71 results about "Order scheduling" patented technology

The invention relates to the technical field of intelligent manufacturing and production optimization scheduling, and particularly discloses a thermal fuse flexible production-oriented order scheduling resource scheduling optimization method, which comprises the following steps of: generating a structured vector by extracting inherent and dynamic characteristics of an order, and constructing a production scheduling dynamic environment model by fusing a device resource state time sequence; the order comprehensive process switching cost and the resource constraint tightness are calculated, order clusters are divided through multi-target constraint clustering, and a pre-arrangement sequence is generated; key resource conflict early warning is completed through time sequence prediction; constructing a multi-target scheduling model by taking the order cluster as a unit, and generating a look-ahead window scheme by combining a rolling optimization solidification plan baseline; and finally, on the basis of a real-time execution data updating model, through dynamic priority preemption and local rescheduling adjustment when abnormity occurs, the problems of high cost of small-batch mixed arrangement, conflict identification lagging, easy damage of a thermosensitive process steady state and poor compliance constraint adaptation are solved, and collaborative optimization of the scheduling efficiency, the production yield and the compliance is realized.

The invention discloses a supply chain logistics flexible scheduling system based on multi-source data fusion, relates to the technical field of logistics scheduling, and solves the technical problems of extensive order scheduling and path optimization and insufficient dynamic response capability. Comprehensive integration of order, transport capacity, inventory and external environment data is realized, an order emergency degree classification standard is quantified, an improved algorithm and a K-means clustering + genetic algorithm are combined, and an optimal basic scheduling path considering timeliness and cost is generated; meanwhile, collaborative optimization of inventory and transport capacity is achieved based on resource characteristics such as the inventory turnover rate and the transport capacity load rate, the daily average delivery order amount of a single vehicle is increased, a double dynamic adjustment mechanism of timed refreshing and event triggering is adopted, refreshing intervals are set according to different transport scenes in a differentiated mode, and the efficiency is improved. The method can quickly respond to abnormal conditions such as sudden congestion and address deviation, can automatically adjust the path, and can reduce the delay rate caused by congestion.

The invention discloses a hierarchical priority task orderly scheduling method based on a virtual thread, which comprises the following steps: S10, receiving task parameters transmitted by a business layer, performing task packaging setting, outputting packaged task objects, and transmitting the packaged task objects to a multi-dimensional calibrator; s20, performing task expiration verification, task fusing verification and dependency verification in the multi-dimensional verifier, returning a passing or failing identifier and a failing reason, and if the passing or failing identifier does not pass, triggering a monitoring alarm to record a discard log; s30, the verified tasks enter a two-dimensional queue manager to be subjected to hierarchical enqueue processing and capacity dynamic adjustment, and the capacity dynamic adjustment uses a dynamic resource allocator to achieve elastic allocation of queue capacity and virtual threads based on load awareness; and S40, performing task execution and task retry management on the tasks in the two-dimensional queue.

The invention discloses a self-optimizing intelligent work order scheduling method and system, belongs to the technical field of work order scheduling, and solves the problems that a traditional scheduling method is poor in dynamic adaptability due to static rules, single in optimization target and dependent on artificial experience, and a model cannot be self-optimized online. According to the technical scheme, the method comprises the steps that work order features and resource state data are collected in real time and input into a pre-trained deep reinforcement learning model to generate a scheduling scheme, a scheduling deviation value is calculated based on actual feedback data to trigger model iterative optimization, and dynamic updating of a scheduling strategy is achieved; the method is mainly used for work order automatic distribution and scheduling optimization in scenes such as a grid command center, a logistics distribution platform and a customer service center.

The invention discloses an intelligent logistics order scheduling method and system, and the method comprises the steps: obtaining a parameter set, the parameter set comprises solid state parameters and dynamic parameters, the solid state parameters are inherent attribute parameters of each order in a to-be-scheduled order queue, and the dynamic parameters are parameters determined according to the dynamic changes of the order queue and an external environment; obtaining a plurality of weight coefficients, wherein the weight coefficient is an importance degree value preset for each parameter; according to the parameter set and the weight coefficient of each parameter, calculating a scheduling priority weight value corresponding to each order through a preset weighting algorithm; ordering the orders in the order queue to be scheduled according to the calculated scheduling priority weight value; and allocating appropriate deliverymen to the orders according to the sequenced order and generating a scheduling instruction. According to the invention, the problems of single logistics order scheduling strategy and difficulty in adapting to real-time dynamic change scenes in the prior art are solved. The distribution efficiency and the resource utilization rate can be improved.

