The techniques disclosed herein enable systems to solve routing problems using machine learning augmented by optimization modules. To plot a route, a system receives a plurality of nodes from a problem space. The plurality of nodes is then analyzed by an optimization module and ranked based on various criteria such as distance from a reference node and deadline. Based on the ranking, the optimization module can select a smaller subset of nodes that is then processed by a machine learning model. The machine learning model can then select a node from the subset of nodes for addition to a route. This process can be repeated until a route is plotted for the full set of nodes within the problem space. In addition, the system can be configured to monitor current conditions of the problem space to modify the route in response to changes.

A program causes an information processing device communicable with a server to execute steps of: displaying stoppage points of the specific vehicle and at least part of a travel route passing through the stoppage points; transmitting the server a first request to modify the travel plan so that a user of the information processing device gets on the specific vehicle or gets off the specific vehicle at the stoppage point when the stoppage point is designated by a user operation; and transmitting the server a second request to modify the travel plan so that the specific vehicle goes by way of the designated point as an additional stoppage point and the user gets on the specific vehicle or gets off the specific vehicle at the additional stoppage point when the point other than the stoppage points is designated by the user operation.

A medical treatment server is configured to reduce load on physicians for work other than examination and treatment. The server is configured to send recruiting conditions and in response receive undertaking conditions that are related to a physician who does not belong to the medical facility. A controller decides whether to appoint the physician depending on whether a pickup location included in the received undertaking conditions is apart from a location of the medical facility by a distance equal to or greater than a threshold. Upon deciding to appoint the physician, the controller determines a pickup location included in the undertaking conditions to be a stopover point.

Examples relate to systems, method and systems, methods and computer programs for efficiently determining an order of driving destinations and for training a machine-learning model using distance-based input data. A system for determining an order of a plurality of driving destinations is configured to obtain information on a distance between the plurality of driving destinations, the distance being defined for a plurality of routes between the plurality of driving destinations, with the routes being defined separately in both directions between each combination of driving destinations of the plurality of driving destinations within the information on the distance. The system is configured to provide the information on the distance between the plurality of driving destinations as input to a machine-learning model, the machine-learning model being trained to output information on an order of the plurality of routes based on the information on the distance provided at the input of the machine-learning model. The system is configured to determine the information on the order of the plurality of driving destinations based on the information on the order of the plurality of routes.

A system and a computer-implemented method employ an appointment optimization and route planning system (AORPS) for optimizing home-visit appointments and related travel for delivering patient care. The AORPS receives registration and patient data from patients and client input including information about healthcare providers, onsite care coordinators, health plans, appointment types, and success rates from a client. The AORPS collates the patient data and generates an input matrix from the client input and the collated patient data. The AORPS generates a predictive model for appointments, capitation, and return on investment for delivering patient care based on appointment and patient history, feedback, and healthcare data. The AORPS generates an appointment schedule with travel routes dynamically based on optimization factors derived from the client input, the collated patient data, the input matrix, the healthcare data, and the predictive model, incorporating real-time changes in patient data, the client input, the optimization factors, and appointments.

A system and method of augmenting a delivery route. The method comprises receiving first location information from a first position device and receiving second location information from a second position device. The method further comprises storing the first and second location information in a memory structure and determining, based on at least one of the first and second information, a time and a location for each of a plurality of stops. The method also comprises reconciling the determined time and location of each of the plurality of stops with a stored route comprising a plurality of stored stops, the stored stops having a stored location and a stored time associated therewith and estimating a plurality of transition times between pairs of the plurality of stops. The method further also comprises constructing an updated timeline based on the plurality of stored stops, the plurality of determined stops, and the plurality of transition times and updating the delivery route based on the constructed timeline.

An information processing apparatus according to an embodiment of the present technology includes an acquisition unit, a connection unit, and a smoothing unit. The acquisition unit acquires route information, the route information including a route including a plurality of positions and a state parameter relating to a moving state of a mobile body when the mobile body moves along the route, the state parameter being associated with each of the positions of the route. The connection unit connects a first route included in acquired first route information and a second route included in acquired second route information to each other. The smoothing unit smooths a difference between a first state parameter associated with a first smoothing point on the first route and a second state parameter associated with a second smoothing point on the second route.

Provided is an information processing method executed by a computer, the information processing method including: acquiring a delivery destination of each of packages; acquiring candidates for a stop place for a mobile body that delivers the packages to stop; determining a group to which at least one delivery destination belongs based on a location of each of the delivery destinations; determining a stop place for the group based on a location of the at least one delivery destination belonging to the group and the candidates for the stop place; and outputting the stop place determined.

A navigation method and apparatus providing a navigation path for a target vehicle are disclosed. The method includes: receiving first location information of the target vehicle sent by a high-definition map module, where the first location information includes lane information of a first location at which the target vehicle is currently located; and determining a navigation path based on the first location information and a destination of the target vehicle. The lane information of the current location of the target vehicle can be provided for a navigation map module by using the high-definition map module, so as to more accurately determine a navigation path suitable for a lane in which the target vehicle is currently located, thereby improving accuracy of navigation planning.

A control apparatus includes a control section that acquires itinerary information indicating a route on which a vehicle travels, the route including a departure point, a destination, and a waypoint that enables charging of the vehicle, and a first departure time for the vehicle to depart from the departure point, and that based on the itinerary information, sets a second departure time resulting from the first departure time being modified such that a time of arrival of the vehicle at the waypoint becomes a predetermined time.