Dynamic Delivery Fee Control for Autonomous Fleet Congestion

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Solution Overview

Problem

Existing delivery systems face delays and inefficiencies due to fixed delivery routes, human error, and peak demand periods, leading to surface traffic congestion and inadequate inventory management for autonomous delivery fleets.

Innovation Solution

Implementing a computer-implemented method using machine learning models to dynamically manage autonomous delivery device fleets by adjusting delivery charges and optimizing delivery plans based on demand and availability, integrated with inventory management and fleet control systems.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional delivery vehicles are used with fixed routes, then delivery coverage is established, but surface traffic congestion increases and delivery delays occur during peak demand

Engineering Contradiction:

Improvedelivery efficiencyVSAvoidsurface traffic congestion

Core Design Contradiction:

ProductivityVSObject-affected harmful factors

Solution Approach 1:

The patent replaces traditional ground-based mechanical delivery vehicles with autonomous aerial delivery devices (drones), substituting one mechanical system with another that operates in a different physical domain (air space vs. road surface), thereby eliminating congestion on ground transportation routes while maintaining delivery capability

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent transitions delivery operations from two-dimensional ground-based routes to three-dimensional air space utilization, allowing delivery devices to operate above congested areas and access locations through vertical dimension, thereby avoiding surface traffic congestion entirely

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

2Productivity

If more delivery vehicles are deployed to handle peak demand, then delivery capacity increases, but traffic congestion and coordination complexity increase

Engineering Contradiction:

Improvedelivery capacityVSAvoidfleet management complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent implements dynamic routing and pricing mechanisms that automatically adjust delivery paths, timing, and resource allocation based on real-time demand conditions, allowing the system to scale capacity elastically without proportional increases in management complexity through automated decision-making algorithms

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent incorporates real-time feedback loops where the system monitors delivery demand, fleet status, and performance metrics, then automatically adjusts routing, pricing, and resource allocation to optimize capacity utilization while maintaining manageable complexity through data-driven decision-making

Inventive Principle:

Principle #23Feedback

3Productivity

If dynamic pricing is implemented to manage demand, then delivery optimization improves, but pricing model complexity increases

Engineering Contradiction:

Improvedelivery optimizationVSAvoidpricing model complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent dynamically adjusts pricing parameters (delivery fees, incentives) based on demand levels, fleet availability, and route characteristics, using automated algorithms to modify economic parameters in real-time to optimize delivery efficiency without requiring complex manual pricing negotiations

Inventive Principle:

Principle #35Parameter changes

4Reliability

If autonomous delivery devices are deployed, then human error is reduced, but system automation complexity increases

Engineering Contradiction:

Improvedelivery accuracyVSAvoidautomation system complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent implements autonomous delivery devices that perform navigation, obstacle avoidance, package delivery, and return-to-base operations independently without human intervention, with embedded sensors, processors, and control systems that enable self-directed operation, thereby eliminating human error while containing automation complexity within individual units

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS12547970B1Variable delivery fee based on congestion

Publication Date: 2026.02.10 WALGREEN CO

AI summary

Systems and methods relating to management of delivery fleets are disclosed. Such systems and methods include using functionally-aligned machine learning engines to manage aspects of fleet dispatch and routing, inventory level adjustments, and order optimization. The techniques may be applied to manage an automated fleet delivery services system controlling a fleet of autonomous delivery devices (e.g., UAVs) to deliver products from a plurality of distribution hubs to fulfil customer orders. The interconnected machine learning engines enable efficient management of various aspects of such automated fleet delivery services in real time, including optimization of fleet distribution, optimization of inventory distribution, and optimization of order distribution. Optimization of order distribution may be obtained by dynamically adjusting delivery charges for orders based upon current fleet capacity and inventory levels.