The invention provides a take-out order scheduling method and system, a storage medium and a program product, and relates to the field of personnel or project management, and the method comprises the steps: generating a path network according to the coordinates of a pickup point and a delivery point of a new order; determining a group of geographic grid indexes covering the path network based on a preset grid cell size to obtain a new order path index set; calculating an intersection index set of the new order path index set and the future path index total set of the candidate rider; and when the number of the unique identifiers of the geographic grid indexes in the intersection index set exceeds a preset path matching threshold value, adding the new order into the order sequence of the target candidate rider with the highest comprehensive score, and adjusting the planned path corresponding to the target candidate rider to complete the scheduling of the new order. By implementing the method, the problem of misjudgment of the smoothness caused by path simplification can be relieved, and the matching accuracy of the order and the rider is improved, so that the overall delivery efficiency is improved.

The invention discloses a printing order intelligent production scheduling optimization method and system based on an equipment operation and maintenance state, and relates to the technical field of the manufacturing industry, and the method comprises the steps: obtaining a multi-source operation and maintenance parameter set of equipment and work order attribute information during printing, and recognizing a same-type work order interval; on the basis of the same type of work order intervals, dividing loss levels to generate an adaptation result which is used for providing an adaptation basis for equipment and work order distribution; on the basis of the adaptation result and the work order attribute information, the interference influence of work order switching on equipment production is identified by analyzing the correlation strength of the same process work order and the equipment part loss, and disturbance strength data is obtained; identifying a to-be-distributed node through the disturbance intensity data, and obtaining a feasibility result through a redistribution operation of work order switching; and constructing a production scheduling network according to the feasibility result, and obtaining a distribution optimization result after path traversal and path re-planning so as to realize dynamic matching of production scheduling and equipment operation and maintenance states.

The invention provides a medical supply chain multi-level collaborative intelligent order scheduling and tracking method and system. The method comprises the following steps: acquiring node information, inventory batch information and to-be-scheduled order information of a supply chain; the method comprises the following steps: constructing an inter-node material scheduling tendency potential energy matrix, calculating demand potential energy by using an S-type gain function according to the order demand quantity of a medical institution, calculating a node state disturbance value in combination with node historical operation interruption frequency and storage environment compliance data, and calculating the timeliness value of each batch through an exponential decay function according to the stock batch material validity period; synthesizing the three to generate a potential energy matrix; generating order candidate scheduling paths based on the potential energy matrix gradient, accumulating path node state disturbance values to obtain path comprehensive disturbance values, and selecting the path with the minimum value as a scheduling path; scheduling is executed according to the path, the logistics information is tracked in real time, and when the logistics information deviates, the historical operation interruption frequency of related nodes is updated, the node state disturbance value is adjusted, and subsequent order scheduling is optimized.

The application provides a medical supply chain multi-level collaborative intelligent order scheduling and tracking method and system, acquires supply chain node information, inventory batch information and to-be-scheduled order information; constructs a material scheduling tendency potential energy matrix between nodes, calculates demand potential energy by using an S-shaped gain function according to the quantity of medical institution order demand, calculates node state disturbance values in combination with historical operation interruption frequency and storage environment compliance data of the nodes, calculates the time value of each batch by an exponential decay function according to the shelf life of the inventory batch, and generates a potential energy matrix by comprehensively integrating the three; generates an order candidate scheduling path based on the gradient of the potential energy matrix, accumulates the path comprehensive disturbance value of the path node state disturbance value, and selects the minimum value as the scheduling path; performs scheduling according to the path, tracks logistics information in real time, updates the historical operation interruption frequency of the related nodes when the logistics information deviates, adjusts the node state disturbance value, and optimizes subsequent order scheduling.

