AUTOMATICALLY PLANNING DELIVERY ROUTES USING CLUSTERING

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AUTOMATICALLY PLANNING DELIVERY ROUTES USING CLUSTERING - A simplified explanation of the abstract

This abstract first appeared for US patent application 18745724 titled 'AUTOMATICALLY PLANNING DELIVERY ROUTES USING CLUSTERING

Simplified Explanation: This patent application describes a method for training a machine learning module to determine load thresholds for delivery vehicles based on historical data, generating clusters for nodes, reassigning nodes to clusters, transmitting delivery routes to user devices, and re-training the machine learning module.

The method trains a machine learning module using historical load thresholds and delivery performance data.
Clusters are generated for nodes based on location information and load capacity of delivery vehicles.
Nodes are reassigned to clusters for optimized delivery routes.
Delivery routes are transmitted to user devices for display.
The machine learning module is re-trained to improve accuracy.

Potential Applications: This technology can be applied in logistics and transportation industries to optimize delivery routes, improve efficiency, and enhance overall performance of delivery vehicles.

Problems Solved: This technology addresses the challenges of determining load thresholds, optimizing delivery routes, and improving delivery performance in a dynamic environment.

Benefits: The benefits of this technology include increased efficiency, reduced delivery times, cost savings, and improved customer satisfaction.

Commercial Applications: Title: "Optimized Delivery Route Management System" This technology can be commercially used by logistics companies, e-commerce platforms, and transportation services to streamline operations, reduce costs, and enhance customer experience. The market implications include improved competitiveness, increased market share, and enhanced brand reputation.

Prior Art: Further research can be conducted in the field of machine learning algorithms for route optimization and load threshold determination in logistics and transportation.

Frequently Updated Research: Stay updated on advancements in machine learning algorithms, route optimization techniques, and data analytics in the logistics industry.

Questions about Optimized Delivery Route Management System 1. How does this technology improve delivery performance and efficiency? - This technology improves delivery performance and efficiency by using historical data to train machine learning modules, generating optimized delivery routes, and re-training the modules for continuous improvement. 2. What are the potential cost savings for companies implementing this technology? - Companies implementing this technology can experience cost savings through reduced fuel consumption, optimized vehicle usage, and improved delivery timeframes.

Original Abstract Submitted

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.