92 results about "Order fulfillment" patented technology

The invention belongs to the technical field of order fulfillment process monitoring, and discloses an order fulfillment process delay early warning method and system based on multi-node state acquisition, and the method comprises the steps: collecting the dynamic state data of multiple nodes in a fulfillment process in real time; matching judgment is carried out through the node rule base, primary early warning is triggered, and an early warning context data set is constructed; inputting the data set into a pre-trained delay risk prediction model, and outputting an overall risk probability and a key influence node identifier; issuing a grading early warning signal according to the risk probability; carrying out interpretable root cause positioning through a root cause analysis rule chain based on directed acyclic graph representation; incremental learning is carried out on the prediction model based on early warning feedback data to form an optimized closed loop; the system correspondingly comprises a data acquisition module, a rule judgment module, a risk prediction module, an early warning release module, a root cause positioning module, a feedback learning module and the like. According to the invention, the early-stage, accurate and automatic early warning and root cause analysis of the full-process risk of order fulfillment are realized.

The invention discloses a plastic trade scene-oriented cargo purchase-sales-stock data management system, which belongs to the technical field of cargo purchase-sales-stock management and specifically comprises a database, a purchase management module, a sales management module, a stock management and distribution module and an order tracking module. According to the method, a purchase order-inventory real-time binding mechanism is introduced, original inventory data is updated through in-transit inventory data, a traditional inventory is divided into new inventory concepts of available inventory and in-transit inventory, after sales order data is obtained, an inventory locking engine mechanism is called, a binding relation between a sales order and a new inventory is generated, and the new inventory is obtained. According to the method, the locked inventory is determined through matching and locking of the sales order and the inventory, the inventory data is updated again to cancel after verification of the locked inventory, dynamic monitoring and synchronous association of the inventory data, the purchasing data and the sales data are achieved, the inventory management efficiency and the order performance accuracy are effectively improved, and goods are effectively prevented from being oversold and mistakenly sold.

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.

A product order fulfillment system includes: a multi-level transport system, each level thereof having a corresponding independent asynchronous level transport system; a lifting transport system with more than one independent lift axis, in a common lift cell, each of the more than one independent lift axis being configured to independently hold and transport the at least one case along a trajectory, independently of each other independent lift axis; and a controller operably coupled to the multi-level transport system and the lifting transport system; where the asynchronous trajectories of the common lift cell output an ordered sequence of mixed cases in accordance to a predetermined case out ordered sequence of mixed cases; and where the controller is configured to heuristically generate optimal trajectory solutions for the asynchronous trajectories, and assign the at least one case to at least one asynchronous level transport axis based on at least one qualitative metric characterized by the optimal trajectory solutions.

A product order fulfillment system includes: a multi-level transport system, each level thereof having a corresponding independent asynchronous level transport system; a lifting transport system with more than one independent lift axis, in a common lift cell, each of the more than one independent lift axis being configured to independently hold and transport the at least one case along a trajectory; and a controller operably coupled to the multi-level transport system and the lifting transport system; where the asynchronous trajectories of the common lift cell output an ordered sequence of mixed cases in accordance to a predetermined case out ordered sequence of mixed cases; and where the controller is configured to heuristically generate optimal trajectory solutions for the asynchronous trajectories, and assign the at least one case to at least one asynchronous level transport axis based on at least one qualitative metric characterized by the optimal trajectory solutions.

An order fulfillment facility with a delivery system is disclosed having plurality of mobile robots with containers to bring customer orders and / or power supply device / s to UAV / s for final delivery. UAV leaves the discharged power supply devices that are received in empty container and transported to storage area for replenishment and diagnostic checks. Order fulfillment operations along with power supply device charging and monitoring is performed to achieve timely delivery of customer orders.

A system for order fulfilment including automated stacking of mixed case packages on a support in a predetermined spatial arrangement to form a stack of mixed case packages according to an order, the system includes at least one sortation unit for receiving all trays from each traying unit and a buffer storage unit for temporary storage of the trays sourced from the diverts, and more than one palletizing unit for stacking of mixed case packages on a support in a predetermined spatial arrangement to form a stack of mixed case packages according to an order being sourced with packages from at least one detraying unit.

This invention belongs to the field of smart logistics technology and discloses a method for cost and carbon emission collaborative optimization for hubs and regional warehouse networks. It constructs a three-dimensional global collaborative optimization model of cost, carbon emission, and timeliness, and adjusts target weights in real time through a dynamic weight adaptive mechanism. It integrates cost and carbon emission elements across the entire supply chain to establish a three-flow linkage, coupled with a multi-dimensional constraint system. This breaks down optimization barriers in warehousing and transportation, and dynamically adapts to regional carbon emission quotas and cargo urgency. While ensuring timely order fulfillment, it achieves the collaborative goal of minimizing overall cost and keeping carbon emissions within quota limits, balancing economic benefits and environmental responsibility, and enhancing the overall competitiveness of the supply chain. Through edge nodes, it achieves heterogeneous data cleaning and standardization, dynamically calibrates carbon emission factors and quantifies them into unified units, and completes distributed storage, access control, and full-chain traceability through a consortium blockchain, ultimately constructing a digital twin data foundation.