This invention relates to the field of intelligent manufacturing and industrial automation technology, specifically to an intelligent order scheduling and material management system for a milling and turning workshop. It includes a task parsing module, a feature mapping module, a state acquisition module, a potential field construction module, a scheduling calculation module, and a feedback execution module. The system receives task drawing data and bill of materials data, extracts geometric feature bounding dimension data and machining tool vector sequence data, and tensors and encodes them. It collects the current tool library configuration status parameters of the target machining equipment and the physical attribute parameters of work-in-process, constructs a continuous potential energy field distribution matrix, and dynamically refreshes it. It calculates the potential energy gradient descent direction of the multidimensional machining feature tensor flow, generates a task queue of associated paths and a theoretically estimated execution cycle time sequence, issues CNC machining task plans and automated guided vehicle (AGV) allocation instructions, and feeds back the actual cycle deviation correction value for closed-loop updates. This invention makes the scheduling results closer to the actual executable state of the workshop.

The invention relates to a logistics order allocation method and device, computer equipment and a storage medium. The method comprises the following steps: in response to an order type of a to-be-created logistics order being a new order type and receiving user voice input in a voice input box in a logistics order creation interface, performing voice recognition processing on the user voice to obtain mailing information of the to-be-created logistics order; in response to a received mailing product photo uploaded in a photo uploading box in the logistics order creation interface, carrying out photo analysis processing on the mailing product photo to obtain mailing cargo product information of the to-be-created logistics order; and generating a logistics order creation request according to the mailing information and the mailing product information, and sending the logistics order creation request to an order scheduling server, so that the order scheduling server creates and distributes a logistics order according to the logistics order creation request. By adopting the method, the user order reporting efficiency can be improved.

The invention discloses an intelligent management system for a packaging production line, and relates to the technical field of production line packaging, and the system comprises a product identification module which is configured to read a unique identifier of a product through a two-dimensional code scanner and / or an RFID reader; the parameter storage and mapping module is arranged on the edge equipment, and is configured to store a mapping relation between the product and the corresponding packaging parameter, and query and generate an equipment control instruction set according to the ProductID; the edge controller module comprises an industrial IPC and PLC interface board card and is used for receiving the equipment control instruction set and issuing the equipment control instruction set to packaging execution equipment through an industrial communication protocol; the packaging execution equipment can automatically adjust the mechanical position and the technological parameters according to the received instruction; the rapid type changing buffer module comprises double buffer queues and is configured to pre-load parameters of a next order during the operation period of the current order and realize non-stop parameter changing in an order changing empty window period; and the order scheduling optimization module is configured to optimize an order execution sequence based on the remodeling cost matrix.

The invention discloses a work order production scheduling method and device, and a storage medium. The method comprises the steps of obtaining a genetic algorithm, parameters and order information for optimizing a k-means clustering algorithm; initializing a k-means clustering algorithm according to the order information, and optimizing the genetic algorithm according to a preset production scheduling scene; based on the parameters and a genetic algorithm, optimizing the initialized k-means clustering algorithm to obtain a k-means clustering algorithm optimized based on the genetic algorithm; and calculating according to the k-means clustering algorithm to obtain an optimal production scheduling scheme, and performing work order production scheduling according to the optimal production scheduling scheme. According to the method, the k-means clustering algorithm is improved through the genetic algorithm, and the work order scheduling flow is optimized in combination with the improved algorithm, so that the accuracy of the optimization result is improved.

The invention discloses an intelligent work order scheduling method and system based on multi-factor cost prediction, and belongs to the technical field of work order management. According to the method, a multi-factor dynamic cost model comprehensively considering in-transit time, skill matching degree, work order priority and service time limit SLA risk is constructed by acquiring work order and personnel states in real time, and a cost index is calculated for each potential scheduling scheme. According to the system, a variable neighborhood search VNS optimization algorithm is adopted, and global solution is carried out on the basis of a cost matrix so as to find an optimal task allocation scheme with the lowest total cost. According to the method, the weight can be dynamically adjusted and optimized according to operation states such as real-time traffic and personnel load, self-adaptive intelligent decision making is realized, and finally, an optimization scheme is automatically distributed to a personnel terminal, so that the work order scheduling efficiency, the resource utilization rate and the SLA fulfillment rate are comprehensively improved.

