52 results about "Order set" patented technology

The invention discloses a multi-objective optimization-based multi-commodity order fulfillment method and device, equipment and a medium, and relates to the technical field of supply chain management. The multi-commodity order fulfillment method comprises the following steps: acquiring order and inventory information including a current order set, a commodity set and a warehouse inventory set; and modeling an order fulfillment problem based on an actual demand of fulfillment of a multi-commodity order in the order and inventory information. The established model is a multi-objective optimization problem containing three objectives. Generating an initial population by adopting a heuristic method based on priority and inventory feasibility; and carrying out iterative optimization on the initial population by adopting a rapid non-dominated sorting genetic algorithm with elitism based on a double-layer elitism strategy to obtain a Pareto non-dominated solution set. And selecting a final order fulfillment scheme from the Pareto non-dominated solution set according to the preference of a decision maker. According to the method, three core operation indexes are synchronously improved under the condition of meeting feasibility constraints such as inventory, processing capability and demand.

The invention relates to the technical field of textile production scheduling, in particular to a textile production full-process intelligent scheduling system, which comprises a data acquisition module for setting a target production cycle and collecting basic information of all orders in the target production cycle to form an order set; the basic information of the order comprises an order type, an order quantity and order delivery time; and the working procedure determination module divides the production process of the textiles produced by the orders into a plurality of working procedure links based on the order types and the textile production process conditions, the working procedure links comprise key working procedures and auxiliary working procedures, and the auxiliary working procedures are auxiliary processing procedures of the key working procedures. According to the textile production full-process intelligent scheduling system provided by the invention, personalized prediction can be carried out on the actual production line productivity of each order in the target period, an extensive historical average value is abandoned, and the accuracy of productivity prediction is remarkably improved.

The invention discloses an intelligent production scheduling method for a copper coil pipe coiling and drawing process. The intelligent production scheduling method comprises the following steps: step 1, converting a monthly order into a daily production order; 2, translating order specifications to obtain an order set; and step 3, scheduling production by using an intelligent algorithm, carrying out frame material division, batch distribution and machine scheduling, and arranging dynamic shutdown to reduce the power cost. According to the intelligent production scheduling method for the copper coil pipe coil drawing process, frame material process similarity clustering and multi-target optimization scheduling are introduced, automatic production scheduling of the copper coil pipe coil drawing process is achieved on the basis of order data, process parameters and equipment information, and therefore the production efficiency of the copper coil pipe coil drawing process is improved while it is guaranteed that an order production task is completed. The working efficiency of the disc drawing equipment is improved, and energy conservation and cost reduction are achieved while production scheduling is optimized.

The invention provides an online car-hailing aggregation mode multi-stage order dispatching method and device and a storage medium. The method comprises the following steps: performing one-order-to-multi-driver matching calculation on each order record according to a first matching rule, selecting a target driver according to an order attribute and a driver attribute, and generating a first order sending result; comprehensively evaluating the matching condition of the driver with the multiple associated order records under the same travel demand, and when a preset adjustment condition is satisfied, adjusting the matching object of the driver from the order record in the first matching relationship to the target order record determined by evaluation, and generating a second order sending result; and comprehensively evaluating the matching condition of the driver and the candidate order set according to a scheduling strategy, and when a preset adjustment condition is satisfied, adjusting a matching object of the driver to a new target order record to obtain a final matching relationship. According to the method, the order sending response timeliness can be improved, the driver order matching quality is improved, and the multi-platform scheduling flexibility and the transport capacity utilization efficiency are enhanced.

The application provides a logistics transportation management method and system based on artificial intelligence, relates to the field of logistics transportation, and solves the technical problems that the prior art is difficult to balance real-time performance, distribution feasibility and resource utilization efficiency in the sudden interruption scene of a logistics vehicle, resulting in slow scheduling response, frequent secondary conflicts or failure of high-value order fulfillment. The method comprises the following steps: obtaining a set of orders to be delivered by a target vehicle at present and real-time position information; based on a preset hard constraint condition, screening candidate vehicles and constructing a task migration feasibility graph; inputting the task migration feasibility graph into a pre-trained lightweight graph neural network model to output the compatibility scores of each candidate vehicle for each order to be delivered in the order set; based on the compatibility scores, evaluating the urgency of the order to be delivered; and based on the urgency of the order to be delivered, updating the delivery task scheme of each candidate vehicle. The application is used in the process of logistics transportation.