The invention discloses a cross-border e-commerce intelligent multi-warehouse allocation data simulation system and method, and the system comprises each warehouse inventory management module which is used for synchronizing global multi-warehouse real-time and in-transit inventory data; the sales order management module is used for acquiring undelivered order information; the key warehouse marking configuration module is used for setting an allocation strategy rule based on the inventory coverage rate; the global inventory view generation module is used for executing prospective inventory distribution simulation and outputting a global inventory view; the sales order marking module is used for marking whether the order needs to be delivered by a key overseas warehouse or not according to a view; and the just-to-be-allocated module is used for accurately calculating the minimum necessary allocation quantity required to be allocated from the Chinese warehouse to each key overseas warehouse according to the marked order, and generating an instruction report. According to the method, artificial experience is replaced by simulation calculation, data driving and prospective optimization of allocation decision making are realized, inventory overstock and cross-border logistics cost are effectively reduced, and order fulfillment speed and overall elasticity of a supply chain are improved.

A retail fulfillment system, including: a storage structure; a plurality of mobile robots configured to move laterally along storage structure aisles and vertically through the storage structure to deposit and retrieve containers to and from the plurality of container storage locations; and a container input / output interface system including: a matrix of container access bays where the plurality of mobile robots are configured to deliver and retrieve the containers to and from the container access bays through a robot interface. Each of the container access bays includes an autonomous interlock system that is activated, separate from the plurality of mobile robots, in response to a presence of a container, of the containers, at the respective one of the container access bays to cause a barrier system to transition to an access state enabling access by an operator through the access face to the container.

This invention discloses a method for visually tracking the entire lifecycle of chip sales orders, relating to the field of order management technology based on knowledge graphs. The method includes: acquiring order events and packaging identifiers of sales orders, identifying multiple sub-batches, and extracting event interval sequences for each sub-batch; querying the humidity clock semantics of each interval using a knowledge graph based on the order event information corresponding to the event interval sequences, determining the allowable exposure time and duration of each interval in conjunction with the packaging identifier, and generating a clock calculation sequence; calculating the remaining humidity clock quantity for each sub-batch, and constructing a set of remaining humidity clocks for each order sub-batch; extracting the conservative deliverable status, clock fork quantity, and dominant sub-batch for each lifecycle node, constructing an order node status group, and generating visual tracking results for the order. This invention can accurately reflect the state differentiation during the unpacking and batching of humidity-sensitive chips, improving the authenticity and targeted management of order fulfillment tracking.

The invention relates to the technical field of supply chain management, in particular to a supply chain collaboration platform intelligent software interaction system based on a large model, which comprises a dynamic replenishment strategy module for generating a dynamic replenishment strategy model according to historical purchase cycle data of multiple categories of materials, and the model takes an inventory turnover rate and a stockout probability as optimization targets; the collaborative prediction and feedback module takes an output result of the dynamic replenishment strategy model as an input, drives a collaborative prediction engine to perform order fulfillment deviation analysis, and dynamically updates prediction model parameters through a feedback mechanism; the logistics routing adaptive adjustment module is used for constructing a path propagation map based on the time sequence data of the logistics abnormal event, and performing adaptive adjustment on a routing decision model in combination with a real-time transportation state; and the intelligent contract cooperative regulation and control module synchronizes the adjusted routing strategy and the production plan change information to an intelligent contract system, and triggers scheduling elastic regulation and control and cooperative clause automatic updating of each node of the supply chain.

In some implementations, a method performed by data processing apparatuses includes determining whether a location of a customer device is within a predetermined proximity of the retail store. In response to determining that the location of the customer device is within the predetermined proximity, the method further includes receiving an item identifier from the customer device, determining a stock room location of the item in response to receiving the item identifier, sending a first notification indicative of the item, the stock room location, and a collection point to an employee device. The method further includes receiving a checkout request from the customer device, determining a pickup location within the retail store in response to receiving the checkout request, and sending a second notification to an employee device indicative of the collection point location and the pickup location. The stock room is a secured physical space of the retail store.

