Abstract: A method can include training a machine learning module based on training data to determine a load threshold for to-be-dispatched delivery vehicles, wherein the training data is based on historical load thresholds and historical delivery performance data of delivery vehicles. The method further can include generating one or more clusters for nodes based, at least in part, on (a) location information of the nodes from order data and (b) load capacity information of the delivery vehicles. Also, the method can include reassigning the nodes to the one or more clusters. Additionally, the method can include transmitting the one or more delivery routes, as determined and re-determined, to be displayed on one or more user devices. Moreover, the method can include re-training the machine learning module. Other embodiments are disclosed.

Abstract: A method including setting a preferred delivery route as an initial delivery route comprising original delivery stops.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform: receiving historical driver search information corresponding to a first offer publish time criterion, the first offer publish criterion including a driver lag time; building a machine learning model based on the driver search information to determine a first metric and a second metric; analyzing the first metric and the second metric with an optimization model to determine a second offer publish time criterion that reduces the driver lag time; receiving an order for a delivery for an item, the order including a delivery time window; transmitting the order to a driver search platform subject to the second offer publish time criterion to reduce the driver lag time and mitigate delivery outside of the delivery time window. Other embodiments are disclosed herein.

Abstract: 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.

Abstract: A method to be implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media. The method can include determining, via a source-departure-prediction machine learning model, a source departure time for a delivery route to one or more stops based at least in part on a load ready time. The source-departure-prediction machine learning model can include a first model and a second model. Determining the source departure time further can include using the first model to determine the source departure time, while not using the second model, when a commodity type of the delivery route is of a first type; and using the second model to determine the source departure time, while not using the first model, when the commodity type of the delivery route is not of the first type. The method additionally can include determining a respective transit time for each of one or more legs for the delivery route.

Abstract: A method including setting a preferred delivery route as an initial delivery route comprising original delivery stops.

Abstract: 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.

Abstract: A method including sorting multiple package delivery requests in a descending order based on a respective degree of limitation for a first respective constraint for each of the multiple package delivery requests. In some embodiments, each of the multiple package delivery requests comprises the first respective constraint and a respective destination. In many embodiments, the method further can include generating one or more delivery routes originated from a depot based, at least in part, on the multiple package delivery requests, as sorted, and a respective destination distance for each pair of the multiple package delivery requests. In some embodiments, the method additionally can include generating one or more driving directions based on the one or more delivery routes. Other embodiments are disclosed.

Abstract: 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.

Abstract: A method including planning delivery routes using clustering. The method can include extracting (a) location information of nodes from order data, (b) time window information of the nodes from the order data, and (c) load capacity information of delivery vehicles. The method further can include generating, in real-time, one or more clusters for the nodes based, at least in part, on (a) the location information of the nodes and (b) the load capacity information. Each of the one or more clusters can be associated with a respective vehicle of one or more dispatched vehicles of the delivery vehicles. The method also can include determining, in real-time, a respective centroid for each of the one or more clusters based on location information of cluster nodes of the nodes for each of the one or more clusters. Further, the method can include computing, in real-time, a node-cluster distance matrix.

Abstract: Systems and methods for allocating inventory between a first level distribution center and multiple second level distribution centers.

Abstract: Systems and methods for providing reserve ratio of a product in a first level distribution center. The method includes: providing, by a computing device, demand forecasts in a first predetermined time, inventory and sale loss data of the product in the first and second level distribution centers; defining an object function having components of first level cost due to out of stock of the product at the first level distribution center, second level cost due to out of stock of the product at the second level distribution center, and delivery cost due to out of stock of the product at the second level distribution center and delivery of the product from the first level distribution center, using the demand forecasts, inventory and sale loss data of the product; and minimizing the object function to obtain the reserve ratio of the product in the first level distribution center.