The application provides a dynamic adjustment method for digital economic policy response, comprising: performing clearance backwriting lag evaluation on the time dislocation span, identifying orders exceeding the normal backwriting cycle to form an order composition backlog order set; determining the normal fluctuation range of trade volume without policy intervention based on the same period historical trade volume before policy implementation to construct a baseline interval; performing growth significance evaluation on the trade volume after stripping the delay interference by comparing the trade volume after stripping the delay interference with the baseline interval to identify a true growth signal; performing intersection operation on the time interval corresponding to the true growth signal and the concentrated backwriting period, if the intersection is not empty, then deducting the part occupied by the concentrated release amount in the concentrated backwriting period from the trade volume increment corresponding to the true growth signal to obtain a trade policy response evaluation result reflecting the actual incentive effect of the policy.

The invention discloses an order processing method and device, and relates to the technical field of computers. A specific embodiment of the method comprises the steps of determining a declaration priority of each warehouse based on received warehouse productivity data corresponding to a to-be-declarated order set; according to the declaration priority of each warehouse, setting an order declaration strategy of each warehouse; and performing declaration processing on the to-be-declarated orders in the to-be-declarated order set based on the order declaration strategy of each warehouse. According to the embodiment of the invention, the declaration priority of each warehouse is dynamically adjusted according to the warehouse productivity data, the declaration strategy is dynamically adjusted according to the declaration priority, order declaration is carried out based on the productivity gap, the order declaration efficiency is improved, warehouse order overstock is avoided, and the warehouse operation speed is improved.

The application discloses a denture manufacturing scheduling control method, device, equipment and medium, and relates to the field of production and manufacturing. The method comprises the following steps: acquiring a batch order set; acquiring a preset process node sequence of each denture manufacturing order and a priority score based on multi-dimensional characteristics based on order information of the batch order set to obtain an order priority set queue; the higher the priority score is, the higher the urgency of completing the corresponding denture manufacturing order is; establishing an MDCN decision tree based on process types and corresponding resources; and matching corresponding resources for each preset process node based on preset rule constraints and the MDCN decision tree to obtain a feasible scheme set, wherein the feasible scheme set comprises at least one feasible scheme, and each feasible scheme comprises resources matched by each preset process node and operation time of the resources; and performing optimization screening on the feasible scheme set to obtain a target scheme. The application can improve the synergy between resources and the comprehensive utilization rate of resources.

The invention provides a scheduling duration adjustment method and device based on a gain model, a product and a medium. The method comprises the steps of obtaining a to-be-distributed delivery order set with an association relationship; constructing an order relation graph of the order set; inputting the order relation graph into a gain model, so that the gain model extracts a graph embedding vector of the order relation graph based on a graph neural network, and predicts a gain value corresponding to each order under one or more predefined duration adjustment strategies for each order based on the graph embedding vector; the duration adjustment strategy comprises a strategy for performing duration adjustment on the initial scheduling duration; the gain value characterizes the probability gain that the order and other orders in the order set are distributed to the same delivery capacity relative to the non-execution of the order execution duration adjustment strategy; and determining a final adjustment strategy of the initial scheduling duration of the order according to the gain value of the order.

The invention provides an e-commerce order self-operation and crowdsourcing transport capacity integrated distribution method and electronic equipment. The method comprises the steps of obtaining a to-be-allocated order set, a commodity set, inventory data, transport capacity data and time window data of an e-commerce platform; constructing a cost optimization model of the to-be-allocated order set, wherein the cost optimization model comprises a front transport capacity cost parameter, a self-operation transport capacity cost parameter and a crowdsourcing transport capacity cost parameter; determining a decision variable and a target constraint condition of the cost optimization model; based on the to-be-allocated order set, the commodity set, the inventory data, the transport capacity data and the target constraint condition, solving a decision variable of the cost optimization model to obtain a target variable value of the decision variable; and outputting a target allocation strategy of the to-be-allocated order set based on the target variable value of the decision variable. According to the invention, the overall timeliness of the order set can be improved, the overall delivery cost is reduced, and the order timeliness default problem is reduced.