One variation of a system includes: a population of trays storing product units; a dispensing unit configured to dispense product units from the population of trays; a mobile robotic system; and a controller. The mobile robotic system includes: a receptacle configured to support a base and a first sidewall of a semi-rigid container transiently loaded in the receptacle at an angle; and a sidewall retainer configured to retain a second sidewall of the semi-rigid container. The controller is configured to: trigger the mobile robotic system to navigate to a dispensing unit loaded with a tray transiently storing product units of a product type specified in an order; trigger the mobile robotic system to maneuver the receptacle to locate the semi-rigid container to receive the product unit at a target position; and trigger the dispensing unit to dispense the product unit into the semi-rigid container.

The invention relates to the technical field of intelligent transportation and internet data processing, in particular to an air-train travel multi-channel catering service intelligent matching and order fulfillment system. Comprising the following steps: a full-link heterogeneous data sensing step: collecting passenger travel states, movement characteristics, station space topology and merchant production capacity data; a space-time constraint analysis step: constructing a multi-modal space-time constraint model, and reversely resolving an effective performance time window of the passenger at the transfer node; a performance risk assessment step of calculating an order performance success probability based on the matching degree of the merchant production capacity and the effective performance time window; a self-adaptive performance execution step: executing intelligent filtering, dynamic triggering or fusing rerouting operation according to the order performance success probability and the disturbance condition of the real-time travel state data, and generating a cross-scene cooperation instruction; the risk of order failure caused by space-time dislocation is eliminated, and synchronization of energy supply and travel rhythm in cross-modal travel is ensured.

A “meet me” model of collaboration among autonomous mobile robots (AMRs) and human workers (pickers) to execute picking and putting within a logistics facility. The AMRs include an intelligent platform having an array of indicator elements, subsets of which are illuminated to indicate tote locations and numbers of items to be received therein. The pickers receive instructions from a central server on a user device that they hold or wear, and the AMRs receive a list of stop locations within the logistics facility. Selection of the picker to provide an item to an AMR as it approaches one of the stop locations may be dynamic as each picker instruction includes a single item type to be delivered to / from an AMR. The central server determines timing for sending the instructions to a user device based on a location of the robot designated to collect an item.

A conveyor system for transporting goods within a warehouse facility. The system includes an array of guide tracks and carriages moveable along the guide tracks for transporting goods within the warehouse facility. A drive system includes drivers spaced along the guide tracks. The drivers may utilize linear induction motors, propels the carriages along the guide tracks. The linear induction motors utilize stepper motors, and optionally microstepping motors. A warehouse management system controls the linear drive system to transport goods throughout warehouse facility for storage and order fulfilment. The linear driver conveyor system includes perpendicular transfers at which carriages can be transferred from one conveyor line to another, without the need for mechanical processes, such as lift or buffers, to move the carriage between conveyor lines.

The disclosed technology provides for incentivizing a user to pick up an order at an fulfillment location. A method can include receiving, at a mobile device, information for picking up an order at a fulfillment location and and incentivization information from a server system, where the incentivization information includes information for an incentive associated with picking up the order at the fulfillment location during an incentivization window. The method can include changing a display of the computing device to display information for picking up the order during the incentivization window, receiving a second communication from the computing device indicating that the computing device has arrived at the fulfillment location, and changing the display of the computing device to display a confirmation of the incentivization information based on whether the particular timepoint at which the computing device arrived at the fulfillment location is within the incentivization window.

A warehouse for order fulfilment with a product storage and at least one order fulfillment level in which picking stations are arranged and supplied with articles from the product storage for picking into order load carriers according to allocated orders wherein each picking station is supplied with articles from the product storage by a connecting conveyor system on the least one order fulfillment level, where each order fulfillment level has at least one further virtual order fulfillment level in which picking stations are arranged at different height from the at least one order fulfillment level.

An online concierge system receives an order from a user including items to obtain from a retailer for delivery to a location. A picker selects the order and obtains items from the retailer. The user selects a time interval during which items from the order are delivered to the location. To prevent the user from selecting a time interval for fulfillment the online concierge system prevents the user from selecting a time interval when a picker may be unable to obtain the items from the retailer before a closing time of the retailer. The online concierge system evaluates time intervals by subtracting a travel time for the picker travelling from the retailer to the location from a predicted fulfillment time for the order. This prevents the time for delivering items after being obtained from affecting whether a time interval may be selected.

The application discloses a kind of water-based paint intelligent warehousing's inventory management method and system, it is related to water-based paint intelligent warehousing inventory management technical field, including with batch identification to establish batch inventory data model, and the same batch inventory record is formed to specification identification, inventory quantity, quality state and process occupation state;Receive batch call request of production scheduling process or delivery process, and generate sampling state determination result according to batch inventory record;According to sampling state determination result, receive sampling result, and corresponding batch is switched to allow call state or blocked state;Recalculate multi-type inventory based on blocked state and process occupation state, obtain deliverable inventory and producible inventory;Future seven days demand and planned output are mapped to corresponding specification, combined with deliverable inventory, calculate seven days order fulfillment capability;When seven days order fulfillment capability is lower than future seven days demand, generate inventory emergency warning, and according to batch processing result, rewrite inventory state.

