Delivery Vehicle Resource Optimization via Time Slot Segmentation
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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 resource bottlenecks, making them inefficient for handling a high volume of delivery orders.
Innovation Solution
A system that includes a computing device communicating with a vehicle server to determine available time slots, assign delivery orders to vehicles based on selected time slots and parameters, and calculate optimized delivery routes, utilizing meta-heuristic algorithms and local search methods to optimize vehicle resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the number of delivery orders increases, then the service capacity increases, but the combinatorial space complexity increases exponentially
Solution Approach 1:
The patent segments the large-scale delivery optimization problem into multiple smaller sub-problems by dividing the set of delivery orders into different groups or batches. This allows the optimization algorithm to process each subset separately, reducing the combinatorial space complexity from exponential O(2^n) to manageable polynomial levels, while still achieving near-optimal overall routing solutions.
2Quantity of substance
If the number of delivery orders increases, then the service capacity increases, but the computational time increases
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing routing information, distance matrices, and optimization parameters before the actual delivery optimization is needed. When new delivery orders arrive, the system leverages these pre-computed data structures to rapidly determine optimized routes, significantly reducing the computational time required compared to performing full optimization from scratch.
3Quantity of substance
If the number of delivery orders increases, then the service capacity increases, but the computational resource bottleneck worsens
Solution Approach 1:
The patent implements self-service by enabling the optimization system to automatically manage its own computational resources through dynamic allocation, parallel processing, and adaptive algorithm selection. The system autonomously adjusts the level of optimization detail based on order characteristics, vehicle constraints, and available computational capacity, allowing it to efficiently handle large volumes of delivery orders without external intervention and maintaining high throughput.
Data Source
AI summary
Systems and methods for optimizing delivery vehicle resources (e.g. a plurality of vehicles) are described herein. Available time slots for the plurality of vehicles are determined and presented to a user. In response to receiving a delivery order indicating a selected time slot, the delivery order is assigned to a vehicle from the plurality of vehicles based on a time slot indicated in the delivery order and a set of delivery parameters. A delivery route is calculated for each vehicle having a delivery order based on the set of delivery parameters. An optimized delivery route is calculated for each vehicle having a delivery order based on the set of delivery parameters.