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 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.

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 method for dynamic intelligent scheduling includes following steps: collecting and recording resource constraints of multiple schedules on a production line and decision data of changes made to the schedules by a scheduler; cross-enumerating schedule combinations by using multiple production goals as penalty conditions; establishing a mathematical model based on the resource constraints and multi-objective weights corresponding to each schedule combination and importing the resource constraints to calculate schedule results; recording the penalty condition corresponding to the schedule combination matching the decision data as a valid penalty; using values of parameters corresponding to the valid penalty and values of the penalty conditions respectively as inputs and outputs to train a learning model; and responding to a scheduling request, finding a weight of each schedule combination by using the learning model according to the resource constraint of the current schedule and the production goals, and generating a recommended schedule accordingly.

The present disclosure provides a warehousing control system and a computer device. The warehousing control system includes: a communication module configured to transmit and receive information; and a warehousing server configured to: plan, upon receiving a container warehousing task via the communication module, a target warehousing area and a target warehousing space corresponding to each container in the container warehousing task, and assign a warehouse hoisting apparatus to hoist a target container carried by a transportation vehicle to a corresponding target warehousing space; and/or assign, upon receiving a container distribution task via the communication module, a warehouse hoisting apparatus to load a target container in the container distribution task onto an assigned transportation vehicle. In this way, intelligent unmanned warehousing can be achieved, such that the operation efficiency of warehousing can be improved and the cost of warehousing management can be reduced.

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 provides a flexible job-shop scheduling method based on a limited stable matching strategy, and belongs to the field of job-shop scheduling. The method adopts the following design solution: a. generating an initial chromosome population through integer coding and initializing relevant parameters; b. conducting crossover and mutation operations on parent chromosomes to obtain progeny chromosomes; c. combining the progeny chromosomes and the parent chromosomes into a set of to-be-selected chromosomes, and selecting a next generation of chromosomes from the set through limited stable matching operation; and d. stopping the algorithm if meeting cut-off conditions; otherwise turning to step b. The present invention introduces a limited stable matching strategy into the selection process of the progeny chromosomes to solve a multi-target flexible job-shop scheduling problem, so as to overcome the defects of insufficient population distribution and insufficient convergence in the existing method for solving the multi-target flexible job-shop scheduling problem when solving such problem, thereby obtaining excellent scheduling solution with good timeliness and high reliability.

A production planning system (6) for a raw materials industry plant (ANL), which determines the production planning data (Pi) thereof and specifies said data to the automation system (1) of the plant (ANL). A state monitoring system (7) determines previous and future anticipated states (Z1) of components of the plant (ANL). A quality determination system (8) determines states (Z2) of output products (Ai) produced and still to be produced by the plant (ANL) and/or past and future states (Z3) of the plant (ANL) as a whole. A maintenance planning system (9) and/or the production planning system (6) receive, from the state monitoring system (7), the states (Z1) of the components of the plant (ANL), determined by the state monitoring system (7) and, from the quality determination system (8), the states (Z2 and Z3) of the output products (Ai) and/or of the plant (ANL) as a whole, determined by the quality determination system (8). They consider the data received from the state monitoring system (7) and from the quality determination system (8) in the determination of maintenance planning data (W) and/or the production planning data (Pi).

A computer-readable recording medium storing a specifying program for causing a computer to perform processing including: obtaining first information indicating an order of processing a plurality of objects; obtaining second information indicating a type being processable by each work device; obtaining a result of a simulation regarding processing of the work devices; counting a number N1 of objects having moved to another work device although processing is capable of being executed on an object to be distributed next, and counting a number N2 of objects being caused to wait since processing is executed on another object although processing is capable of being executed on the object to be distributed next, under conditions such that each object waits when the work device executes processing on another object; and specifying a work device to be increased or decreased, according to any of the number N1 or the number N2.

A method includes identifying a set of wafers, wherein each wafer is associated with a respective start time of a set of start times, determining whether the set of wafers includes an idle wafer, in response to determining that the set of wafers includes an idle wafer that is idle for a duration that exceeds a predefined threshold value, generating a modified set of start times by modifying at least the start time for the idle wafer, and initiating a computer simulation forecasting processing of the set of wafers using a wafer modification chamber and a wafer movement chamber based on the modified set of start times. The computer simulation uses a machine learning model trained based on a first duration to perform a first manufacturing task using the wafer modification chamber and a second duration to perform a second manufacturing task using the wafer movement chamber.