Delivery Route Assignment Using Time Slots for Large Order Volumes
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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, resulting in inefficient delivery route optimization and prolonged computation times.
Innovation Solution
A system that includes a computing device communicating with vehicle servers to receive and analyze time data, determine available time slots, and assign delivery orders to vehicles based on selected time slots and parameters, optimizing delivery routes using machine learning processes to improve resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the number of delivery orders increases, then the delivery service capacity increases, but the combinatorial complexity increases exponentially
Solution Approach 1:
The patent segments the delivery optimization problem into two distinct phases: (1) route determination phase that identifies candidate routes without full optimization, and (2) route optimization phase that refines selected routes. This segmentation allows the system to handle large numbers of orders by processing them in manageable stages rather than attempting complete combinatorial optimization of all orders simultaneously.
Solution Approach 2:
The system performs preliminary route determination before final optimization. In the route determination phase, candidate routes are identified based on basic constraints without exhaustive search. This preliminary action reduces the combinatorial space before the optimization phase begins, making the overall process scalable to large order volumes.
2Quantity of substance
If the number of delivery orders increases, then the delivery service capacity increases, but the computational resource bottleneck increases
Solution Approach 1:
The computational process is divided into two phases with different resource requirements. The route determination phase uses lighter computational resources to generate candidate routes, while the optimization phase focuses resources on refining fewer selected routes. This segmentation prevents computational resource bottlenecks even as order volume increases.
Solution Approach 2:
The system performs partial optimization by determining routes without complete optimization in the first phase. This partial action approach generates sufficient candidate routes using minimal computational resources, then applies full optimization only to selected routes, maintaining resource efficiency at scale.
3Manufacturing precision
If the optimization time increases, then the route optimization quality improves, but the system responsiveness deteriorates
Solution Approach 1:
The optimization process is segmented into route determination (quick, lower precision) and route optimization (slower, higher precision) phases. This segmentation allows the system to quickly generate candidate routes and then invest more time only in optimizing the selected routes, balancing responsiveness and optimization quality.
Solution Approach 2:
The route determination phase performs preliminary routing without full optimization, providing quick candidate solutions. This preliminary action establishes a baseline route structure rapidly, allowing the system to respond quickly to new orders while still enabling subsequent detailed optimization when time permits.
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.