Walmart Apollo, LLC

Chenxin Ma

Mingang Fu

A method including setting a preferred delivery route as an initial delivery route comprising original delivery stops. The method further can include evaluating, in real-time, the preferred delivery route, comprising: (a) determining a to-be-resequenced delivery stop list by moving a random quantity of delivery stops from the original delivery stops to the to-be-resequenced delivery stop list based on a delivery-stop-selecting act, wherein: after the to-be-resequenced delivery stop list is determined, a modified preferred delivery route comprises remaining delivery stops from the original delivery stops; (b) after determining the to-be-resequenced delivery stop list, creating, in real-time, a resequenced delivery route by adding each delivery stop of the random quantity of delivery stops of the to-be-resequenced delivery stop list to the modified preferred delivery route; (c) determining, in real-time, whether the resequenced delivery route is preferred over the preferred delivery route; and (d) when the resequenced delivery route is determined to be preferred over the preferred delivery route, resetting, in real-time, the preferred delivery route to be the resequenced delivery route. The method further can include transmitting, in real-time via a computer network, the preferred delivery route to a driver mobile device of a delivery driver for the delivery driver to follow the preferred delivery route to make deliveries. Other embodiments are disclosed.

Automatically planning delivery route

A method including planning delivery routes using clustering. The method can include generating clusters for nodes for order data based, at least in part, on (a) location information of the nodes and (b) load capacity information of delivery vehicles. The method also can include determining a respective centroid for each of the clusters. Further, the method can include computing a node-cluster distance matrix. Additionally, the method can include reassigning the nodes to the clusters based, at least in part, on: (a) the node-cluster distance matrix; (b) load capacity information of dispatched vehicles of the delivery vehicles; and (c) a predetermined average load threshold of the dispatched vehicles. Moreover, the method can include further reassigning, in at least 2 passes, the nodes to the clusters based, at least in part, on the time window information of the nodes. Other embodiments are disclosed.

Automatically planning delivery routes using clustering

A method including automatically planning delivery routes. The method can include extracting an initial delivery route from the delivery data. The initial delivery route can comprises delivery stops. The method also can include setting a preferred delivery route as the initial delivery route. The method also can include evaluating, in real-time, the preferred delivery route. The method also can include repeating evaluating, in real-time, the preferred delivery route until a predetermined stop condition occurs. The method further can include, after repeating evaluating, in real-time, the preferred delivery route, transmitting, in real-time via a network, the preferred delivery route to a driver mobile device of a delivery driver for the delivery driver to follow the preferred delivery route to make deliveries. Other embodiments are disclosed.

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Belmont, CA, United States of America

Chenxin Ma