The embodiment of the invention provides an order processing method and device, electronic equipment and a storage medium, and belongs to the technical field of supply chains. The method comprises the steps of obtaining a target order set of a target supplier, and obtaining a to-be-processed order screening rule and an order sorting rule corresponding to the target supplier; performing order matching on the target order set according to the to-be-processed order screening rule to obtain a plurality of first to-be-processed orders matched with the to-be-processed order screening rule; according to each order sorting rule and the rule priority corresponding to the order sorting rule, multiple rounds of sorting are carried out on the multiple first to-be-processed orders, and order processing is carried out on a to-be-processed order queue obtained in each round in sequence; and after the multiple rounds of sorting operation are completed, if it is determined that a second to-be-processed order which is not matched with the to-be-processed order screening rule exists in the target order set, sorting the second to-be-processed order according to a preset order processing rule. According to the embodiment of the invention, the accuracy of processing abnormal orders can be improved.

The embodiment of the invention provides an order processing method and device, equipment and a storage medium. The method comprises the following steps: receiving at least one order to be processed; determining a transportation scheduling result of the at least one order, wherein the transportation scheduling result at least indicates a transportation timeliness requirement and / or a transportation tool corresponding to the at least one order; determining a target order set corresponding to the at least one order based on the order information corresponding to the at least one order, the transportation scheduling result and a predetermined screening rule; and based on each order in the target order set, a picking task for the target goods corresponding to each order in the target order set is generated, and the picking task indicates execution of a picking operation for the target goods. Therefore, the optimized order processing scheme is generated through intervention of an intelligent algorithm in combination with the transportation scheduling result, so that warehouse operation can be arranged more accurately, and the cooperation efficiency of the whole warehousing and logistics process is improved.

The application provides a kind of goods inventory allocation method and device, which comprises: obtaining the current remaining delivery time and a plurality of historical delivery times of goods in the order to be allocated;Fitting is carried out on the plurality of historical delivery times of goods, and the cumulative distribution function of the delivery time of goods is obtained;According to the current remaining delivery time and the cumulative distribution function of the delivery time, the delivery time parameter of the goods in the order to be allocated is determined, wherein the delivery time parameter decreases with the increase of the current remaining delivery time;According to the delivery time parameter of the goods in each order to be allocated, the order to be allocated is sorted, and the target order set after sorting is obtained;Inventory allocation is carried out on the target order set.The application can reduce the inventory turnover period.

The invention provides an order allocation method and device, electronic equipment, a readable storage medium and a program product, and relates to the technical field of computers and the field of intelligent logistics, and the method comprises the steps: dividing a to-be-processed order set into at least one commodity set; calculating the volume performance of the commodity set; in response to the fact that the volume performance of the commodity set meets a preset parameter range, the commodity set is split according to a preset numerical value condition, a first kind of commodity subset and a second kind of commodity subset meeting the preset numerical value condition are obtained, and the preset numerical value condition includes that the volume performance is equal to a preset volume value; and under the condition that the second type of commodity subset does not meet the preset numerical value condition, orders corresponding to the second type of commodity subset are allocated to a target batch order set, and the generation time of the target batch order set is later than the generation time of the to-be-processed order set.

The application relates to an order type determination method and device, computer equipment and a storage medium. It relates to the payment and finance field, and comprises the following steps: obtaining order characteristics of a target order based on the characteristic values of the target order in multiple preset order characteristic dimensions; determining an aggregation characteristic value of the target order based on the order characteristics of the target order and the order characteristics of historical orders in a historical order set; the aggregation characteristic value is used to represent the aggregation degree of the target order and the historical orders in the case of distribution according to the order characteristics; combining the characteristic values of the target order in at least one preset order characteristic dimension and the aggregation characteristic value to obtain a target combination feature; and predicting the order type to which the target order belongs by using the target combination feature. The method can improve the prediction accuracy of the order type.

