A computer-implemented method of scheduling jobs for an industrial process includes receiving jobs to be executed on machines within a manufacturing facility. A job schedule is generated based on an optimization function that minimizes total energy cost for all the machines during a time horizon based on a summation of energy cost at each time step between a start time and an end time. The energy cost at each time step is a summation of (a) a first energy cost associated with each machine in sleeping mode during the time step, (b) a second energy cost associated with each machine in stand-by mode during the time step, and (c) a third energy cost associated with each machine in processing mode during the time step. The jobs are executed on the machines based on the job schedule.

A process decision support device includes: a machining performance data storage unit that stores machining performance data indicating machining performance of each of a plurality of machining devices including a machining device that adds material and a machining device that removes material; a process pattern generation unit that refers to the machining performance data and sequences and allocates machining steps performed by a combination of any of the plurality of machining devices to generate a process pattern as a combination of the machining steps with which a product is manufacturable; and an output unit that outputs content of the process pattern generated by the process pattern generation unit.

A method for allocating resources to machines of a production facility comprises: receiving forecast data indicating planned demands for the resources and a demand deviation for each resource; generating a new demand for each resource from the planned demand and the demand deviation of the resource in several iterations; assigning capacities to the new demands in each iteration by determining resource-machine combinations, wherein a priority is determined for each resource and each machine based on a set of priority rules, wherein the resource-machine combinations are determined by combining the resources and the machines according to the priorities; and generating a roll-out plan assigning resource-machine combinations to be rolled out to future time periods, wherein the roll-out plan is generated from the resource-machine combinations of different iterations and an estimated roll-out time for each resource-machine combination such that a total number of the resource-machine combinations to be rolled-out is minimized.

An embodiment method for operating by a controller a cell based mobility production system which produces various kinds of mobilities with a plurality of cells connected in series or in parallel includes arranging a necessary task for each cell in consideration of a mobility production target and a deviation of utilization rate between the cells, setting a sequence of the cells through which a vehicle body matched for a type of mobility passes in consideration of the task arranged for each cell, and determining a feeding sequence of each type of vehicle body in consideration of the sequence of the cells through which each type of vehicle body passes.

To provide a numerical control method and a processing device capable of generating a plurality of machine-specific machining programs, from one machining program to a single workpiece. A numerical control method is to be executed by a computer. The numerical control method includes a generating step of dividing, into a plurality of paths, a machining path of an original NC program for a machine configured to machine a single workpiece, corresponding to contents of machining to the workpiece in the original NC program, and of generating, on the basis of the respective divided machining paths, individual NC programs for machines corresponding to the respective machining paths.

Systems, methods, devices, and other techniques for planning and re-planning robot motions. The techniques can include obtaining a schedule that defines an execution order for a plurality of plans, each plan defining a sequence of tasks; providing instructions to the robotic system to execute plans from the plurality of plans according to the schedule; obtaining measurements for one or more parameters that represent a state of the robotic system or its environment that results from execution of at least one plan from the plurality of plans; determining, based on the measurements, that a particular plan from the plurality of plans, which has not completed execution, is to be revised; generating, using the measurements, a revised version of the particular plan; and adjusting the schedule so as to resolve a conflict between the revised version of the particular plan and at least one other plan in the plurality of plans.

A process decision support device includes: a machining performance data storage unit that stores machining performance data indicating machining performance of each of a plurality of machining devices including a machining device that adds material and a machining device that removes material; a process pattern generation unit that refers to the machining performance data and sequences and allocates machining steps performed by a combination of any of the plurality of machining devices to generate a process pattern as a combination of the machining steps with which a product is manufacturable; and an output unit that outputs content of the process pattern generated by the process pattern generation unit.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for optimizing a plan for one or more robots using a process definition graph. One of the methods includes receiving a process definition graph for a robot, the process definition graph having a plurality of action nodes. One or more of the action nodes are motion nodes that represent a motion to be taken by the robot from a respective start location to an end location. It is determined that a motion node satisfies one or more splitting criteria, and in response to determining that the motion node satisfies the one or more splitting criteria, the process definition graph is modified. Modifying the process definition graph includes splitting the motion node into two or more separate motion nodes whose respective paths can be scheduled independently.

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.

A computer-implemented method of scheduling jobs for an industrial process includes receiving jobs to be executed on machines within a manufacturing facility. A job schedule is generated based on an optimization function that minimizes total energy cost for all the machines during a time horizon based on a summation of energy cost at each time step between a start time and an end time. The energy cost at each time step is a summation of (a) a first energy cost associated with each machine in sleeping mode during the time step, (b) a second energy cost associated with each machine in stand-by mode during the time step, and (c) a third energy cost associated with each machine in processing mode during the time step. The jobs are executed on the machines based on the job schedule.