steps of: creating an instance of a simulation controller and an instance of a software model of the manufacturing cell, determining a next timed action to be performed by state machines, incrementing the simulation to the next timed action, updating the software model and the simulation controller each time a state machine performs a timed action, and repeating the steps of determining the next timed action, incrementing the simulation, and updating the software model and the simulation controller, until all of the workpieces have been processed. The simulation manager is configured to output a simulated completion time for processing the workpiece order.

A planning tool used to facilitate more efficient design of a central plant is configured to provide an advanced user interface. The user interface includes a symbol palette with selectable symbols that represent resource suppliers, subplants, energy loads, and resource storage devices associated with a central plant. The user interface allows users to drag these symbols onto a workspace and form various connections between the symbols. The user interface provides feedback to the user and prevents improper connections by evaluating user inputs according to a set of rules. The rules define valid relationships between the resource suppliers, subplants, energy loads, and resource storage devices. The user interface also allows users to specify equipment contained within the subplants. After a central plant model is created via the user interface, users can simulate operation of a central plant according to the model for planning, budgeting, and/or design considerations.