Delivery Route Optimization via Snapshot Partitioning
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Solution Overview
Problem
Current resource optimization systems, such as ROVR, struggle to scale with large order sizes, leading to exponential complexity and computational bottlenecks, resulting in inefficient delivery route optimization and increased processing time.
Innovation Solution
A system comprising schedulers and optimizers that manage delivery requests, generate interim snapshots, and apply optimization processes to determine available time slots and optimize delivery routes for multiple vehicles, utilizing meta-heuristic algorithms and local search methods to minimize costs and ensure timely deliveries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of delivery orders is increased, then the delivery service capacity is improved, but the combinatorial space complexity increases exponentially
Solution Approach 1:
The patent divides the large-scale delivery optimization problem into multiple smaller sub-problems by partitioning delivery orders into different groups or time slots. Each sub-problem is solved independently using optimization algorithms, avoiding the need to explore the entire exponential combinatorial space at once. This segmentation allows the system to handle thousands of orders by breaking them into manageable chunks that can be optimized separately and then combined.
2Productivity
If the number of delivery orders is increased, then the delivery service capacity is improved, but the computational resource bottleneck increases
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing delivery snapshots that capture the state of delivery routes at specific time points. These snapshots are generated in advance and can be quickly retrieved and modified when new orders arrive, rather than performing complete re-optimization from scratch. This preliminary preparation significantly reduces the computational resources needed during peak ordering periods.
Solution Approach 2:
The optimization system operates periodically by generating delivery snapshots at regular intervals or triggered by specific events (such as a threshold number of orders). Between these periodic optimization cycles, the system uses the existing snapshot data to handle individual order requests efficiently, performing only incremental updates rather than full combinatorial optimization. This periodic operation pattern reduces overall computational resource demand while maintaining service capacity.
3Manufacturing precision
If the optimization time is increased, then the route optimization quality is improved, but the system responsiveness deteriorates
Solution Approach 1:
The system applies partial optimization by focusing computational efforts on the most critical aspects of route optimization rather than exhaustively exploring all possible route combinations. The delivery snapshots capture essential optimization results without requiring complete enumeration of all alternatives. This partial action approach achieves sufficiently high route optimization quality while maintaining fast processing times suitable for real-time delivery systems.
Data Source
AI summary
Horizontally-scalable systems and methods for scheduling and optimizing deliveries are described herein. At least one scheduler is configured to receive a request to schedule a delivery for an origination location. The request includes a desired time slot. The request is compared to a persistent delivery snapshot for the origination location to determine availability of the desired time slot. An interim delivery snapshot including the requested delivery is generated when the persistent delivery snapshot has an available time slot corresponding to the desired time slot. At least one optimizer is configured to receive the interim delivery snapshot and generate an updated persistent delivery snapshot by applying an optimization process to the interim delivery snapshot.