The invention provides a dynamic scheduling management method and system for cod crisp biscuit processing orders, and relates to the technical field of order scheduling management, and the method comprises the steps: obtaining basic scheduling data; identifying procedure switching waiting time, and performing structured decomposition on the procedure switching waiting time to generate variable structure segment data; constructing a target process demand chain according to order feature information of the target order, and performing association processing on the target process demand chain and the variable structure segment data; embedding the variable structure segment data into a plurality of candidate positions of the current process link structure, and executing structure replacement calculation on each candidate position; a target structure replacement scheme meeting a preset condition is screened, and connection offset calculation is executed according to the plan starting time of the target structure replacement scheme and a subsequent batch and process time sequence constraint information; when the connection offset is within a preset offset threshold, generating an update process link structure, and expanding the update process link structure into an update scheduling time axis; according to the invention, the autonomy and accuracy of dynamic scheduling management are improved.

This invention relates to the field of intelligent work order scheduling platform design technology, and discloses an intelligent work order scheduling system, method, device, and medium. The method includes: the system integrates multiple functional modules to achieve efficient and intelligent scheduling of equipment failure work orders. First, the work order access module receives equipment failure information from different channels to ensure comprehensive and timely information. Next, the profile building module uses historical service data of maintenance personnel to build a multi-dimensional capability profile, providing a data foundation for intelligent matching. Then, the intelligent allocation module accurately matches the profile data and work order parameters, associating the most suitable maintenance personnel with the work order. The order grabbing interaction module generates a list of work orders to be grabbed and an order grabbing interface, enhancing scheduling flexibility and improving scheduling efficiency. The execution tracking module monitors the work order processing progress in real time, ensuring transparent and controllable scheduling. Finally, the knowledge base question-and-answer module provides professional knowledge support to maintenance personnel, helping them quickly resolve work order issues.

The application discloses an order production scheduling and real-time early warning management system, comprising an order scheduling module, a process tracking module, a progress early warning module and a resource board module, wherein the order scheduling module is configured to determine scheduling information according to order product information; the process tracking module is configured to track the current state of the product by associating the ERP, MES and PLM systems; the progress early warning module is configured to conduct multi-level comprehensive risk early warning according to the current date, the delivery date and the current state; and the resource board module is configured to summarize order product production information and conduct statistical analysis. The application also discloses an order production scheduling and real-time early warning management method. The application can deeply integrate specific business knowledge, realize full closed-loop management from intelligent scheduling to real-time early warning, fill the digital gap between the planning and execution layers, and improve the overall operation resilience, delivery reliability and customer satisfaction.

The application discloses a kind of order scheduling information determination method, device and equipment and storage medium, belong to computer technical field.The method specifically includes: obtaining environmental parameter information in industrial production and first weight matrix, the environmental parameter information includes order urgency, equipment idle rate and energy price fluctuation rate;Based on the environmental parameter information, using fuzzy logic controller, obtain weight adjustment amount;Based on the weight adjustment amount, the first weight matrix is updated to obtain second weight matrix;Based on the second weight matrix and the preset multi-objective value, using particle swarm optimization algorithm, determine optimized order scheduling information.

The invention relates to the technical field of equipment asset management, in particular to a mechanism and data fusion driven intelligent work order scheduling method and system, and the method comprises the steps: constructing a mechanism model; training an AI prediction model by using historical data; acquiring a current work order pool, real-time states of all personnel and environment information; according to the constructed constraint condition set, preliminarily screening available personnel maintained in the personnel dynamic ability map to obtain a candidate personnel set; calling a trained work order complexity prediction model and a personnel performance prediction model, and predicting the predicted processing time of each work order and the expected result of processing each work order by each candidate personnel in combination with the analyzed work order attributes and the candidate personnel set; and inputting the current work order pool, the candidate person set, the predicted processing time, the expected result and the multi-objective optimization function into an AI scheduling engine, and outputting an optimal work order-person allocation scheme. The work order response time is shortened, and the resource utilization rate is improved.

