Abstract: A calculation unit acquires first to third prices that are sales unit prices of a first product, a second product, and power generated by a power generation device. Based on at least one of the first to third prices, the calculation unit determines a first supply amount of the first product to a sales means, a second supply amount of the first product to a second manufacturing device, and a third supply amount of the first product to the power generation device. A control signal output unit outputs a control signal such that the first product is supplied to the sales means at the determined first supply amount, the first product is supplied to the second manufacturing device at the determined second supply amount, and the first product is supplied to the power generation device at the determined third supply amount.

Abstract: A method includes accessing historic five-minute electricity data for a predetermined number of previous days, determining, using the historic five-minute electricity data for the predetermined number of previous days, a maximum charge price for the predetermined number of previous days, and determining, using the historic five-minute electricity data for the predetermined number of previous days, a minimum discharge price for the predetermined number of previous days. The method further includes determining a current amount of charge of a battery, accessing live five-minute electricity data for a current day, and communicating one or more instructions to discharge a battery when a next live five-minute electricity price is greater than the determined minimum discharge price and the current amount of charge of the battery is greater than zero.

Abstract: The embodiment of the present disclosure provides a method and an Internet of Things system for controlling a gas supply cost based on smart gas. The method is implemented by a smart gas management platform of the Internet of Things system. The method includes: predicting a gas supply quantity for a future preset time period based on a planned gas supply quantity of a gas supplier; predicting a gas demand quantity for the future preset time period based on a gas usage quantity of a historical user; determining a gas gap for the future preset time period based on the gas supply quantity and the gas demand quantity; and determining a gas compensation scheme based on the gas gap, operational requirements of an end of a pipeline, and operational parameters of the end of the pipeline.

Abstract: Disclosed in the present invention is a spontaneous edge application deployment and pricing method based on an incentive mechanism. The method comprises the following steps: building an edge end application oriented spontaneous deployment system architecture; then proposing an incentive mechanism aiming at spontaneous edge application deployment and prizing; solving the spontaneous edge application deployment and prizing problem based on a backward induction method, thereby obtaining an optimal deployment solution of an edge server and an optimal prizing strategy of an application provider.

Abstract: Methods and systems for autonomous item delivery and/or pick up are provided. IN some aspects, a mothership travels along an item route. One or more autonomous delivery vehicles may be dispatched from the mothership as the mothership progresses along the route. Each of the autonomous delivery vehicles may deliver and/or pick-up one or more items at one or more item locations. In addition, as the mothership progresses along its route, it may also stop to facilitate the manual delivery and/or pick-up of additional items via a human operator. Upon completing their delivery and/or pick up tasks, the autonomous delivery vehicles return to the mothership, either at the point at which they dispatched from the mothership, or at a different location along the item delivery route.

Abstract: An unmanned aerial delivery vehicle acquires transfer position information indicating a position at time of package transfer of an unmanned ground delivery vehicle that receives a package in a case where a predetermined condition based on position information of the unmanned aerial delivery vehicle that transports a package is satisfied. A position of the unmanned ground delivery vehicle at the time of package transfer is identified by the unmanned aerial delivery vehicle. The unmanned ground delivery vehicle acquires delivery destination information of the package transported by the unmanned aerial delivery vehicle.

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.