Dynamic Route Optimization for Package Delivery
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Solution Overview
Problem
Conventional package delivery planning is inefficient, often relying on pre-scheduled routes that do not adapt to changing delivery loads, leading to underutilization of resources and missed opportunities for optimized delivery options due to limited access to available package delivery information and coordination challenges between package receivers and delivery entities.
Innovation Solution
An intelligent package delivery system that obtains and analyzes order information to determine optimized routes by clustering delivery stops using geofences and time windows, integrating with external data sources for real-time optimization, and utilizing autonomous vehicles for automatic execution, while also predicting and confirming package delivery orders with promotional incentives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If pre-scheduled package delivery routes are used, then delivery planning is simple and straightforward, but resource utilization is poor and delivery efficiency decreases when delivery load varies
Solution Approach 1:
The patent implements dynamic route optimization by continuously adjusting delivery routes based on real-time package delivery load and order information. The system transitions from static pre-scheduled routes to dynamic adaptive routing that responds to changing delivery requirements, thereby maintaining both planning simplicity and high delivery efficiency under varying load conditions.
Solution Approach 2:
The system incorporates feedback mechanisms by monitoring package delivery load, vehicle location, and order information in real-time. This feedback enables the route optimization engine to continuously refine delivery routes, ensuring that resources are efficiently allocated while maintaining operational simplicity through automated adjustments.
2Ease of operation
If package delivery planning is done for a single package order independently, then each order can be processed individually, but overall delivery efficiency is reduced due to lack of coordination between orders
Solution Approach 1:
The patent merges multiple independent package delivery orders into a coordinated fleet-wide optimization problem. The system consolidates order information from multiple orders and uses a unified route optimization engine to determine optimal routes across the entire fleet, thereby improving overall delivery efficiency while still processing individual order requirements.
Solution Approach 2:
The route optimization system serves multiple orders simultaneously through a universal optimization framework. The system handles diverse order types and delivery requirements through a single multi-functional platform that coordinates vehicles across the entire fleet, achieving improved productivity without sacrificing individual order processing capabilities.
3Productivity
If increased package delivery load occurs, then more delivery opportunities are available, but existing resources are insufficient to handle the increased demand
Solution Approach 1:
The system dynamically scales resource utilization by optimizing routes in real-time based on delivery load. When delivery load increases, the system maximizes the utilization of existing vehicles through intelligent route consolidation and coordination, effectively increasing delivery capacity without requiring proportional increases in physical resources.
Solution Approach 2:
The optimization system changes operational parameters such as route sequences, vehicle assignments, and delivery windows to accommodate increased delivery loads. By adjusting these parameters dynamically, the system can handle higher productivity demands using the same physical resources, effectively scaling capacity through intelligent parameter optimization.
4Loss of energy
If decreased package delivery load occurs, then fewer deliveries are needed, but vehicles still travel to unnecessary locations
Solution Approach 1:
The system extracts and removes unnecessary delivery stops from vehicle routes when delivery load decreases. The optimization engine analyzes order information and eliminates locations that are no longer necessary to visit, thereby reducing fuel consumption and energy waste while maintaining appropriate vehicle utilization for actual delivery requirements.
Solution Approach 2:
The system applies partial routing actions by visiting only the necessary subset of locations rather than following complete pre-scheduled routes. When delivery load is low, the optimization engine determines the minimal set of stops required, avoiding excessive travel to unnecessary locations and reducing energy loss while maintaining adequate productivity.
Data Source
AI summary
Methods, computer program products, and systems are presented. The methods computer program products, and systems can include, for instance: obtaining order information for a plurality of package delivery orders, wherein the order information includes one or more package pickup stop and one or more package drop off stop; and determining one or more optimized package delivery route for one more vehicle using the order information, wherein an optimized package delivery route of the one or more optimized package delivery route includes a first stop associated to a first order of the plurality of package delivery orders and a second stop associated to a second order of the plurality of package delivery orders.