Example implementations described herein are directed to a system for manufacturing dispatching using reinforcement learning and transfer learning. The systems and methods described herein can be deployed in factories for manufacturing dispatching for reducing job-due related costs. In particular, example implementations described herein can be used to reduce massive data collection and reduce model training time, which can eventually improve dispatching efficiency and reduce factory cost.

Provided is a job plan creation system capable of creating an efficient job plan when the job plan is reconfigured on the basis of the progress of the jobs after the completion of the jobs that day and by the job starting time of the following day, for example. A job plan creation system extracts an interrupted job that has been started but has not been completed, a delayed job that should have been started based on the job plan but has not been started, and a not-started job that is scheduled to be started on the job plan and so has not been started from the job plan, and creates the job plan while setting priorities in the order of the interrupted job, the delayed job and the not-started job.

A method for operating a system for a multiple dynamic job sequence includes providing a packet of information in order to process different production runs. An additional packet of information is used to inquire about respective states during the manufacturing process so as to enable interventions to be made based on the jobs being not processed as originally. An additional packet of information is received, from which information regarding physical detection of a printing and processing situation is taken such that each downstream processing machine is able to identify the job and a job end. By identifying the job and the job end, an additional packet of information is forwarded to the plant controller, the plant controller detecting an actual state of the manufacturing process therefrom such that new or adapted job sequences and job ends are directly defined during the manufacturing process.

A work management device manages a board work line having multiple board work machines for performing work on a board. The work management device includes a problem detection section configured to detect that a problem has occurred in any of the multiple board work machines; a handling method database configured to accumulate handling methods for problems; an updating section configured to update handling methods for problems at any time; and a work instruction section configured to extract and indicate to an operator a handling method for a problem from the handling method database when the problem detection section detects that the problem has occurred.

According to an embodiment, an information processing device includes a memory and one or more processors coupled to the memory. The one or more processors configured to: receive rule data and system data, the rule data representing rules of monitoring control and including first state information among a plurality of pieces of state information by which states of monitoring control targets are expressed in a relation between two concepts, the system data including second state information among the plurality of pieces of state information and indicating a state of a first target included in a monitoring control system; determine a condition of collation between the pieces of state information; collate, in accordance with the condition, the first state information included in the rule data and the second state information included in the system data; and output a result of the collation to an output device.

A semiconductor fabrication scheduling method includes creating a load scheduling data schema including facility data of product lots to be dispatched to a plurality of workstations; generating a load schedule profile using a load-balancing model and based on the load scheduling data schema, wherein the load-balancing model includes one or more objective functions and there is at least one weight factor in an objective function; generating a current load schedule based on the load schedule profile; dispatching the product lots to the plurality of workstations using the current load schedule to complete fabrication of the product lots; obtaining a set of current key performance indicators (KPIs) of the completed fabrication of the product lots; and automatically adjusting the weight factors of the objective functions of the load-balancing model based on the current KPIs using a big-data architecture to generate a next load schedule for next cycle of fabrication.

A display method includes storing, in a storage unit, first history information in which a processing is executed by a substrate processing apparatus based on a recipe; storing, in the storage unit, second history information in which an execution instruction for a job is received to instruct execution of the processing; and calculating, based on the first history information and the second history information, a first idle time indicating a time during which the processing is not performed and a second idle time indicating a time from an end of the job to a start of execution of a next job, and displaying the first idle time and the second idle time.

A semiconductor fabrication scheduling method includes creating a load scheduling data schema including facility data of product lots to be dispatched to a plurality of workstations; generating a load schedule profile using a load-balancing model and based on the load scheduling data schema, wherein the load-balancing model includes one or more objective functions and there is at least one weight factor in an objective function; generating a current load schedule based on the load schedule profile; dispatching the product lots to the plurality of workstations using the current load schedule to complete fabrication of the product lots; obtaining a set of current key performance indicators (KPIs) of the completed fabrication of the product lots; and automatically adjusting the weight factors of the objective functions of the load-balancing model based on the current KPIs using a big-data architecture to generate a next load schedule for next cycle of fabrication.

A method and system to help control body-shop processing of vehicles, based on timing of interaction with a touch-screen display. In an example implementation, a body shop will be equipped with a computing system including a touch-screen display, with the computing system being configured to manage presentation on the display of graphical representations of job-cards for individual body-shop jobs, such as individual vehicles in for repair. With such an arrangement, body shop personnel could drag and drop job cards from one section to another to indicate transitions of jobs between body-shop processing steps. The computing system will then advantageously make use of data regarding the timing of those drag-and-drop operations as a basis to control body-shop processing, such as be predicting a processing duration of a job currently in process and taking action to modify processing of the job based on the predicted duration for instance.

A manufacturing-job apparatus includes a control module, a holding module, and a job module. The holding module is configured to hold a job object. The job module is configured to execute a job for the job object. The control module includes a communication unit and a common interface. The communication unit is configured to communicate with each of the holding module and the job module. The common interface is configured to supply motive power to both of the holding module and the job module.