22 results about "Delivery timing" patented technology

The invention provides a production scheduling scheme generation method, system and device based on a digital twinning model, and relates to the technical field of digital twinning, and the scheme comprises the steps: determining a process route according to order information; the process route comprises a plurality of procedures and a flow sequence among the procedures; the order information at least comprises delivery time; according to the order information, the process route and preset parameters, performing station matching calculation on each process in the process route to obtain a station matching number; inputting the distribution number and the order information into a pre-constructed digital twinborn model, and performing simulation calculation by the digital twinborn model to obtain a simulation result; and determining a production scheduling scheme according to the simulation result and the order information. According to the scheme, through the digital twinning technology, the production demand is accurately predicted, the production scheduling scheme is optimized, the production process is monitored in real time, the digital twinning model is constructed, intelligent and flexible production management is achieved, efficiency, delivery accuracy and emergency handling capacity are effectively improved, and industry intelligent transformation is assisted.

A computer-implemented system (100) for real-time detection of supply chain disruptions and forecasting of the financial impact in a computer-driven ERP environment, wherein the system (100) comprises: a data acquisition interface (1) configured to continuously capture structured and unstructured operational data from one or more ERP modules, supply chain execution systems, and enterprise event streams; a data management and harmonization engine (2) configured to validate, normalize, deduplicate and semantically map the captured data into a unified, time-aligned canonical enterprise schema; a disturbance signal fusion and anomaly detection engine (3) configured to fuse signals from multiple sources and detect disturbance events using one or more machine learning models to output a disturbance value, event class and a list of affected nodes; a supply chain dependency graph and a digital twin builder (4) configured to create and update a dynamic dependency graph representing suppliers, facilities, transportation routes, SKUs, orders, contracts and lead times, and propagate disruption effects across the graph; a financial impact forecasting engine (5) configured to estimate the real-time and forward-looking effects of the disruption on one or more financial measures, including sales, margin, working capital, cash flow, service level penalties and inventory holding costs, and furthermore the effects on the cost of goods sold (COGS) and / or the landed costs using a Kl-based forecast with a range of uncertainty; a module for coordinating corrective actions and automating workflows (6) configured to generate ranked corrective actions and initiate ERP workflow steps, including reordering, reassignment, alternative sources of supply, production rescheduling and logistics diversions, wherein the ranked corrective actions include recommendations to resolve the disruption by using available stock, reassigning low-priority shipments or diverting shipments that can be delivered later within a lead time window; an explainability, audit trail and compliance logging module (7) configured to record model inputs, feature assignments, decision justifications, scenario assumptions and action results as an immutable audit trail; and a visualization, alerting and collaboration interface (8) configured to output real-time alerts, dashboards and scenario comparisons to authorized users and downstream systems via APIs and role-based access controls, wherein the interface (8) generates a quick report view that displays early disruption signals and impact on manufacturing costs, delivers notifications to mobile devices, triggers a special urgent notification if the disruption affects a high-priority trading partner, and accepts user responses on mobile devices that automatically and without delay trigger corrective actions or updates in the ERP system.

An object of a system according to an embodiment is to propose an optimal delivery timing based on a use frequency of a user.SOLUTION: A system includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects use frequency data of a user. The analysis unit analyzes the data collected by the collection unit and calculates a delivery timing. The proposing section proposes a delivery timing to the user based on the analysis result obtained by the analyzing section.SELECTED DRAWING: Figure 1

To provide a cushioning material that can be attached to irregularly shaped structural materials, has a short delivery time, reduces the number of parts, and is easy to disassemble and reuse. [Solution] The cushioning material 1 consists of four cushion blocks 10 connected in series along the center line c with a connecting portion 14 in between. A fixing hole shape 20 extends from the leading cushion block 10, and a fixing string shape 30 extends from the trailing cushion block 10. Each cushion block 10 has a rectangular base portion 12, a pillow portion 11 provided on the base portion 12, a circular projection portion 13 protruding from the lower surface 12a of the base portion 12, and umbrella portions 17, 18, and 19 that protrude from the outer circumferential surface of the projection portion 13 at intervals.

