Logistics Method Clustering Delivery Items by Recipient Location
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Solution Overview
Problem
Traditional goods delivery methods, particularly in urban areas, face inefficiencies due to high traffic and limited parking, leading to increased costs, pollution, and longer delivery times, especially for time-sensitive items like food, and struggle to meet consumer demands for rapid and cost-effective delivery.
Innovation Solution
A computer-implemented logistics method that clusters delivery items by recipient location into parent and child clusters, optimizing routes and schedules for parent and delivery agents, allowing for efficient navigation and reduced road usage through the use of various transport means, including drones and self-driving vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional motor vehicles (vans) are used for delivery in urban areas, then delivery capacity and range are improved, but road usage and traffic congestion increase
Solution Approach 1:
The delivery system is segmented into multiple levels: distribution centers at regional level, hubs at local level, and various delivery agents (foot, bike, vehicle) at neighborhood level. This segmentation allows large-scale delivery operations to be broken down into smaller, more efficient units that can operate independently, reducing the need for large vans to navigate through congested urban streets while maintaining overall delivery capacity.
Solution Approach 2:
Hubs serve as intermediary points between distribution centers and final delivery locations. Parcels are transferred from vans at distribution centers to hubs, where they are then distributed by smaller agents. This intermediary system eliminates the need for delivery vans to travel deep into urban areas, reducing road usage and traffic congestion while maintaining delivery efficiency.
2Ease of operation
If delivery vehicles park frequently in high-traffic areas, then delivery access to recipients is improved, but delivery time and efficiency deteriorate
Solution Approach 1:
The system performs preliminary clustering of delivery locations into zones and pre-plans optimal routes and stop sequences before delivery begins. Delivery agents receive pre-calculated itineraries that minimize parking and retrieval time, allowing them to execute deliveries efficiently without spontaneous decision-making in high-traffic areas.
Solution Approach 2:
The route optimization system dynamically adjusts delivery sequences and hub selection based on real-time conditions such as traffic patterns, weather, and agent availability. This dynamic adaptation allows the system to respond to changing urban conditions while maintaining optimal delivery efficiency and minimizing time loss.
3Productivity
If more delivery vehicles are deployed to meet increasing consumer demand, then delivery coverage and capacity are improved, but pollution and street crowding increase
Solution Approach 1:
The hub system serves multiple functions: it acts as a transfer point for parcels, a distribution center for local agents, a parking area for vehicles, and a coordination point for route optimization. This multi-functionality allows the system to handle increased delivery volumes without proportionally increasing vehicle numbers, as hubs can accommodate multiple agents and parcels simultaneously.
Solution Approach 2:
The system replaces the mechanical system of direct van-to-door delivery with a hybrid system that combines centralized distribution (at hubs) with decentralized delivery by various agents including foot delivery and bicycles. This substitution reduces reliance on motor vehicles for the final delivery leg, thereby reducing pollution and street crowding while maintaining delivery coverage.
4Ease of operation
If drivers follow preferred routes through delivery areas, then route familiarity and operational simplicity are improved, but delivery efficiency and time consumption deteriorate
Solution Approach 1:
The system continuously collects data on delivery performance, traffic conditions, and agent behavior, then uses this feedback to optimize routes and hub locations. The feedback loop allows the system to learn from actual delivery patterns and adjust routes to balance driver familiarity with optimal efficiency, rather than relying solely on predetermined preferred routes.
Data Source
AI summary
A computer-implemented logistics method of arranging delivering of items to recipients situated at different recipient locations, comprising: recording items received at a distribution centre, clustering the records according to location of the recipient, locating a hub position for each cluster, further clustering the records, creating an individual schedule for each agent with events and locations and timings for the events, and instructing the agent to deliver the items by providing individual schedule information.