The present invention discloses an adaptive-learning intelligent scheduling unified computing frame and system for industrial personalized customized production. Based on a deep neural network and reinforcement learning, a most suitable optimization algorithm is selected by automatic decision-making for a global customized production task with an industrial big data module at the bottom as an information basis, and a global optimal static scheduling plane is generated; a current dynamic event in a factory are monitored in real time; if no dynamic event requiring dynamic scheduling optimization is monitored, the global optimal static plan is executed sequentially; when a dynamic event impact requiring dynamic scheduling optimization is monitored, information of the current dynamic event is interpreted and classified, and corresponding optimization algorithms are automatically selected for dynamic scheduling optimization according to different types of dynamic events; and a dynamic scheduling scheme is evaluated by a subsequent module, an optimization scheme is regenerated or a most suitable optimization algorithm is automatically decided based on the scheme according to an evaluation result, and an equipment deployment sequence is generated for an automatic deployment. Considering the features of complicated procedures, a large amount of customization information and the high frequency of diversified dynamic events in personalized customized production, the present invention provides the adaptive-learning intelligent scheduling unified computing frame and system for industrial personalized customized production, that adopt two steps in the three aspects of static scheduling planning, dynamic scheduling planning and equipment deployment based on deep learning, that is, targeted optimization is performed after classification, thus improving the optimization efficiency and effect; and the system better fits the features of personalized customized production, and can effectively improve the efficiency of personalized customized production and minimize manual decision-making costs.

A method for sequence control of program modules, includes providing a control means and a description file having a configuration for controlling a program sequence. The method further includes providing a plurality of program modules which can be executed by a real-time operating system, the program modules being created using one or different programming languages. The method furthermore includes initiating selected program modules by executing specifications in the description file using the control means.

An information processing method includes acquiring contents of operations of devices and definition information defining temporal subordination of the operations in response to an input from a user, and generating a time chart of the operations based on the contents and the definition information.

Embodiments disclosed herein generally relate to methods, systems, and non-transitory computer readable medium for scheduling a substrate processing sequence in an integrated substrate processing system. A client device assigns a processing sequence to each substrate in a batch of substrates to be processed. The client device assigns a processing chamber to each process in the process sequence for each processing chamber in the integrate substrate processing system. The client device generates a processing model for the batch of substrates. The processing model defines a start time for each substrate in each processing chamber. The client device generates a timetable for the batch of semiconductor substrates based off the processing model. The client device processes the batch of substrates in accordance with the timetable.

The invention relates to production planning systems and methods. A production optimization system is provided which has the following: a production environment with at least one first machine, a second machine, a distribution unit configured to be connected to the first machine and the second machine via a communication network and to ascertain the machine state thereof, and an optimization unit configured to generate a production plan where at least one task for producing a product is separated into machine-specific sub-tasks for the first machine or the second machine, said sub-tasks being provided with a sequence for the first machine or the second machine. The distribution unit ascertains the next occurring sub-task for the first machine or the second machine from the optimization unit while taking into consideration the respective machine state, to remove the sub-task from the production plan, and to assign the sub-task to the respective machine.

Embodiments disclosed herein generally relate to methods, systems, and non-transitory computer readable medium for scheduling a substrate processing sequence in an integrated substrate processing system. A client device assigns a processing sequence to each substrate in a batch of substrates to be processed. The client device assigns a processing chamber to each process in the process sequence for each processing chamber in the integrate substrate processing system. The client device generates a processing model for the batch of substrates. The processing model defines a start time for each substrate in each processing chamber. The client device generates a timetable for the batch of semiconductor substrates based off the processing model. The client device processes the batch of substrates in accordance with the timetable.

In order to create, in real time, a production plan capable of responding to a sudden plan change in consideration of costs arising due to the production plan and a failure probability of a manufacturing process, a production plan management device includes: a communication unit configured to acquire product order information including a customer request for a first product and cost criterion information specifying a cost criterion for a budget of costs required to manufacture the first product; a storage unit configured to store at least a structural constraint database including structural constraint information specifying a structural constraint defining a condition for manufacturing processes to manufacture the first product in a factory; and a production plan generating unit configured to generate, in real time, a production plan that satisfies the customer request, the cost criterion, and the structural constraint and defines manufacturing processes for manufacturing the first product.

The example embodiments are directed to a system and method for analyzing different production events as a group by modeling the production events as intervals of time. In one example, the method includes modeling a plurality of production processes as a plurality of intervals of time which are overlapping one another on a graph over a predetermined period of time, each modeled production process including a sequence of events over time, determining a slice of time on the graph within the predetermined period of time based on event timings within the plurality of production process, identifying an event that is occurring in each production process during the slice of time based on the overlapping plurality of intervals of time, and storing the identified events of each production process together as a group along with an identification of the slice of time.

A method for sequence control of program modules, includes providing a control means and a description file having a configuration for controlling a program sequence. The method further includes providing a plurality of program modules which can be executed by a real-time operating system, the program modules being created using one or different programming languages. The method furthermore includes initiating selected program modules by executing specifications in the description file using the control means.

To make it possible to plan and provide a new production schedule reflecting characteristics or tendencies appearing in production schedules planned in the past. A schedule planning section calculates, on the basis of history information concerning production schedules of products planned in the past, a schedule pattern including production order of the products while considering constraint conditions in producing the products, rearranges the production order of the products according to the calculated schedule pattern, and creates a plurality of schedule candidates concerning a production schedule of the products. A schedule evaluating section evaluates the plurality of schedule candidates on the basis of evaluation indicators corresponding to the constraint conditions, and selects a best production schedule out of the plurality of schedule candidates.