An automated product handling system and method for efficiently storing, retrieving and delivering bagged items includes a frame assembly housing a multi-tiered gantry system with vertical storage shelves, and a delivery box mounted for vertical movement adjacent to the storage shelves. A telescoping delivery drive system moves the delivery box vertically to align with selected storage shelves. The delivery box includes a horizontally extensible conveyor system and automated bag handling mechanisms comprising vacuum cups to secure bag bottoms and vertically moveable hooks to lift bag sides around products. A control unit coordinates system operations including receiving customer orders through a mobile application, optimizing storage shelf selection based on delivery timing and customer proximity, controlling product bagging and storage, and managing customer retrieval operations. The system enables fully autonomous operation with dynamic storage assignment, multi-shelf order coordination, and automated fault handling. Methods for operating the system include receiving orders, selecting optimal storage locations, automated bagging and shelf transfer operations, and controlled customer retrieval processes.

To provide a metal pipe column which can improve durability of a taper metal pipe provided at an embedment part without causing a higher price and a long delivery period, and which has excellent economical efficiency, and also to provide the metal pipe column which can further improve durability by preventing or suppressing potential corrosion, by considering a natural potential (difference) of metal related to the embedment part, and which has excellent quality.SOLUTION: A metal pipe column 1 is constituted by joining a plurality of taper metal pipes 10. The taper metal pipe 10 has a constitution in which the diameter is expanded downward in a taper-shape, and by the taper metal pipe 10 on the lower stage being inserted in the taper metal pipe 10 on the upper stage, an inner surface of the taper metal pipe 10 on the upper stage and an outer surface of the taper metal pipe 10 on the lower stage are held in a surface-contact manner. A taper metal pipe 10N provided at an embedment part is made of stainless steel or titanium.SELECTED DRAWING: Figure 1

We provide an ordering system that can avoid space constraints in areas such as parts storage and production sites. [Solution] An ordering system 1 for ordering the delivery of multiple types of parts by specifying the quantity and delivery time, comprising a controller 4 that creates and transmits order data. The controller 4 determines the required parts, their quantities, and delivery times as order data. From the required parts, it determines the additional quantity of each part that can be loaded together until the cart is full, based on the ratio 6 of parts that can be loaded together on the same cart and the total number of parts that can be loaded together until the cart is full. The controller 4 then adds the parts and quantities, including the additional quantities, to the order data.

The invention discloses a B2B order intelligent management method and system based on reinforcement learning, and the method comprises the steps: collecting order data of a to-be-processed first order queue in real time, the order data comprising order priorities and delivery time; judging whether a target order meeting a preset order insertion condition exists in the first order queue or not according to the order data, if yes, extracting order feature information of the target order from the order data, and associating each piece of order feature information with a currently processed second order queue; according to an order insertion influence quantitative evaluation model, performing quantitative evaluation on the order feature information to obtain a quantitative evaluation result, the quantitative evaluation result comprising influence information of inserting the target order into the second order queue; the quantitative evaluation result is used for indicating a specific order insertion item of inserting the target order into the second order queue. Therefore, the processing flexibility and the management intelligence degree of the B2B order can be improved, and meanwhile, the accuracy of the order insertion decision and the order resource utilization rate are improved.

To provide an information processing apparatus and program allowing for setting up a requested time slot of delivery matched with the convenience of a customer by easier operation to understand.SOLUTION: A shopping supporting apparatus (information processing apparatus) comprises an order-information acquiring section that acquires order information related to a commodity which a customer desires to purchase, a possible date-and-hour of delivery output section that outputs for display possible date and time slot of delivery of the commodity to a customer terminal possessed by the customer, a desired date-and-hour of delivery acquiring section that acquires the date and time slot of delivery desired by the customer, a permissible range of hour acquiring section that acquires a permissible range of hour which the customer permits for a time slot of delivery, and an incentive calculating section (discount amount setting section) that sets up a discount amount of the commodity depending on the permissible range of hour acquired by the permissible range of hour acquiring section.SELECTED DRAWING: Figure 7

A system is provided.SOLUTION: A system comprising: user interface means for a receiver to input and change a desired delivery time; communication means for transmitting the desired delivery time input through the user interface means to a server; data management means for receiving the desired delivery time transmitted by the communication means and storing the desired delivery time in a database; route generation means for generating an optimal delivery route based on the desired delivery time stored in the data management means; and notification means for transmitting information on the delivery route generated by the route generation means to a terminal of a deliverer.SELECTED DRAWING: Figure 1

