214 results about "Order fulfillment" patented technology

The invention relates to the technical field of commodity trade management, and discloses a commodity trade business management system based on a block chain. A block chain data acquisition module of the system obtains commodity basic information, trade enterprise qualification information and logistics node information in real time through a preset data interface, and synchronizes the information to a block chain distributed account book; the trade risk quantitative analysis module generates a trade link real-time risk coefficient set based on account book real-time data in combination with a trade compliance rule base, historical order fulfillment data and current logistics state data; a risk control strategy collaborative analysis module generates a plurality of candidate risk control strategy schemes when the risk coefficient exceeds a preset threshold value, calculates a collaborative execution cost factor when the enterprise qualification state is changed in combination with logistics node information, and obtains an interference influence value set of each scheme on the trade process; and a risk control strategy execution feedback module updates a block chain smart contract execution instruction according to the optimal risk control strategy scheme, and facilitates efficient management and control of commodity trade business.

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.

Some embodiments provide order packaging control systems, comprising: a product packaging (PP) control circuit communicatively couple with an automated custom package fabrication (CPF) system configured to fabricate on demand custom packages, an automated product retrieval (PR) system configured to autonomously retrieve one or more products, pick stations, and a set of sensor systems. The PP control circuit can be configured to receive orders; prioritize the orders and define an order fulfillment sequence, track states of the PR system and the CPF system; determine product retrieval timing, and correspondingly determine fabrication timing of when the CPF system is to fabricate corresponding packaging; and control the PR system and the CPF system in controlling synchronous operation of the PR system and the CPF system to synchronize an availability of products of an order at a pick station and an availability of a custom package at the pick station.

The embodiment of the invention provides an order implementation method, system and device based on distributed transactions, equipment and a storage medium, and relates to the technical field of computer application. Receiving an order request sent by user equipment, and sending a TRY request to each micro-service in the plurality of micro-services in parallel; after it is determined that the data reservation operation of each micro-service is successful, CONFIRM requests are sent to each micro-service in parallel; and after determining that the data reservation operation of any micro-service fails or receiving a cancel request for any micro-service, respectively sending a CANCEL request to each micro-service, so that each micro-service cancels the data reservation operation of the micro-service after receiving the CANCEL request sent by the order service. According to the invention, the method achieves the control of a plurality of micro-services, improves the business processing efficiency while guaranteeing the consistency and atomicity of distributed transactions, and further improves the business throughput.

A trained model is used to generate a post-delivery effort-based tip increase recommendation for a user of an online system. The online system applies the computer model to predict, based on information about an original order placed by the user and data describing an effort required to fulfill the order, a tip amount that is likely to lead to satisfaction of a picker associated with the online system who fulfilled the order. Responsive to information about a sentiment of the user in relation to the fulfillment process, the online system generates, based on the predicted tip amount and an original tip amount provided by the user before the fulfillment process for the order was completed, a tip adjustment amount. The online system causes a user interface of a device associated with the user to display the tip adjustment amount prompting the user to adjust the original tip amount.

Aspects of the present application relate to an order fulfillment system that may include one or more product induction regions. A given product induction region may include a plurality of product storage apparatuses, each holding a plurality of products, and a product transfer apparatus operable to transfer a product, among the plurality of products, into a shipping container. The order fulfillment system may include autonomous mobile robots (AMRs). An AMR may move to a shipping container delivery system to receive a shipping container and then move the shipping container to the product transfer apparatus to receive the product. The AMR may then move to a further location for further processing of the shipping container. The order fulfillment system may be combined with a production system. The product storage apparatuses may be universal crates, which may be returned to production system when empty to be refilled with products.

