Abstract: The present disclosure provides a computerized method for package management, including: storing, in a data structure, information of a seller in association with a plurality of items sold by the seller; receiving an indication of a transaction, the transaction indication including an address of a customer and at least one item identifier corresponding to an item sold by the seller; converting the transaction indication into a shipment request by formatting the transaction indication according to a standardized format required by a shipper; generating a message containing the formatted shipment request; transmitting the message to the shipper in real time to initialize shipment of the item; and providing real time confirmation to the seller and the customer that shipment has been initialized.

Abstract: The disclosed computer-implemented method may include matching transportation requests. By collecting and batching match requests over an extended period, a dynamic transportation matching system may identify more efficient matches (e.g., may match transportation requests with greater overlaps). In addition, by dynamically setting and/or extending the upper bound of time that a transportation request may remain batched with other transportation requests, the dynamic transportation matching system may account for contextual information thereby situationally improving matching efficiencies made possible with higher upper bounds while avoiding requestor dissatisfaction, lost conversions, or other inefficiencies that may result from upper bounds that are too high.

Abstract: Provided is a transport system, a control device, and a method whereby package transport efficiency can be improved. A transport system 1 includes at least one memory configured to store a program code, and at least one processor configured to access the program code and operate as instructed by the program code. The program code includes a detection code configured to cause the at least one processor to detect a first package to be collected at a specified collection location, and an identification code configured to cause the at least one processor to identify a second package to be delivered to the collection location.

Abstract: Methods and systems for recipient-assisted recharging during delivery by an unmanned aerial vehicle (UAV) are disclosed herein. During a UAV transport task, a UAV determines that the UAV has arrived at a delivery location specified by a first flight leg of the transport task. The UAV responsively initiates a notification process indicating that a recipient-assisted recharging process should be initiated at or near the delivery location. When the UAV determines that the recipient-assisted recharging process has recharged a battery of the UAV to a target level, and also determines that a non-returnable portion of the payload has been removed from the UAV while a returnable portion of the payload is coupled to or held by the UAV, the UAV initiates a second flight segment of the transport task.

Abstract: The disclosed computer-implemented method may include matching transportation requests. By collecting and batching match requests over an extended period, a dynamic transportation matching system may identify more efficient matches (e.g., may match transportation requests with greater overlaps). In addition, by dynamically setting and/or extending the upper bound of time that a transportation request may remain batched with other transportation requests, the dynamic transportation matching system may account for contextual information thereby situationally improving matching efficiencies made possible with higher upper bounds while avoiding requestor dissatisfaction, lost conversions, or other inefficiencies that may result from upper bounds that are too high.

Abstract: A method (800) and system (150) for optimizing delivery of consignments is disclosed. Real-order data for delivering a consignment including a plurality of packages is received. The real-order data includes package related information and vehicle related information, which are pre-processed to generate a plurality of inputs. A machine learning model (164) trained using DRL is selected to optimize an objective function of minimizing an overall cost of consignment delivery by optimizing a number of vehicles selected for consignment delivery and optimizing a number of consignees and a number of drop locations serviced by each selected vehicle. The plurality of inputs is provided to the machine learning model (164) to predict a sequence of loading actions in relation to loading of the plurality of packages in the vehicles. A loading plan (504) is generated based on the sequence of loading actions. The loading plan (504) optimizes the delivery of the plurality of packages associated with the consignment.

Abstract: Disclosed herein are system, method, and computer program product embodiments for managing a user request for a transport of good. An embodiment operates by receiving a user request for the transport of the good from a pickup location to a destination location. After receiving the user request, a pickup and destination node is identified from a pre-generated graph of nodes. Subsequently, a transit cost for the pickup location or the destination location is determined. Based on the lowest transit cost, a preferred route among possible routes from the pickup location is generated. Thereafter, a transporter arrival time related to the pickup location and/or an intermediate location is determined based on the preferred route, and a transporter to pick up the good from the pickup location or the intermediate location is assigned and tracked during the transporting of the good.

Abstract: Methods and systems for recipient-assisted recharging during delivery by an unmanned aerial vehicle (UAV) are disclosed herein. During a UAV transport task, a UAV determines that the UAV has arrived at a delivery location specified by a first flight leg of the transport task. The UAV responsively initiates a notification process indicating that a recipient-assisted recharging process should be initiated at or near the delivery location. When the UAV determines that the recipient-assisted recharging process has recharged a battery of the UAV to a target level, and also determines that a non-returnable portion of the payload has been removed from the UAV while a returnable portion of the payload is coupled to or held by the UAV, the UAV initiates a second flight segment of the transport task.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: obtaining a travel plan of a user including one or more travel item. Success values of respective travel items are calculated based on respective preferences by the user for each travel item in respective SV categories. Real time data relevant to the travel items are acquired and success values are updated for any predicted changes to any travel items, as caused by the real time data. The changes and updated SV is produced to the user.

Abstract: A system includes a memory and at least one processor to import data from a source, the data comprising a list of legs and a list of multi-leg truck loads (MTLs), each leg comprising a trip from an origin to a destination and each MTL comprising at least one leg, automatically generate an unapproved package comprising a sequence of MTLs selected and removed from the list of MTLs, store the unapproved package in a database, transmit the unapproved package to a client computing device for approval, and receive the approval of the unapproved package from the client computing device and store the unapproved package as an approved package in the database.

Abstract: Data sources containing a plurality of health information is combined throughout a plurality of travelers. Profile vectors are created from each individual member of the traveling party and a group profile vector is built as a representative whole of the traveling parties interests. Potential travel destinations vectors are clustered and mapped with dimensions comprised in the group profile vector. A recommended vacation or travel destination itinerary is proposed based on the highest overlaying dimensional score between the group profile vector and potential travel destinations.