The invention provides a production and purchase collaborative optimization system and method based on a constraint theory. According to the method, a supply chain and a production process are optimized by defining a system target, identifying key constraints, coordinating purchase and production plans, implementing a purchase overdue early warning mechanism, and implementing a feedback and improvement mechanism. The system architecture comprises a front-end user interface and a control background, and the control background is provided with a target definition module, a constraint identification module, a production and purchase cooperation module, a purchase overdue early warning module and a feedback improvement module. And the early warning mechanism triggers early warning when the actual delivery time exceeds the predicted delivery time and the buffer erosion rate is greater than the buffer erosion rate threshold value by monitoring the predicted delivery time, the actual production plan and the raw material inventory of the purchase order in real time. According to the DBR optimization algorithm, the overall efficiency of the system is improved by recognizing constraint resources, setting a buffer area, establishing a signal mechanism and dynamically adjusting a purchase plan and a production plan. According to the invention, the collaborative efficiency of purchasing and production is effectively improved, the operation cost is reduced, and the timeliness and reliability of the purchasing process are ensured.

In e-commerce, web content (such as a checkout page) may include delivery time information associated with a product. To obtain the delivery time information, the computer may send an application programming interface (API) request to the fulfillment network. However, a fulfillment network is often delayed in responding to an API request. Also, there may be data privacy concerns with this approach. Alternatively, precomputed delivery time information may be sent from the fulfillment network, but this may result in a large amount of data that must be stored. In some embodiments, a data structure may be generated that includes a product-to-schedule mapping and a set of delivery schedule tables. Each of the delivery schedule tables may be based on a respective different set of one or more categories to which multiple ones of a plurality of products map. Computer memory resources may be saved.

The invention discloses a dynamic optimization method for multi-supplier order distribution on a cross-border supply chain, and the method comprises the steps: obtaining the historical cross-border logistics timeliness of a region where suppliers are located, and the historical long-term production quality, production cost and delivery time data of the suppliers, and carrying out the multivariate fusion preprocessing of the data through standardization and an attention mechanism; performing data decomposition; extracting local features through a time convolution network, capturing a long-term dependency relationship through a long-short-term memory network, and outputting a comprehensive prediction value; performing trapezoidal fuzzy number conversion and defuzzification processing on the qualitative standard of the supplier to generate a normalized fuzzy weight; and finally, through an iterative bilateral auction mechanism, iterative bilateral optimization is performed based on the predicted value and the fuzzy weight, and social benefit maximization of order allocation is realized. According to the method, the problem that quantitative and qualitative indexes are difficult to dynamically integrate in a traditional method is solved, and the efficiency and adaptability of cross-border supply chain order allocation are remarkably improved.

A system and method for optimizing delivery of data over hybrid networks by generating delivery plans derived from a delivery schedule and optimizing delivery timing, delivery method, and network selection based on data priority, caching availability, delivery cost, and destination reach.

The invention provides a goods taking method of an intelligent trolley and a control system. According to the method, the target task is added into the task waiting queue for queuing through the delivery time window and the delivery priority of the target task, and the waiting time of the target task is predicted through the latest pickup time window of the plurality of arranged tasks before the target task in the task waiting queue; predicting the latest goods taking time window of the target task based on the delivery time window, the waiting time length and the delivery planning path of the target task; and controlling the target intelligent trolley to complete goods taking in the latest goods taking time window of the target task. The starting time point of delivery of the intelligent trolleys is delayed through the latest goods taking time window, the intelligent trolleys including execution tasks can be included in the goods taking management range by strictly controlling the goods taking time window, the utilization rate of the intelligent trolleys in a factory is increased, and therefore the number of the intelligent trolleys on the road of the factory is reduced.

The invention relates to a system (100) for optimizing production, wherein the production is characterized by a production plan, comprising: an ordering device (110) configured to issue a purchase order, wherein the purchase order characterizes an order quantity of resources for production and a delivery time for receipt of the ordered item; a planning device (120) configured to determine the plurality of planning parameters based on the production plan, wherein the plurality of planning parameters characterize parameters configured to implement the production plan;An optimization tool (130) configured to create a simulation of the planning tool (120) and to optimize a variety of planning parameters of the simulation using a cost function based on input data, wherein the input data includes at least one planning parameter of the planning tool (120) currently used by the planning tool (120), wherein the optimization tool (130) is further configured, based on the simulation and the cost function, to output a variety of optimized planning parameters, and wherein the ordering tool (110) is configured to output the purchase order based on the variety of optimized planning parameters.

