Binary-Tree Delivery Scheduling for Route and Time Accuracy

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Current delivery management systems face issues such as delayed deliveries, perishable goods not being delivered on time, and undelivered orders due to poor scheduling, leading to increased effort, time, and costs for retailers.

Innovation Solution

A computing device employs a machine learning model based on binary trees to optimize delivery scheduling by determining delivery time windows and routes, using historical order data to train trees that provide features and outputs for efficient delivery management.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual scheduling methods are used, then simplicity of operation is maintained, but delivery timing accuracy deteriorates

Engineering Contradiction:

Improvedelivery timing accuracyVSAvoidscheduling system complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent replaces manual mechanical scheduling with an automated machine learning system using binary trees. The system automatically processes historical order data, determines features, trains binary trees, and generates delivery schedules without manual intervention, thereby improving timing accuracy while managing complexity through automation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The scheduling system performs self-service by automatically training machine learning models using historical data and generating optimized delivery schedules without requiring external expert intervention. The binary trees autonomously learn from past patterns and apply them to new scheduling decisions.

Inventive Principle:

Principle #25Self-service

2Reliability

If traditional scheduling is used, then implementation simplicity is maintained, but delivery reliability deteriorates

Engineering Contradiction:

Improvedelivery reliabilityVSAvoidscheduling system complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system performs preliminary action by training machine learning models in advance using historical order data. The binary trees are pre-trained with past delivery patterns, weather conditions, and traffic data to predict and ensure reliable delivery timing before actual deliveries occur, improving reliability through advance preparation.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback by continuously using historical order data and actual delivery outcomes to retrain and improve the binary tree models. The system learns from past performance, adjusting its scheduling predictions to improve delivery reliability over time through iterative optimization.

Inventive Principle:

Principle #23Feedback

3Productivity

If simple scheduling methods are used, then operational ease is maintained, but productivity deteriorates

Engineering Contradiction:

Improvedelivery efficiencyVSAvoidscheduling operation ease

Core Design Contradiction:

ProductivityVSEase of operation

Solution Approach 1:

The patent replaces simple manual scheduling operations with an automated machine learning system. The binary trees automatically process multiple features including historical data, weather, and traffic conditions to generate optimized delivery routes and timing, significantly improving delivery efficiency while removing the need for complex manual scheduling operations.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

4Loss of time

If manual route planning is used, then system simplicity is maintained, but time consumption deteriorates

Engineering Contradiction:

Improvescheduling timeVSAvoidscheduling system complexity

Core Design Contradiction:

Loss of timeVSDevice complexity

Solution Approach 1:

The route planning system performs self-service by automatically determining optimal delivery routes and timing using trained binary trees. The system autonomously processes order data, evaluates multiple features, and generates schedules without requiring manual route planning time, thereby reducing scheduling time while managing complexity through automated decision-making.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS12443911B2Apparatus and methods for determining delivery routes and times based on generated machine learning models

Publication Date: 2025.10.14 WALMART APOLLO LLC

AI summary

This application relates to apparatus and methods for generating and implementing a machine learning model in electronic delivery systems to determine delivery routes and times. In some examples, a computing device generates a machine learning model comprising a plurality of indexed binary trees. Each indexed binary tree determines either a first value, or a second value, based on comparing an input to a condition value. The machine learning model can generate prediction values based on the determined values of all indexed binary trees. The machine learning model is trained with historical data. Once trained, the machine learning model's performance is evaluated. Based on the evaluation, the machine learning model may be further refined. Once the machine learning model's performance satisfies requirements, the computing device employs the machine learning model to determine vehicle delivery routes and estimated delivery times.