This application provides a method and apparatus for optimizing ride-hailing order scheduling based on adaptive fairness constraints using reinforcement learning, belonging to the field of ride-hailing order scheduling technology. The method includes: acquiring pressure index data of the ride-hailing market at the current time step, fairness evaluation index data corresponding to the current time step and multiple historical time steps; determining historical statistical features based on the fairness evaluation index data corresponding to multiple historical time steps; determining a target fairness threshold based on the pressure index data and historical statistical features; determining the degree of constraint violation of the target fairness threshold based on the fairness evaluation index data corresponding to the current time step, and using Lagrange constraint reinforcement learning to determine the parameters of a neural network model used for ride-hailing order scheduling, and determining the updated neural network model based on the parameters of the neural network model; and scheduling ride-hailing orders based on the updated neural network model. This application can optimize ride-hailing order scheduling.

The application belongs to the technical field of steel production and artificial intelligence, and relates to a construction method of a hot rolling scheduling hierarchical reinforcement learning model, comprising the following steps: obtaining a single product order data set according to hot rolling production daily plan order data, and constructing a hot rolling production scheduling sample data set based on the data set; presetting a daily rolling total number, constructing a hot rolling scheduling hierarchical reinforcement learning model corresponding to all rolling processes, training the model by using a training sample data set, and generating an overall rolling order scheduling sequence; and verifying the trained model by using a verification sample order data set to obtain a final hot rolling scheduling hierarchical reinforcement learning model; the application improves model decision efficiency and training stability, effectively adapts to order structure changes and process parameter updates, ensures that the generated scheduling scheme meets process feasibility and production stability requirements, and significantly reduces the calculation cost caused by invalid action exploration.

The present application relates to the field of supply chain scheduling and digital twin technology, and specifically provides a supply chain order scheduling method, system, device and storage medium based on digital twin, the method comprising: acquiring the basic attributes and topological association relationship of each scheduling node of the supply chain link, and constructing a digital twin model; configuring the data synchronization frequency according to the scene, collecting the real-time running state data of the node and dynamically updating the model; analyzing the order information to obtain the order priority, constraint condition and optimization target weight; generating a candidate scheduling scheme that meets the constraints based on the twin model, simulating execution to obtain scheduling evaluation indexes, and selecting the optimal scheme through multi-objective optimization algorithm sorting; collecting the execution state of the optimal scheme, adjusting the algorithm weight and iteratively optimizing the digital twin model, so as to improve the efficiency and emergency adaptation capability of the supply chain order scheduling, and realize the adaptive operation and continuous iterative optimization of the whole process of the supply chain order scheduling.

The invention discloses an intelligent enterprise work order management system, which comprises a work order information acquisition module, relates to the technical field of enterprise data processing, and is characterized in that historical work order processing data is introduced, a feature association model is constructed, and a work order association score is generated; the priority sequence of the standard work orders in the standard work order set is obtained in combination with the work order remaining time and the resource occupation amount, work order priority evaluation is made to better fit the actual production rule through historical experience, subjective experience deviation is avoided, the reasonability of scheduling decision making is improved, and the work order delay rate is reduced in combination with priority ranking of the work order remaining time and the resource occupation amount; the weight group is randomly generated, the fitness is evaluated through the work order scheduling simulation model, and a new group is generated based on the to-be-selected weight group to screen the optimal weight, so that the weight group of the work order relevance score, the work order remaining time and the resource occupation amount better fits the actual production scene.

This specification discloses an order scheduling method, apparatus, storage medium, and electronic device. Based on the fit between any order to be assigned and any delivery capacity, an initial matching scheme is determined, consisting of the matching relationships established between the order to be assigned and the delivery capacity. Instead of directly assigning orders based on the matching relationships in the initial matching scheme, at least some of the matching relationships are adjusted based on the initial matching scheme to obtain an adjusted optimized matching scheme. Then, a target matching scheme is selected from the initial and optimized matching schemes, and orders are assigned based on the matching relationships in the target matching scheme. The order scheduling method proposed in this specification ensures that the optimized matching scheme after adjusting the matching relationships is better than the initial matching scheme under a second comprehensive fit metric, and that the selected target matching scheme is better than the other matching scheme under a target fit metric.