The invention relates to a system (100) for optimising production, wherein the production is characterised by a production plan, comprising: an ordering means (110), which is designed to output a purchase order, wherein the purchase order characterises an order quantity of resources for the production and a delivery time of an arrival of the ordered order; a planning means (120) which is designed to determine the plurality of planning parameters on the basis of the production plan, wherein the plurality of planning parameters characterises parameters which are designed to implement the production plan; an optimisation means (130), which is designed to create a simulation of the planning means (120) and to optimise a plurality of planning parameters of the simulation by means of a cost function based on input data, wherein the input data comprises at least one planning parameter of the planning means currently used by the planning means (120), wherein the optimisation means (130) is also designed to output a plurality of optimised planning parameters on the basis of the simulation and the cost function, wherein the ordering means (110) is designed to output the purchase order on the basis of the plurality of optimised planning parameters.

The invention provides a production task scheduling method and device for die change optimization, and the method comprises the steps: generating a main time axis for marking delivery time nodes, and a plurality of sub time axes which are the same as production equipment in number and are aligned with the main time axis; and filling production task units of products of various specifications and models in a time period between every two adjacent delivery time nodes corresponding to each time axis, so that the production quantity of the products contained in the production task units meets the delivery quantity of the corresponding delivery time nodes and has the shortest comprehensive mold change time. Each production task unit is provided with a die change time subunit and a production time subunit of a production batch plan of products of one specification and model, the judgment problem of the optimal die change opportunity can be converted into the dynamic calculation problem of the optimal production unit size, and the production efficiency is improved in a simpler and more visual mode.

In e-commerce, web content (such as a checkout page) may include delivery time information associated with a product. To obtain the delivery time information, the computer may send an application programming interface (API) request to the fulfillment network. However, a fulfillment network is often delayed in responding to an API request. Also, there may be data privacy concerns with this approach. Alternatively, precomputed delivery time information may be sent from the fulfillment network, but this may result in a large amount of data that must be stored. In some embodiments, a data structure may be generated that includes a product-to-schedule mapping and a set of delivery schedule tables. Each of the delivery schedule tables may be based on a respective different set of one or more categories to which multiple ones of a plurality of products map. Computer memory resources may be saved.

Computer-implemented method for controlling and / or regulating a spectacle lens manufacturing process, comprising: Recording orders for the production of spectacle lenses or pairs of spectacle lenses and storing the orders in an order data storage system; automatic sorting of the stored orders in an insertion sequence, wherein the sorting is performed according to at least one sorting criterion; and Inserting the spectacle lenses or pairs of spectacle lenses to be manufactured according to the individual orders into production, wherein the insertion of the spectacle lenses or pairs of spectacle lenses to be manufactured into the production takes place in discrete time intervals, and wherein within a discrete time interval the spectacle lenses or pairs of spectacle lenses to be manufactured are inserted into the production according to the insertion sequence, where at least one sorting criterion includes an insertion point, the insertion point for a specific order being determined by: Recording the time of receipt of the order, Adding to the time of receipt of the order within a predetermined delivery interval, thereby determining a delivery time; Subtracting from the delivery time of a machining interval, thereby determining the insertion time, wherein the order data storage is divided into several groups, each order is assigned to one of the groups, and wherein group membership is a sorting criterion; and where the sorting of orders within a group or subgroup is done according to the insertion time.

This invention discloses a dynamic optimization method for multi-supplier order allocation in cross-border supply chains. It acquires historical cross-border logistics timeliness data for the supplier's region, as well as historical long-term production quality, production costs, and delivery times data. The data undergoes multivariate fusion preprocessing through standardization and attention mechanisms; data decomposition is then performed; local features are extracted using a temporal convolutional network, and long short-term memory networks capture long-term dependencies, outputting a comprehensive predicted value; trapezoidal fuzzy number transformation and defuzzification are applied to the supplier's qualitative criteria to generate normalized fuzzy weights; finally, an iterative bilateral auction mechanism is used to perform iterative bilateral optimization based on the predicted values ​​and fuzzy weights, maximizing the social benefits of order allocation. This invention solves the problem of traditional methods struggling to dynamically integrate quantitative and qualitative indicators, significantly improving the efficiency and adaptability of cross-border supply chain order allocation.