Delivery Route Clustering Algorithm for Load and Time Constraints
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Solution Overview
Problem
Conventional delivery route planning systems are inefficient in handling hundreds or thousands of orders, as they fail to account for real-world constraints such as pickup and delivery times, and load capacity, especially in the context of same-day or 2-hour delivery services.
Innovation Solution
A computer-based method that clusters delivery nodes based on location and load capacity, determines centroids, computes node-cluster distance matrices, and reassigns nodes in real-time to balance loads and adhere to time constraints, using a modified clustering algorithm that prioritizes load and time window requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional delivery route planning systems are used, then the system is simple to operate, but it cannot handle real-world constraints such as pickup and delivery times, and load capacity
Solution Approach 1:
The patent segments the delivery network into clusters of nodes (delivery locations) that are grouped together based on spatial proximity and constraint compatibility. This segmentation allows the system to handle complex constraints by processing them in manageable groups rather than attempting to optimize the entire network at once, thereby achieving reliable constraint compliance without overwhelming system complexity
Solution Approach 2:
The patent performs preliminary actions by pre-processing node attributes (such as pickup and delivery time windows, load capacity requirements) before route planning begins. The system pre-organizes nodes into clusters based on these attributes, so that when actual route planning occurs, the constraints are already accounted for in the cluster structure, enabling reliable constraint compliance without adding complexity during the main planning process
2Productivity
If manual delivery route planning is used, then constraints can be observed and handled by staff, but it is a time consuming task that cannot keep up with same-day or 2-hour delivery requirements
Solution Approach 1:
The patent implements self-service by enabling the system to automatically perform route planning without human intervention. The automated clustering algorithm processes hundreds or thousands of delivery nodes independently, using computational methods to optimize routes while respecting constraints. This self-service capability dramatically increases productivity and eliminates the time-consuming manual planning process, allowing the system to meet same-day or 2-hour delivery requirements
Solution Approach 2:
The patent replaces the mechanical system of manual staff planning with an automated computational system. Instead of human staff manually observing and handling constraints, the system uses algorithms to process constraint data, cluster nodes, and generate optimized routes automatically. This substitution of mechanical manual labor with automated computation dramatically reduces planning time and increases productivity
3Reliability
If all delivery requests are treated as equal, then the system is easy to operate, but it cannot account for real-world constraints such as pickup time, delivery time, and load capacity
Solution Approach 1:
The patent applies local quality by treating different delivery nodes differently based on their specific constraints and characteristics. Rather than treating all requests equally, the system clusters nodes based on their individual attributes (pickup time windows, delivery time windows, load capacity requirements), creating localized groups that share common constraint profiles. This allows the system to satisfy diverse constraints while maintaining a unified automated processing approach, balancing reliability with ease of operation
Data Source
AI summary
A method including planning delivery routes using clustering. The method can include generating clusters for nodes for order data based, at least in part, on (a) location information of the nodes and (b) load capacity information of delivery vehicles. The method also can include determining a respective centroid for each of the clusters. Further, the method can include computing a node-cluster distance matrix. Additionally, the method can include reassigning the nodes to the clusters based, at least in part, on: (a) the node-cluster distance matrix; (b) load capacity information of dispatched vehicles of the delivery vehicles; and (c) a predetermined average load threshold of the dispatched vehicles. Moreover, the method can include further reassigning, in at least 2 passes, the nodes to the clusters based, at least in part, on the time window information of the nodes. Other embodiments are disclosed.