The invention belongs to the technical field of logistics management, and particularly relates to a monitoring management system for e-commerce logistics, which comprises a parameter acquisition module, a parameter preprocessing module, a parameter processing module, a dynamic scheduling module, an early warning execution module and a strategy optimization module. On the basis of dynamic matching of delivery urgency, cost coefficient and vehicle bearing capacity, transportation resource allocation is optimized, the no-load rate and resource waste are reduced, and the logistics resource utilization rate is remarkably increased; a three-level early warning mechanism and an elastic scheduling strategy quickly cope with load fluctuation, overload and load reduction / low-load capacity expansion, the logistics delay risk is reduced, and the order performance timeliness is guaranteed; through data-driven priority delivery coefficient calculation and simulation optimization threshold adjustment, manual dependence is reduced, and the scientificity and foresight of scheduling decisions are improved; the strategy optimization module constructs a self-adaptive logistics management system through historical data backtracking and bottleneck diagnosis, a dynamic iteration algorithm model and threshold parameters, and improves industry competitiveness for a long time.

A system is configured to train a customer understanding model to generate a preference score for substitution items. The customer understanding model generates a preference score for each of a plurality of related substitution items based on order data including data indicative of at least one item ordered and location data indicating a location of a first store. The customer understanding model ranks each of the substitution items based on the preference score. Order data is transmitted including substitution data identifying each of the substitution items and corresponding rank. Performance data associated with a set of operations implemented based on the order data and the substitution data is obtained. An updated customer understanding model is trained based on the performance data and iteratively modified based on the updated training dataset and updated performance metrics generated from second performance data.

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 incentization 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.

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 relates to the technical field of artificial intelligence engineering, and provides a supply chain management-oriented multi-modal AI large model fusion software platform construction method, which adopts the following technical scheme that the supply chain management-oriented multi-modal AI large model fusion software platform construction method comprises the following steps of: collecting multi-source heterogeneous data in a supply chain, comprising real-time inventory change data, historical demand fluctuation characteristics, emergency semantic information, transportation mode multi-modal information, supplier risk scores and public opinion analysis results, and non-structured documents and numerical indexes of cross-enterprise business processes, a multi-modal AI large model fusing texts, images and time sequence information is constructed based on the collected data, and the multi-modal AI large model is used for analyzing the data. And performing semantic feature extraction, numerical index fusion modeling and risk prediction, outputting based on a multi-modal AI model, dynamically regulating and controlling an inventory water level, adaptively regulating and controlling a purchase plan, intelligently regulating and controlling a logistics scheduling strategy, foresight regulating and controlling a supply chain cooperation strategy, and regulating and controlling an order fulfillment progress in real time.

Based on orders fulfilled by shoppers of an online concierge system, the online concierge system identifies items in an order that are difficult to find in a warehouse in which the order is fulfilled. When a shopper obtains a difficult to find item from the warehouse, the online concierge system prompts the shopper to provide information for finding the difficult to find item in the warehouse. The online concierge system stores the information for finding the difficult to find item from the shopper in association with the difficult to find item and with the warehouse. Subsequently, when a different shopper is fulfilling an order from the warehouse including the difficult to find item, the online concierge system displays the information for finding the difficult to find item in the warehouse to the different shopper.

The invention provides an order fulfillment method and system supporting delayed delivery after cut-off, and relates to the technical field of warehouse management, and the method comprises the steps: obtaining fulfillment orders of a plurality of delayed delivery requests; carrying out delayed delivery qualification verification on each fulfillment order; inquiring the real-time state of a warehouse-out notification list associated with each verified performance order in the warehouse; according to the real-time state, classifying and intercepting the performance orders passing the verification to obtain a plurality of first target performance orders which are successfully intercepted; performing performance order delivery date modification processing on each first target performance order to generate a plurality of second target performance orders; under the condition that each second target fulfillment order reaches the delivery day cut-off time, generating a finished goods supply order, an ex-warehouse notification order and a delivery carriage acceptance order of the second target fulfillment order, and synchronously executing management operations of inventory freezing, inventory release and inventory supplementary occupation; and otherwise, waiting for the second target fulfillment order to the delivery day cut-off time.

