44 results about "Manufacturing scheduling" patented technology

A method and system for visually displaying real-time enterprise management information is provided. An application integration platform receives plural types of data from manufacturing and information systems within an enterprise and analyzes the plural types of data to determine key performance indicators, such as a number of tardy jobs. A process control server receives manufacturing data from at least one work center and forwards the manufacturing data to the application integration platform. A user interface that interfaces with the application integration platform provides a visual display of the number of tardy jobs in accordance with the class of user interacting therewith. The user may then reschedule the tardy jobs on alternate machines and determine how such rescheduling will affect the expected completion times of all jobs running on all machines.

An apparatus and method for a production planning system using a process of adaptive learning is disclosed. A production planning system receives production objects wherein each production object has at least one attribute. In one embodiment, the production objects include information relating to product to be manufactured. After receipt of production objects, multiple preference scores according to the attributes of the production objects are calculated. In one embodiment, each preference score represents desirability of transition from manufacturing a first object to a second object. The system subsequently identifies a suggested production plan which includes a sequence order of two or more objects in response to the preference scores. In one embodiment, the planning preferences are updated or modified by an adaptive learning system in response to adjustments of the suggested production plant by a planner.

The present invention provides an orthodontic process phased into two or more sub-processes for repositioning a patient's teeth. In each sub-process, a complete treatment plan is established, but not all (i.e. only some) appliances in the plan are fabricated to move the teeth, out of the expectation that teeth movement may not completely follow the full course as planned. The entire orthodontic process is thus featured with dynamic planning and incremental implementation of the plans, to address ever-changing treatment profile.

The invention provides a multi-species multi-process multi-unit manufacturing scheduling method based on an improved cellular machine. The method includes: 1) building a cellular automaton model of a workshop scheduling system: a) each workpiece is processed according to a certain process sequence, and the workpiece enters a next station to be processed only after a front process is completed; (b) each machine can only process one workpiece at a time period simultaneously, and a next workpiece can be processed only after processing of one workpiece is completed; and (c) the processing of the workpiece on the machine cannot be interrupted until the process is completed; and 2) building a multi-objective function: the condition is that: 2.1) maximum completion time of all processes of the workpiece is shortest; 2.2) maximum delay of the workpiece is smallest; and 2.3) balance ratio of each workpiece device is high. A scheduling general objective function of each discrete unit in each time step of a cellular automaton is obtained through a weighting method. The multi-species multi-process multi-unit manufacturing scheduling method achieves dynamic monitoring and is good in robustness.

A dental restoration method that eliminates time-consuming, labor-intensive steps conventionally needed to fabricate molds, and thereby facilitates dental restorations in a shorter time, while reducing the discomfort and embarrassment sometimes associated with provisional restorations involves scanning an existing dental structure to generate a three-dimensional digital model of the existing dental structure, generating a three-dimensional digital model corresponding with a planned dental structure, and using an additive manufacturing process and apparatus (e.g., a 3-D printer) to fabricate the planned dental structure directly on the existing dental structure.

The illustrative embodiments provide a computer implemented method, apparatus, and computer usable program code for processing orders. A request for delivery by a requested delivery date is received for an order having an original delivery date. The ordered product is broken down into one or more materials required for manufacturing the ordered product. For at least one material required for manufacturing the ordered product, a date when an engineering change becomes effective for the material is specified. An alternate bill of materials is created based on the specified date when the engineering change becomes effective for the material. An alternate manufacturing plan based on the alternate bill of materials is created. Manufacturing is initiated based on the alternate manufacturing plan. If manufacturing can be completed in time for delivery by the requested delivery date, the original delivery date of the order is replaced with the requested delivery date.

A method for forecasting a demand for rotable parts includes collecting demand data for one or more rotable parts associated with a product inventory. A demand pattern associated with the demand data is identified for each of the one or more rotable parts. A future demand associated with the one or more rotable parts is forecasted for at least one future demand period based on the identified demand pattern. An inventory level associated with each of the one or more rotable parts is established, for the at least one future demand period, a based on the future demand and a predetermined customer service level. The method also includes adjusting a manufacturing schedule associated with the one or more rotable parts based on the established inventory level.

Tools and/or resources which newly become available to a re-entrant flow manufacturing line, data processing pipeline or the like are allocated in substantially real time to processing operations independent of and consistent with a scheduling segment previously generated by dispatching items such as workpieces or data to tools and/or resources of a manufacturing line until the newly available tool or resource can be conveniently included in a subsequently generated schedule segment in order to avoid productivity loss due to schedule latency. Items can be dispatched in a priority order to accelerate completion of processing. A schedule segment including the newly available tool or resource can be facilitated by merging allocations of a real time dispatch list with allocations of a dispatch list corresponding to a previously generated schedule segment.

A manufacturing management device includes first cell planning device connected to the manufacturing machine of first manufacturing cell, and second cell planning device connected to the manufacturing machine of second manufacturing cell. The planning part of the first cell planning device calculates quantity of operation that would be left unfinished with respect to the quantity stipulated in a manufacturing plan. The first cell planning device transmits a request for transfer including information on the quantity of operation that would be left unfinished to the second cell planning device. The second cell planning device modifies at least one of the operation period and the period of maintenance work of the manufacturing machine in the second manufacturing cell so as to increase the quantity of operation during the predetermined period.