The embodiment of the invention discloses an intelligent production scheduling method and related equipment, which are used for performing intelligent production scheduling under the condition of improving the production scheduling efficiency. The method comprises the steps that configuration information input in an interface is acquired, the configuration information comprises productivity configuration of a production line and constraint conditions of production scheduling, a large model is utilized to analyze order data of multiple target plan orders in a target batch order set according to preset cue words and the configuration information, and order data of multiple target plan orders in the target batch order set are obtained; obtaining initial production scheduling strategies corresponding to a plurality of target plan orders output by the large model, and analyzing order data of the plurality of target plan orders by using the small model based on the initial production scheduling strategies, the configuration information, the current residual capacity of the current production line and an optimization algorithm, and obtaining a target production scheduling strategy corresponding to the plurality of target plan orders output by the small model, thereby performing production scheduling on the plurality of target plan orders based on the target production scheduling strategy.

Embodiments of the present disclosure disclose a picking task execution method and device, electronic equipment and a computer readable medium. A specific embodiment of the method comprises: determining the number of orders of an order set for a target order item and inventory information of the target order item; determining item storage warehouse information corresponding to each order in the order set according to the number of orders and the inventory information, to obtain a set of item storage warehouse information; generating at least one order subset according to the set of item storage warehouse information and the order set; generating a set of picking task order information corresponding to each order subset in the at least one order subset, to obtain a plurality of sets of picking task order information; and controlling a picking device to execute a plurality of sets of picking tasks corresponding to the plurality of sets of picking task order information. The embodiment is related to intelligent logistics and can efficiently complete various picking tasks.

The invention relates to the technical field of packaged drinking water distribution, and discloses a packaged drinking water distribution path optimization scheduling system and method. A data acquisition unit of the system obtains a distribution area order set and path information, and paths are divided according to an order distribution sequence and a distance measurement result to obtain sub-road sections; the feature recognition unit recognizes real-time traffic data to obtain path features of each sub-road section, and marks the sub-road sections meeting optimization conditions; a parameter generation unit generates distribution optimization parameters in combination with the sub-road section scheduling information of the fixed nodes and the marked nodes and the road section intervals; the database construction unit associates and integrates the optimization parameters to construct a comprehensive scheduling database; and the optimization unit determines a basic scheduling program through the path control model according to the customer group information and the real-time traffic index curve, and outputs an optimized scheduling program after optimization of the path optimization model. The system can improve the distribution efficiency, improve the service quality and reduce the operation cost.

The invention provides an order grouping method and device, equipment and a medium, and relates to the technical field of warehouse logistics, and the method comprises the steps: adding orders into an order pool corresponding to a production scheduling identification type according to the production scheduling identification type of the received orders, carrying out the accumulation processing, and carrying out the accumulation processing of the orders in the accumulation processing process, and inputting order data of the order pool into the prediction model in real time, performing order formation processing according to an order formation strategy corresponding to the production scheduling identification type to obtain a real-time prediction set order corresponding to the order pool, and outputting a target set order of the order pool at an order interception moment at the order interception moment of accumulative processing. By pertinently selecting the order grouping strategy, integrating the order group in the order set and predicting the continuously increased order data in real time in a recursion mode, the data processing amount can be reduced, the prediction efficiency and the production efficiency can be improved, and the cost can be reduced.

The application is a kind of online car-hailing driver space behavior analysis method based on operation record data, comprising: collecting operation record data formed in the operation process of online car-hailing, normalizing order time and space position information, and constructing order set of drivers within a set time period; based on the order set, calculating the driver behavior index according to the preset behavior quantification rule; associating the behavior index with the preset space analysis unit, performing spatial mapping and aggregation processing on the driver operation behavior, and constructing the driver space behavior feature data set of the space unit level; based on the driver space behavior feature data set, differentiating the driver operation behavior in different space analysis units for analysis, and obtaining the analysis result. The application introduces a spatial difference description mechanism in the process of driver operation behavior analysis, realizes effective analysis of the operation behavior characteristics of online car-hailing drivers in urban space, and provides technical support for travel service management.