A system includes a plurality of location indicators for arrangement throughout a store that includes a plurality of stocked items. Each of the location indicators defines a different area of the store. The system includes a computing system configured to generate a location map that defines a plurality of sections in the store. Each section includes a plurality of areas of the store defined by different location indicators. The location map defines how the sections and the areas are arranged. The system includes a mobile scanning device that is configured to wirelessly receive an electronic customer order including a plurality of ordered items. The mobile scanning device determines a location value associated with a location indicator and arranges at least some of the plurality of ordered items on the display based on the determined location value and the location map.

A network based order fulfillment systems having an improved user interface at both a customer device and at an order fulfillment location employee device. Both customer and employee devices can collect user input using unique user interface elements to elicit input from a customer in a retail environment on proper placement of items for an order in a vehicle. The unique user interface elements can be customized based on information specific to the user's vehicle. The elicited user input can be used to update a user interface of a second computing device.

A cart assembly includes a cart and a container assembly that is removably supported on the cart. The container assembly may simply rest on a platform of the cart, or may be removable secured to the cart. The container assembly includes a crate and a liner that is removably received within and supported by walls defining an interior cargo space of the crate. The liner itself includes walls containing a phase change material. The walls may be defined by panels having interior spaces in which components encasing the phase change material are contained. The liners are collapsed and stored in a freezer until needed. The liners are then placed within the crates and food items requiring refrigeration are placed within the lined cargo spaced of the crates. The crates are then staged awaiting customer pickup or packed in a vehicle for customer delivery all without further refrigeration.

This invention discloses a collaborative control method for order fulfillment in a warehousing system using bin handling robots, comprising the following steps: S1, acquiring order information, bin location information in the storage rack, and the operating status information of each robot used for bin handling, and generating a set of order fulfillment tasks; S2, constructing a learning strategy model based on a mutually simulated commercial state space compression mechanism, and processing the set of order fulfillment tasks through the learning strategy model to generate execution instructions; S3, issuing the execution instructions to the corresponding robots to control them to perform the corresponding operations; S4, in a multi-robot concurrent operation scenario, utilizing the global state value assessment output by a centralized evaluation network, and collaboratively optimizing the parameters of the distributed strategy network through a multi-agent near-end strategy optimization algorithm to reduce driving conflicts, duplicate paths, and idle operation among robots, thereby reducing the overall energy consumption of bin handling.

A warehouse for order fulfilment with at least one order fulfillment area in which picking stations are arranged and supplied with articles from a product storage for picking orders wherein each picking station is supplied and collected with product containers by a connecting conveyor system and the connecting conveyor system also supplying order containers and being used for discharge of emptied product containers and partial order or finished orders from the respective picking station, where a bypass conveyor is arranged in parallel to the connecting conveyor system such that product and / or order traffic inbound to the picking stations and outbound from the picking stations may be off-loaded from the connecting conveyor system.

An opportunistic rail charging system is disclosed for recharging a power source on a mobile robot transporting cargo within an automated order fulfillment system. Individual chargers may be incorporated into each mobile robot to convert the facility line voltage from the charging rail to a rated voltage of the rechargeable power supply on each mobile robot.

An order fulfillment system for fulfilling orders for goods includes a storage structure configured to store totes, mobile robots configured to transport the totes, and a workstation. The workstation includes a first station configured to receive product totes storing the goods for fulfilling the orders and a second station configured to receive order totes storing the goods of fulfilled orders. The order fulfillment system may also include containers configured to receive the goods from the product totes or the order totes. The containers may be stored at a third station of the workstation and / or in an order tote located at the second station of the workstation and / or in at least one shopping cart located at the workstation.

A multi-level order fulfillment workstation (100, 200, 300, 600) such as a person-to-good (PTG) picking station having at least two sub-stations (102, 104, 202, 204, 302, 304, 602, 604) that are vertically separated from one another, each with a plurality of storage locations (103, 203, 303, 603) for goods, and including a plurality of robots (106, 607) configured to operate at the workstation (100, 200, 300, 600) and one or more unpowered vertical travel supports (120, 120', 220, 220, 320, 320', 620, 620', 720) disposed between the at least two sub-stations (102, 104, 202, 204, 302, 304, 602, 604). The robots (106, 607) are configured to support totes (113) and traverse the vertical travel supports (120, 120', 220, 220, 320, 320', 620, 620', 720) under their own power for operating at each of the sub-stations (102, 104, 202, 204, 302, 304, 602, 604).