The invention belongs to the related technical field of vehicle control and scheduling, and particularly relates to an unmanned vehicle scheduling system and method based on strategy driving, and the system employs a three-layer architecture, and comprises a framework layer which comprises a visual human-computer interaction interface, a scheduling service system and an automatic driving system, data management, work order management, scheduling task execution and unmanned vehicle state feedback functions are provided, and the layer is kept unchanged when a service scene is newly added; the strategy layer supports three elements of dynamic configuration and strategy management and provides a customized interface of scheduling rules and parameters, and the three elements of the strategy comprise a scheduling algorithm plug-in program, a parameter configuration page and a new work order page; and the scheduling layer is used for instantiating and operating scheduling logic, associating a specific strategy, calling the function of the framework layer and the scheduling capability defined by the strategy layer to command the unmanned vehicle to complete scheduling operation, and realizing business work order fulfillment. The system and the method have the advantages of rapid scene adaptation, high system compatibility, low development cost, flexible service configuration and the like.

A mobile robot for travelling within an order fulfillment center including a deck and one or more vertical storage racks, a vertical storage rack of the one or more storage racks including a plurality of horizontal rails, spaced apart from each other on respective levels of the vertical storage rack, and a pair of vertical tracks, spaced apart from each other and extending between the plurality of levels of the vertical storage rack, the mobile robot including a motor, a shaft configured to be driven by the motor, first and second pairs of wheels and a pair of drive gears.

A system includes a plurality of stocked items arranged throughout a store, wherein each of the stocked items includes an item identifier (ID) code. A computing system receives an electronic customer order that includes a plurality of ordered items. A mobile scanning device is carried by a user and includes a mobile scanning device ID. The mobile scanning device wirelessly receives the electronic customer order. The mobile scanning device scans a first item ID code on a first stocked item and wirelessly transmits the mobile scanning device ID to the computing system along with the first item ID code indicating that the first stocked item has been picked and scanned. The computing system generates association data that associates the mobile scanning device ID with the first item ID code. The association data indicates that the first stocked item is moving throughout the store with the mobile scanning device.

An automated order fulfillment system and mobile robot are disclosed, where the mobile robot includes a compliant drive for moving between levels of a multilevel storage structure. In one example, the compliant drive comprises a drive shaft having splines configured to provide rotational play that prevents jamming of a vertical drive gear on the end of the shaft with a rack in a vertical track.

A system and method is provided for controlling an automated warehouse or order fulfillment facility. The system includes a sequencing tower, which functions as a buffer for inbound items and shipping containers, and various pick / decant workstations which are connected between the sequencing tower and an automated storage and retrieval system (ASRS). The sequencing tower is adapted for receiving, storing, and releasing newly erected shipping containers and inbound vendor cases. The pick / decant workstations function as a hub between the sequencing tower and the ASRS, where operators at the pick / decant workstations pick order items to shipping containers to fulfil orders or pick inbound / vendor items to inventory containers to be stored in the ASRS. The system and methods synchronize the sequencing tower and ASRS to release items to arrive at the workstations simultaneously to optimize efficiency and throughput of the facility.

The invention relates to the technical field of supply chain finance, in particular to a supply chain finance movable property pledge supervision risk dynamic assessment method and system, and the method comprises the steps: obtaining transportation GPS position and temperature, humidity and vibration data, marking abnormity, analyzing order fulfillment and inventory circulation, judging inventory overstock or delay, assessing price fluctuation risk, and calculating pledge risk weighted value. And judging and updating the risk level. According to the invention, through real-time monitoring of temperature and humidity, vibration, path deviation and other information in the transportation process, whole-process monitoring of transportation and inventory circulation is realized, and the pledge risk identification capability is improved. Weighted evaluation is carried out in combination with transportation, performance, inventory and market price data, the risk contribution degree is reflected, the evaluation result is updated in time, a supervisor is helped to find problems in advance, the response speed is increased, the fund risk is reduced, the transparency and safety of movable property pledge management in supply chain finance are improved, and the financing risk is effectively prevented.