The invention discloses a production and manufacturing scheduling optimization method based on an improved genetic algorithm. The method comprises the following steps: firstly, aiming at a production scheduling optimization target, establishing a mathematical model, determining a population fitness function, reading order information and equipment information, numbering equipment, encoding an order process into a chromosome gene, initializing a population, the maximum number of iterations and a production scheduling matrix; carrying out operations such as crossover and variation on chromosomes of the population to obtain a new generation of population, arranging order procedures corresponding to chromosome genes on equipment in a conflict-free manner by combining a production scheduling matrix, and calculating a fitness function of population individuals; and finally, selecting next-generation individuals according to a binary tournament selection strategy, reserving fitness individuals to the next generation, repeating the steps until the maximum iteration frequency is reached, and decoding the individual chromosome with the highest fitness as an optimal production scheduling scheme. The global optimization can be quickly realized, and the effect is better than that of the existing optimization method.

The invention provides a flexible manufacturing scheduling system and a manufacturing system comprising the flexible manufacturing scheduling system. The flexible manufacturing scheduling system is incommunication connection with at least one controller, and each controller is used for controlling at least one actuator. The flexible manufacturing scheduling system may include a processor. The processor may be configured to receive order information, and control one or more of the at least one controller based on a pre-programmed scheduling logic corresponding to the order information to makethe controller control an action of the at least one actuator to fulfill the order task, wherein the scheduling logic is an instruction set required to fulfill the order task and recognizable and executable by the controller.

The invention discloses a product energy-saving scheduling optimization method for a flexible manufacturing system, which belongs to the technical field of production scheduling. The implementation method comprises the steps: workshop product structures and machining and assembling resources are obtained, and a product structure set and a production resource set are constructed; a process set, a process time set and an energy consumption set are constructed; aiming at the assembly process, the process with the maximum start time of each assembly process is selected as an actual completion process to form an assembly constraint, and the assembly constraint is fused into the flexible production scheduling model to construct a flexible manufacturing scheduling model; and the flexible manufacturing scheduling model is optimized through an improved multi-target particle swarm-genetic algorithm. The optimization speed can be guaranteed, the algorithm convergence can be improved, a flexible manufacturing system scheduling scheme with a better target function is obtained, the scheduling scheme can integrally consider the production process and the assembly process of a product and is moresuitable for existing production and assembly working conditions, and therefore the production time of the product is shortened, and energy consumption in the production process of the product is reduced.

In a physical distribution model of the circulation type, synchronization is established between a reuse plan and a delivery/fabrication plan to thereby reasonably reduce the excess and deficiency of inventory articles at a confluence point of regenerated reusable articles and newly delivered component parts. With regard to newly delivered lower-level items, predict a variation or fluctuation of stock quantity based on at least a delivery schedule of them and a present stock amount along with usage plans in manufacturing processes. As for lower-level items of regenerated articles, predict a delivery due date and an expected number of such regenerated lower-level items based on at least a stock quantity of recovered items collected for reuse purposes and a lead time for taking regenerated lower-level items out of the collected items, thereby controlling, based on the both prediction results, the stock quantity of the lower-level items in manufacturing processes.

The invention relates to a cloud manufacturing scheduling method based on a Grover quantum search algorithm. The cloud manufacturing scheduling method includes the steps: initializing quantum registerstates, setting a cost consumption function and a tensor product; taking a state register as an initial value; and taking M state registers from the N states by using an improved quantum Grover search algorithm, and carrying out minimum search, wherein if the cost consumption function of any new state register is smaller than the initial cost consumption function, the current state register is ascheduling optimal solution, and the scheduling state matrix corresponding to the current state register is taken as an optimal scheduling matrix, otherwise, the optimal scheduling matrix is taken asan initial scheduling state matrix, and an optimal scheduling matrix is output. According to the cloud manufacturing scheduling method, the task allocation planning speed in the cloud manufacturing scheduling problem is increased, and the quantum Grover algorithm is used for conducting hierarchical screening firstly, and most of non-optimal scheduling solutions can be filtered out to change the situation that a plurality of solutions need to be traversed originally into the situation that only a few solutions need to be traversed, and therefore the original problem is simplified, and the search speed is increased.

The invention discloses a cellular machine collaborative scheduling method for a large equipment manufacturing and driving system. The method mainly comprises the following steps: (1) constructing a grid model of a production scheduling cellular machine, describing a cellular state, setting initial conditions and boundary conditions of a production scheduling cellular machine model, and setting an evolution rule; (2) optimizing an evolution rule of the production scheduling cellular machine model; (3) constructing a grid model of the driving scheduling cellular machine according to the actual driving operation condition, describing the cellular state, setting the boundary condition of the driving scheduling cellular machine model, and setting an evolution rule; (4) optimizing the driving scheduling evolution rule by means of a genetic algorithm; (5) designing a large equipment manufacturing production and driving collaborative scheduling simulation system interface. According to the production scheduling and driving scheduling collaborative modeling method based on the cellular machine, a new method and thought are provided for large equipment manufacturing scheduling.

The invention discloses a semiconductor manufacturing scheduling method and system and a computer readable storage medium. The method comprises the following steps: determining a mathematical optimization model according to a data scale; generating a first scheduling sequence according to the mathematical optimization model, and performing iterative calculation on the first scheduling sequence through the mathematical optimization model under a constraint condition to obtain a second scheduling sequence; determining an optimal strategy based on an operation result of each training action, determining a target total strategy according to the optimal strategy corresponding to each state parameter, and performing simulation scheduling according to the target total strategy to obtain a third scheduling sequence; and performing scheduling operation according to the third scheduling sequence. According to the method, rapid target optimization of semiconductor manufacturing scheduling is realized, the production capacity is effectively and reasonably distributed, the resource utilization rate is improved, and the production cost is reduced.