Disclosed herein is technology for performing a simulation based optimization to identify a schedule for a manufacturing tool. An example method may include determining, by a processing device, resources of a manufacturing tool, wherein the resources comprise a first chamber and a second chamber; accessing task data indicating a first manufacturing task and a second manufacturing task; determining a duration to perform the first manufacturing task using the first chamber and a duration to perform the second manufacturing task using the second chamber; updating a machine learning model based on the duration to perform the first manufacturing task and the duration to perform the second manufacturing task; performing a set of computer simulations that uses the machine learning model and the task data to produce a set of simulation results; storing, by the processing device, a simulation result of the set of simulation results in a data store.

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.

Disclosed are a method and a system for transporting order. The method for transporting order includes: marking an ordering priority level of the working machine previously; identifying a priority ordering machine when several working machines issue orders at the same time; determining whether a transporting characteristic information of the priority ordering machine meets the preset condition for triggering a priority order; and if yes, priority ordering the priority ordering machine.

Disclosed is an item transport system. The item transport system includes: a control server, at least one carrying robot, at least one storage container and at least one picking container. The control server obtains order information and container information of items to be transported and integrates the order information with the container information to provide transport information for the at least one carrying robot. The storage container and the picking container are both configured to accommodate items to be stored and items to be picked interchangeably. The carrying robot carries the storage container or the picking container based on the transport information received from the control server. Further disclosed is an item transport method.

An operation management apparatus predicts the machining processes of one or more facilities. This operation management apparatus acquires a machining program operating on the facilities and including an execution time for each process, generates schedule data to which related information (an identifier of the process) is added, and generates, for each of the facilities, a graph (a process schedule) including a time axis and indicating a progress of the process, based on the generated schedule data.

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.

An industrial programming method and apparatus, a device, a storage medium, and a program product are provided. In a programming scheme generation method, a programming device obtains a first group of constraints for a plurality of parameters in an industrial programming job. Further, the programming device constructs, based on the first group of constraints, a cut constraint associated with at least one integer parameter in the plurality of parameters, and constructs a second group of constraints based on the cut constraint and the first group of constraints, where a type of the at least one integer parameter in the second group of constraints is changed. The programming device determines values of the plurality of parameters based on the second group of constraints, to generate a programming scheme for the industrial programming job.

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 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.