A method or system for dynamically optimizing order fulfillment wait times for shoppers using machine learning. The system identifies a shopper's current location via their client device. A trained machine learning model predicts: (i) a first wait time for receiving orders at the current location, (ii) a second wait time at an alternate location, and (iii) a travel time between the two locations. The model is trained using labeled data of shopper wait times, iteratively refining parameters to minimize prediction error. A combined wait time for the alternate location is computed by summing the predicted second wait time and the travel time. If the combined wait time is shorter than the wait time at the current location, the system suggests the alternate location to the shopper. Instructions are transmitted to the shopper's device to display a user interface with a map, the suggested route, and a recommendation indicator.

Systems and methods for an e-commerce health care marketplace are provided. In one embodiment, a method comprises, responsive to a query from a user, retrieving from a database a list of responses comprising one or more health care providers offering one or more health care services at a provider-supplied price; filtering the list of responses based on a geographic location included in the query; providing, for display, the filtered list of responses; receiving a payment from the user for a response selected from the filtered list of responses; automatically sending a notification of the payment to a provider associated with the selected response; and responsive to receiving an order fulfillment notification from the provider, automatically providing the payment to the provider. In this way, health care consumers may make informed purchasing decisions when shopping for health care services and health care providers may receive prompt payment for services delivered.

A method of order fulfillment at a fulfillment center includes: identifying a first number of available collection units available for processing a corresponding first number of orders, identifying, from a list of unfulfilled orders at the fulfillment center, a second number of unfulfilled orders each of which is to be assigned to one of the first number of available collection units for packing, where the second number is selected such that an efficiency metric, which is based on (i) a total distance traversed by a collection device to collect the component items corresponding to the second number of unfulfilled orders from the storage area and (ii) a third number of component items collected using the collection device traversing the total distance, satisfies a threshold condition, and generating a signal to assign one or more particular collection devices to collect the third number of component items.

Disclosed is a robot for performing three-dimensional (“3D”) or multi-plane sortation and / or order fulfillment. The robot may include a motorized base to move about a first plane, a lift that raises and lowers a dispensing receptacle atop the lift about a second plane, and one or more actuators that modify a position of the dispensing receptacle from an upright position to a first tilted position in which the dispensing receptacle is tilted towards a first side of the robot and to a second tilted position in which the dispensing receptacle is tilted towards an opposite second side of the robot. The robot may receive and carry items when the dispensing receptacle is in the upright position, and may dispense the items to a destination on either side of the robot by tilting the dispensing receptacle to the first tilted position or the second tilted position.

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.

The invention discloses a warehouse management method and apparatus, an electronic device and a computer readable storage medium. The method comprises the steps of obtaining customer information and product information of an order; matching a target warehouse group corresponding to the customer information from a plurality of warehouse groups according to the customer information; determining a plurality of second warehouses and warehouse-out product information corresponding to each second warehouse from the target warehouse group according to the product information; and generating first order fulfillment information including the second warehouse information and the ex-warehouse product information. According to the invention, the association among the plurality of warehouses is established according to the customer information, the target warehouse group corresponding to the order can be matched according to the customer information, and the order demand is matched through the target warehouse group, so that a single order can call the plurality of warehouses which are not limited by regions, and the warehouse calling efficiency can be better improved. And the inventory resources are utilized to the maximum extent.

Tote adapters useful to handle and carry one or more containers containing products purchased by consumers and configured to be loaded into a tote in an order fulfillment system include a first panel portion comprising a first container interface configured to engage a first portion of a container configured to contain products purchased by a customer as part of an order placed by the customer and a first tote interface configured to rest on a first edge of a tote. The tote adapters further include a second panel portion comprising a second container interface configured to engage a second portion of the container, and a second tote interface configured to rest on a second edge of the tote.

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.

Fleet management of unmanned aerial vehicles (UAVs) is disclosed. Fleet management systems and methods may be implemented within UAVs, or systems utilizing UAVs, utilized in a delivery system configured to pick up a payload or a package at a shipping location and deliver a payload or package to a delivery location. The fleet management systems and methods may facilitate system operations of a UAV fleet, including, without limitation, positioning or deploying UAVs to meet or fill demand, prepositioning UAVs to meet a demand prediction, association delivery' orders to UAVs, order fulfillment, fleet maintenance management during operations, and flight path planning or generation.