Clustering Delivery Windows Using Historical Data
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Solution Overview
Problem
Current methods for providing estimated pick-up and delivery windows in logistics are not accurate enough, leading to inefficiencies and dissatisfaction among customers who rely on these timelines for planning.
Innovation Solution
A method that identifies clusters of serviceable points based on configurable distance and travel time thresholds, uses historical data to determine an estimated delivery time, and calculates a confidence score to set an accurate delivery window, which is then adjusted based on the confidence score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to provide estimated delivery windows, then the process is simple, but the accuracy of the delivery time estimates is insufficient
Solution Approach 1:
The system segments serviceable points into clusters based on geographic proximity and historical delivery patterns. By dividing the service area into multiple clusters, the system can provide more accurate estimates for each cluster while maintaining manageable complexity through localized analysis rather than system-wide processing.
Solution Approach 2:
The system performs preliminary clustering of serviceable points and pre-calculation of historical delivery statistics before actual delivery estimation. This advance preparation creates ready-to-use cluster profiles and historical data summaries that enable rapid, accurate delivery time estimates without complex real-time calculations.
2Reliability
If delivery windows are made more precise, then customer satisfaction improves, but the complexity of determining accurate timeframes increases
Solution Approach 1:
The system uses historical delivery data as feedback to continuously refine cluster profiles and improve estimate accuracy. By analyzing past delivery performance within each cluster and updating cluster characteristics accordingly, the system achieves higher reliability over time while the feedback loop automates the refinement process, preventing complexity escalation.
Solution Approach 2:
The system dynamically adjusts delivery window parameters based on cluster-specific historical performance metrics. Instead of using fixed time windows, the system modifies window duration and timing parameters according to observed delivery patterns, traffic conditions, and service performance for each cluster, achieving higher reliability through adaptive parameter tuning.
3Measurement precision
If clusters are formed with strict thresholds, then estimation accuracy improves, but the number of serviceable points that can be clustered decreases
Solution Approach 1:
The system employs dynamic threshold adjustment where clustering criteria are not fixed but adapt based on serviceable point density, geographic distribution, and historical delivery pattern similarity. This allows the system to maintain high precision by forming tight clusters where appropriate while automatically relaxing thresholds in areas with fewer points or greater spatial dispersion, ensuring comprehensive coverage.
Solution Approach 2:
The system applies different clustering threshold criteria to different geographic regions and service areas based on local characteristics. Urban areas with high point density receive stricter clustering thresholds for greater precision, while rural or low-density areas use more lenient thresholds to ensure adequate coverage. This localized approach maintains precision where possible while preserving overall system versatility.
Data Source
AI summary
Embodiments of the present invention provide methods, systems, apparatuses, and computer program products for determining delivery or pick-up windows. In one embodiment a method is provided comprising determining whether sufficient historical information/data to determine an estimated pick-up/delivery time is received for each weekday when deliveries are made and in response to determining that the sufficient historical information/data is available for a first weekday, determining an estimated pick-up/delivery time for the first serviceable point and for the first weekday based on the sufficient historical information/data for the first serviceable point and for the first weekday. Similarly, in response to determining that the sufficient historical information/data is not available for a second weekday, determining an estimated pick-up/delivery time for the first serviceable point and for the second weekday based on the first historical information/data.