The present disclosure discloses an information processing method, device, system and computer-readable storage medium, and relates to the field of information technology. The information processing method includes: determining a second user associated with a first user from users corresponding to distribution tasks of a distributor, wherein a state of goods of the second user is Shipped; generating reconsignment information to be provided to the first user, wherein the reconsignment information comprises information of the first user and goods information of the second user; generating distribution task information to be provided to the first user in response to the first user acquiring the reconsignment information and sending a confirmation of the reconsignment information; and changing the state of goods of the second user in the distribution task of the distributor to Delivered after acquiring a confirmation of completion of reconsignment sent by the second user.

Systems and methods which provide shipping management system shipping documentation generation operation which spawns intelligent generation subprocesses are described. The intelligent generation subprocesses of embodiments facilitate autonomous shipping documentation generation functionality of a concatenated shipping documentation generation process. A user may initiate a shipping documentation generation process, whereby an intelligent generation subprocess is spawned, and thereafter the user may be freed to perform one or more independent operation and/or interaction with the shipping management system. One or more verifications, checks, warnings, and/or the like are suspended and one or more parameter, input, selection, and/or the like are defaulted for operation of an intelligent generation subprocess of embodiments. Embodiments implement a documentation queue to provide historical information with respect to intelligent generation subprocess operation.

In implementations of item transfer control systems, a computing device implements a transfer system to receive input data describing types of requested items and corresponding quantities of the types of requested items to receive at each of a plurality of destination sites and types of available items and corresponding quantities of the types of available items that are available at each of a plurality of source sites. The transfer system constructs a flow network having a source node for each of the plurality of the source sites and a destination node for each of the plurality of the destination sites. An integral approximate solution is generated that transfers the corresponding quantities of the types of requested items to each of the plurality of the destination sites using a maximum flow solver and the flow network. The transfer system causes transferences of the corresponding quantities of the types of requested items to each of the plurality of the destination sites based on the integral approximate solution.

Methods, apparatuses, and systems for scheduling tasks to field professionals include: storing, in a database, a plurality of records reflecting characteristics associated with completing a set of technical services, wherein information in each record is derived from historical experience of completing each of the technical services; receiving a request for a new technical service associated with a location; and assigning a field professional to perform the new service having determined from information in the database a likelihood that the field professional will complete the new technical service in a single on-site visit at the location.

A computer system for ranking potential attended delivery/pickup locations is disclosed. In various embodiments, a user or computer system identifies an area in which to establish at least one attended delivery/pickup location. In a particular embodiment, the system receives data associated with potential attended delivery/pickup locations (e.g., attended delivery/pickup location candidates), including the specific characteristics of those candidates. The system then, based at least in part on the characteristics of each of the attended delivery/pickup location candidates, ranks the attended delivery/pickup location candidates and displays the rankings to a user for use in selecting the most suitable candidate for the area.

An online agricultural system manages and optimizes interactions of entities within the system to enable the execution of transaction and the transportation of crop products. The online agricultural system accesses historic and environmental data describing factors that may impact crop product transactions and/or transportation to determine market prices for crop products and crop product transportation. Responsive to receiving a request from an entity, the online agricultural system determines an optimal transaction for the entity, such as a price for selling a crop product, an available crop product for purchase, or a transportation opportunity to transport a crop product.

A system includes storage compartments, each of the storage compartments being configured to store shipments and having a locking mechanism and a computing system that controls operations of the storage compartments. The computing system performs: receiving a request from a providing entity to reserve a portion of the storage compartments, obtaining identification information of a requesting entity requesting a shipment from the providing entity, generating a scannable code; transmitting the scannable code and the identification code of the requesting entity to the providing entity, determining a compartment of the storage compartments to be reserved to temporarily store the shipment, detecting a scan of the scannable code or an input of the identification code on a computing device at a terminal of a location of or adjacent to the reserved determined compartment, and in response to detecting the scan or the input of the identification code, opening the reserved determined compartment.

A delivery method for a traveling vehicle using a drone includes: transmitting, by an orderer, first information including position information of a vehicle to a server and providing, by the server, a position and an inventory of service providers, which correspond to the first information including the position information of the vehicle, to the orderer; requesting, by the server, service provision to the service provider; transmitting, by the service provider, second information including article preparation information to the server; transmitting, by the server, third information including a plurality pieces of receipt position information, which corresponds to the first information including the position information of the vehicle and the second information including the article preparation information, to the orderer; transmitting, by the orderer, a value selected by receiving the third information including the plurality pieces of receipt position information to the server; transmitting, by the server, the selected value to a selected service provider; loading, by the selected service provider, an article of the orderer on a drone and instructing the drone to move to correspond to the third information including the plurality pieces of receipt position information; and receiving, by the orderer, the ordered article.

The systems and methods described herein relate to managing transportation of loads of items within a retail supply chain. A unified load tracking system aggregates information from multiple supply chain computing systems for presentation on a user interface accessible by multiple users. One user interface provide access to a multitude of information that is interconnected by a common, unique identifier for each movement of a load between an origin and destination. A load board is also provided which automates the spot market auction process and load tender process between carriers and a retail enterprise transportation management system.

A system and method for automated machine learning supply chain planning having a computer with a processor and memory and configured to receive a first supply chain network model having one or more material constraints for operations of a first supply chain network. Embodiments include transforming the first supply chain network model into a digital image, training an auto-encoder model to reduce the dimensionality of an input vector, and locating one or more items in the first supply chain network.