The invention discloses a man-machine collaborative scheduling method and system based on an improved genetic algorithm, and the method comprises the steps: initializing an order scheduling environment, processing the imported order data, machine data and material data, and obtaining an initial order scheduling scheme; carrying out iterative optimization processing on the initial order scheduling scheme by adopting an improved genetic algorithm, circularly executing fitness calculation, selection, crossover and mutation operations when an iteration threshold value is not reached to obtain an updated scheduling strategy, and outputting a machine decision scheduling scheme when the iteration threshold value is reached; submitting the machine decision scheduling scheme to an artificial decision interface for determination processing, and receiving an artificial determination instruction generated based on analysis of the machine decision scheduling scheme; and processing the machine decision scheduling scheme according to the manual judgment instruction, and selecting to re-execute iterative optimization of the improved genetic algorithm or output the machine decision scheduling scheme as a final scheduling scheme. According to the invention, efficient optimization of order scheduling and improvement of decision accuracy can be realized.

The invention relates to the technical field related to work order management, in particular to a work order circulation management method and system for automobile maintenance, and the method comprises the steps: introducing workshop real-time operation data and work order execution scene parameters, and generating a conflict work order scheduling instruction set; and triggering a work order circulation linkage scheduling mechanism according to the abnormal type and priority level of work order circulation, and performing cooperative execution management and control on a plurality of edge computing nodes and each work order execution link according to the consistency deviation of the system process. The technical problems of exception identification lagging, scheduling response stiffness and work order execution overall efficiency limitation caused by lack of dynamic perception of the whole process of the maintenance task and frequent resource contention are solved, dynamic scheduling path planning of the maintenance task flow is realized, process conflicts are effectively avoided, exception types and priorities are identified, and the efficiency of work order execution is improved. Cooperative management and control are carried out on the plurality of edge nodes and execution links according to the process consistency deviation, and the technical effects of work order circulation robustness and closed-loop processing capability are enhanced.

The application discloses a kind of based on multi-version management's work order production scheduling method, device and storage medium, by creating and formal production scheduling environment isolated storage draft version, make planner free to simulate production scheduling under the premise of not affecting actual production, avoid the covering of formal plan by misoperation, realize the parallel storage and independent adjustment of multiple schemes;By generating operation instruction object containing pre-adjustment state and post-adjustment state, and based on withdrawal or recovery instruction, respectively according to pre-adjustment state or post-adjustment state update gantt chart display and back or recovery process state, reversible adjustment of single operation is realized, the consistency of interface display and back-end process state is ensured;By publishing satisfactory draft version as formal production scheduling plan, the smooth transition from simulation scheme to production execution is realized.

The application relates to the technical field of data processing methods, in particular to a service order dynamic allocation method and system based on multidimensional data analysis. The method comprises the following steps: determining the emergency degree of a current temporary order according to the user information of a target user and the temporary order information of the current temporary order; determining an insertable group according to the arrangement order information of each arranged order in a service group; combining the temporary order information and the arrangement order information to determine the possible delay degree of the current temporary order after being inserted into the insertable group and the possible interference degree of the current temporary order to other orders in the insertable group; determining the total order completion easy degree of the insertable group according to the arrangement order information; and combining the possible delay degree, the possible interference degree and the total order completion easy degree to determine the target insertion group of the current temporary order. Through the technical scheme, global order scheduling optimization is realized, and the stability and efficiency of the overall task flow are maintained.

This specification provides a smart gas work order scheduling optimization method and an Internet of Things (IoT) system. The method is implemented by the smart gas work order scheduling optimization IoT system, which includes a smart gas user platform, a smart gas service platform, and a smart gas management platform that interact sequentially. The method is executed by the smart gas management platform. In this method, the smart gas service sub-platform can generate work orders to be assigned based on gas processing requests received from the gas user sub-platform. A gas processing request refers to a user's request to handle gas-related issues. Gas-related issues may include fault reporting, service complaints, gas equipment function inquiries, and new gas product inquiries.

This application provides an order scheduling method and a computer storage medium. The method includes receiving travel order information, obtaining service provider information and historical travel service information of at least two candidate service providers corresponding to the travel order information; determining expected revenue based on the travel order information, service provider information, and historical travel service information; and allocating orders to at least two candidate service providers based on the expected revenue to obtain an order scheduling result. Expected revenue represents the overall revenue, which includes not only the short-term revenue corresponding to the travel order but also the probability of candidate service providers offering travel services in the future, reflecting medium-term and long-term revenue. From the perspective of overall revenue, this increases the number of service providers offering travel services in the future predetermined time period, improving the travel experience.