Embodiments of the present disclosure provide a material scheduling method and a material scheduling device for semiconductor processing equipment. The method includes establishing a material list, establishing a first scheduling task list according to process recipes and the material list, and inputting the first scheduling task list into a solver to calculate and output a scheduling result with shortest time for performing all material scheduling tasks in the first scheduling task list and parsing the scheduling result to obtain a movement sequence of all materials. In the technical solutions of the material scheduling method and the material scheduling device for the semiconductor processing equipment of embodiments of the present disclosure, the overall scheduling result can be improved, and the calculation speed can be improved. Thus, the scheduling result can be obtained in real-time.

A tree search-based scheduling method and an electronic apparatus are provided. In the method, multiple order lists are received and a schedule is initialized, wherein each order list includes multiple production operations. In each order list, a first production operation which has not been joined into the schedule yet is selected, such that multiple prior operations are selected. An execution priority of the prior operations is calculated according to multiple dispatching rules, and multiple candidate operations are selected from the prior operations according to the execution priority. Afterwards, the candidate operations are listed as a next operation of the schedule respectively, and a scheduling simulation is performed according to the dispatching rules to obtain multiple scheduling indicators of the candidate operations. Scheduling is performed according to the scheduling indicators.

The present invention disclose a parallel machine batch scheduling method and system based on an improved artificial bee colony algorithm in a deterioration situation. With this method, a near-optimal solution for the parallel machine batch scheduling problem with deteriorating jobs and maintenance consideration can be obtained. The model of the present invention is derived from an actual production process with considerations of machine maintenance and batching as well as additional processing and maintenance time for jobs and machines over time in actual production. According to the present invention, the settlement of this problem is conducive to providing reliable decision support for the production and maintenance of an enterprise in complex real production conditions, thus reducing enterprise operation costs, increasing enterprise productivity, and promoting building of a modern smart factory of the enterprise.

It is possible to update a process plan, a work plan, and a production plan in a short time with respect to a production fluctuation event and flexibly deal with the production fluctuation. A production planning device includes: a process plan generation unit which generates a plurality of process plans including an alternative process plan, the process plans relating to a production process of a product; a work plan generation unit which generates a work plan corresponding to each of the process plans; a production plan generation unit which generates a production plan using the plurality of process plans and the work plan; and a production plan redrafting unit which redrafts a production plan using the plurality of process plans and the work plan when a production fluctuation event occurs.

An AAMP system includes a plurality of AAMP system stations disposed in an environment and configured to perform one or more AAMP processing routines, and a plurality of robots configured to autonomously travel within the environment, where one or more robots from among the plurality of robots include an auxiliary AAMP processing station configured to perform one or more auxiliary AAMP processing routines. The AAMP system includes a controller configured to select an AAMP system station from among the plurality of AAMP system stations to perform the one or more AAMP processing routines based on AAMP system operation data and select a robot from among the plurality of robots to initiate the one or more AAMP processing routines at the selected AAMP system station based on a digital model of the environment and robot operation data, where the robot operation data includes an auxiliary processing state.

A production control system includes a plurality of processing machines configured to successively process a plurality of workpieces conveyed on a production line, and a system server connected to the plurality of processing machines, in which the system server acquires an operating status associated with a time from each of the plurality of processing machines, associates the acquired operating statuses with the plurality of workpieces successively arriving at the processing machines, and thereby identifies each of the plurality of workpieces conveyed on the production line.

A material management apparatus (management computer) includes a production plan acquirer that acquires production plan information including a type of a material (cream solder) for bonding a component to a substrate, the material being used for production of a mounting substrate obtained by mounting the component on the substrate; a material status acquirer that acquires material status information on the material preserved in a material preservatory which preserves an accommodating portion (solder pot) in which the material is accommodated; and a material preparation instructor that creates and transmits an instruction to prepare the accommodating portion to be put out from the material preservatory, based on the production plan information and the material status information.

Provided is a smart factory for production and quality management of a thermoplastic and thermosetting compound capable of predicting and controlling quality and production schedule in the future as well as monitoring a current state of factory by acquiring various types of data generated during a manufacturing process such as a manufacturing schedule, a quality condition, or the like of a manufacturing process.

A calculation amount and calculation time for a substrate conveyance schedule are reduced. A scheduler is provided which is incorporated in a control section of a substrate processing apparatus including a plurality of substrate processing sections that process a substrate, a conveyance section that conveys the substrate, and the control section that controls the conveyance section and the substrate processing sections, and calculates a substrate conveyance schedule. The scheduler includes: a modeling section that models processing conditions, processing time and constraints of the substrate processing apparatus into nodes and edges using a graph network theory, prepares a graph network, and calculates a longest route length to each node; and a calculation section that calculates the substrate conveyance schedule based on the longest route length.

A method for adjusting a process controller in order to manufacture one or a plurality of components in production is disclosed. The method includes (i) obtaining input data characterizing one or a plurality of components, (ii) determining clusters in the input data, (iii) grouping the components into the determined cluster, and (iv) determining a parameterization of a specified process step for the one or plurality of components or subsequent components by way of a R2R controller, depending on the measurement results and the grouping.