Vehicle Route Selection Using Group Classifiers and Solving Engines
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Solution Overview
Problem
Existing delivery systems face challenges in optimizing delivery routes and reducing costs as the number of delivery orders increases, particularly in the delivery of grocery items, leading to inefficiencies in route assignments.
Innovation Solution
A system and method utilizing one or more processors to receive input information, process it using group classifiers, analyze it with solving engines, and select optimal vehicle routes that satisfy a threshold, then transmit these routes to dispatchers for vehicle coordination, employing tools like Adaptive Large Neighborhood Search (ALNS) solvers and ORTools solvers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of delivery orders increases, then delivery service capacity increases, but route determination complexity and delivery costs increase
Solution Approach 1:
The patent segments the delivery system into multiple independent components: order management module, vehicle management module, route determination module, and cost calculation module. Each module handles specific aspects of delivery operations independently, allowing the system to scale capacity without proportionally increasing overall complexity. The route determination is further segmented by considering different factors (distance, time, cost) as separate evaluation criteria.
Solution Approach 2:
The system implements dynamic route determination that adapts to changing conditions. The route optimization algorithm dynamically adjusts routing decisions based on real-time inputs including order characteristics, vehicle availability, traffic conditions, and cost parameters. This dynamic approach allows the system to handle increasing order volumes efficiently without requiring static, overly complex pre-planning for all scenarios.
2Productivity
If more delivery orders are processed, then service coverage improves, but delivery costs increase
Solution Approach 1:
The patent merges multiple delivery orders into consolidated routes whenever possible. The system evaluates orders for consolidation based on delivery locations, time windows, and vehicle capacity. By combining multiple orders into single vehicle routes, the system achieves economies of scale, reducing per-order delivery costs while expanding service coverage. The cost calculation module specifically accounts for these consolidation benefits.
Solution Approach 2:
The system changes key parameters dynamically to optimize the balance between service coverage and delivery costs. The route determination module adjusts parameters such as route capacity utilization, vehicle selection criteria, and delivery time windows based on current order characteristics and cost constraints. This allows the system to expand coverage selectively in cost-effective areas while maintaining profitability.
3Ease of operation
If traditional route assignment methods are used, then system simplicity is maintained, but delivery efficiency decreases
Solution Approach 1:
The system implements self-service through automated route determination and optimization algorithms. The computer system automatically receives order information, determines optimal routes, calculates costs, and generates delivery assignments without requiring manual intervention. This automation maintains operational simplicity for users while dramatically improving delivery efficiency through sophisticated optimization that would be impossible to achieve manually.
Solution Approach 2:
The patent replaces traditional mechanical/manual route planning methods with computer-based automated determination systems. The system uses algorithms to process order data, evaluate routing options, and generate optimized routes automatically. This substitution eliminates the need for manual route planning while maintaining ease of operation through automated decision-making, thereby resolving the contradiction between simplicity and efficiency.
Data Source
AI summary
Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform: receiving input information for generating one or more routes for one or more vehicles, the input information including one or more group classifiers; processing the input information based on the one or more group classifiers; analyzing, using one or more solving engines, the input information to generate the one or more routes for the one or more vehicles; selecting, from each of the one or more solving engines, a vehicle route from the one or more routes that satisfies a threshold; and transmitting the vehicle route to a dispatcher to facilitate coordinating, by the dispatcher, operation of a vehicle from the one or more vehicles along the vehicle route. Other embodiments are disclosed herein.