Embodiments of the present application provide a scheduling method, an electronic device and a storage medium. The method comprises: when there is a target work area in a plurality of work areas, and the number of available feeding ports of the target work area can meet the feeding demand of a to-be-processed order set, the to-be-processed order set is assigned to the target work area; when the number of available feeding ports of any one of the plurality of work areas cannot meet the feeding demand of the to-be-processed order set, a plurality of target sub-order sets are determined according to the picking parameters of each order in the to-be-processed order set and the number of available feeding ports of each of the plurality of work areas, each target sub-order set is assigned to a corresponding work area, and the number of available feeding ports of the work area corresponding to the target sub-order set meets the feeding demand of the target sub-order set.

The invention provides an order delivery method and system, electronic equipment and a computer readable storage medium, the method is applied to the field of computer control, and the method comprises the steps: carrying out the aggregation of to-be-delivered orders corresponding to order articles stored in a connection cabinet according to a preset condition, and obtaining a to-be-delivered order set; dispatching a delivery carrier based on the to-be-delivered order set so as to enable the dispatched delivery carrier to move to a preset docking position of the connection cabinet and to be docked with the connection cabinet; under the condition that butt joint of the distribution carrier and the connection cabinet is completed, an article conveying instruction is sent to the connection cabinet so that the connection cabinet can convey target order articles to a storage bin of the distribution carrier, and the target order articles are order articles corresponding to a plurality of to-be-distributed orders in the to-be-distributed order set; and sending a delivery instruction to the delivery carrier to enable the delivery carrier to deliver the target order article according to the delivery instruction under the condition that the target order article transmission is completed. The method can improve the order delivery efficiency and improve the user experience.

The invention discloses a logistics information visual management system and method based on big data, and relates to the technical field of logistics information management. According to the logistics information visual management method based on big data, logistics operation information is obtained, and a visual scheduling management interface is constructed and comprises an order visualization layer and a transport capacity visualization layer; based on a preset business rule, a target scheduling order set is identified in an order visualization layer, the target scheduling order set comprises a plurality of target scheduling orders, and a preliminary screening transport capacity set of each target scheduling order is extracted in a transport capacity visualization layer; based on a multi-target optimization algorithm, matching optimization processing is carried out on the preliminary screening transport capacity set of each target scheduling order, and a scheduling matching set corresponding to the target scheduling order set is obtained.According to the method, the transport capacity recommendation scheme is generated through the scheduling matching set and is displayed on the visual scheduling management interface, so that the transport capacity empty driving rate can be effectively reduced, and the scheduling efficiency is improved. Order delay caused by mismatching of transport capacity is reduced, and scheduling decision-making efficiency is improved.

The invention discloses an online customer service intelligent order transfer distribution method and system for a BOSS system, and relates to the technical field of customer service, and the key points of the technical scheme comprise the following steps: obtaining all consultation orders of online customer service in the BOSS system, carrying out statistics on all consultation orders according to order numbers of different business types and historical consultation data of clients to obtain a business statistics result set; screening out orders with abnormal business types and quantity from the business statistical result set, marking the orders as target orders, screening out orders associated with the target orders from all consultation orders according to the target orders, and marking the orders as a pre-processing associated order set; performing difference statistics on a first statistical result and a second statistical result in the business statistical result set to obtain first association difference data, and extracting a first target association order from the pre-processing association order set according to the first association difference data; the method has the effect that the accuracy of order distribution is improved to a great extent.

The invention provides a non-final-state order processing method and device, and relates to the technical field of data processing. The method comprises the steps that a non-final-state order set with the order number equal to the error processing number is acquired, all non-final-state orders in the non-final-state order set are distributed to a downstream server one by one, and order distribution time corresponding to all the non-final-state orders is recorded; receiving a processing result of each non-final-state order fed back by the downstream server according to an order state query result of the downstream system, and updating order processing times; and according to the order distribution time, the order processing frequency, the order business type corresponding to each non-final-state order and the average response time of the payment channel responding to the corresponding non-final-state order within the adjacent preset duration before the current moment, the order priority is updated. The device executes the method. According to the method and the device provided by the embodiment of the invention, the processing efficiency of non-final-state order processing can be improved.

The present application belongs to the field of management science and engineering, and specifically relates to an APS scheduling method under a mixed production mode of MTO and MTS. The logical framework of the present application is composed of an order unfolding module, a work order management module and a work order dispatching module, wherein the order unfolding module and the work order dispatching module are the core of the system. In the order unfolding module, the system unfolds into different levels of work order sets in different ways according to whether the order belongs to a planned product order or a market product order, and refers to inventory information and MBOM information. In the work order dispatching module, the work order is dispatched to each device by using an intelligent method according to the dispatching rules, optimization targets and device capacity constraints, so as to complete the scheduling plan. The present application is based on an operational research model and an intelligent algorithm, and is a high-efficiency scheduling method for enterprises with both planned product production and market product production, which has important theoretical significance and wide application value for improving the efficiency of the enterprise production planning system and the flexibility of the manufacturing process.

The application discloses a multi-commodity order fulfillment method and device based on multi-objective optimization, equipment and medium, and relates to the technical field of supply chain management. The multi-commodity order fulfillment method comprises the following steps: acquiring order and inventory information, including a current order set, a commodity set and a warehouse inventory set. Based on the actual demand of multi-commodity order fulfillment in the order and inventory information, an order fulfillment problem is modeled. The established model is a multi-objective optimization problem containing three objectives. A heuristic method based on priority and inventory feasibility is used to generate an initial population. A fast non-dominated sorting genetic algorithm with elitist reservation based on a double-layer elitist reservation strategy is used to iteratively optimize the initial population to obtain a Pareto non-dominated solution set. According to the decision maker's preference, a final order fulfillment scheme is selected from the Pareto non-dominated solution set. The method synchronously improves three core operating indicators under the satisfaction of inventory, processing capacity and demand feasibility constraints.

The invention provides an order allocation method and device. The method comprises the following steps: setting constraint conditions including a same-region order-to-order distance constraint, a cross-region order-to-order distance constraint and a maximum percentage constraint of on-road order combination distance increase; performing same-region clustering on orders in the to-be-allocated order set according to geographic information and constraint conditions to form a preliminary allocation result; when the distance between the individual batch order and the cross-regional order accords with the distance constraint between the cross-regional orders, combining the individual batch order with the cross-regional order closest to the individual batch order; when the total driving distance of the individual batch orders and the on-the-way orders accords with the maximum percentage constraint of on-the-way order combining distance increase, combining the individual batch orders with the on-the-way orders; and according to the timeout rate, the average delivery time of each batch and the total batch number, an objective function is set, the preliminary distribution result is optimized, the number of riders is reduced, the delivery time of the riders is shortened, and the user satisfaction is improved.

This invention discloses a method, system, device, and non-volatile storage medium for cold chain vehicle scheduling. The method includes: collecting status data corresponding to multiple orders and the current status of multiple cold chain vehicles in a target area; determining the order distribution density in the target area based on the order locations of the orders; determining clustering parameters based on the order distribution density; performing density clustering on the multiple orders based on the clustering parameters to obtain multiple order clusters; dividing the multiple order clusters into multiple task clusters based on the timeliness requirements and temperature zone types of the orders; and determining a target scheduling scheme based on the multiple task clusters and the operating status of the multiple cold chain vehicles. This invention solves the technical problems of current cold chain vehicle cluster energy consumption optimization, which is mostly limited to local optimization at the single vehicle or single station level, lacking global scheduling capabilities, and traditional optimization algorithms having slow convergence and being prone to getting trapped in local optima.