Genetic Optimization for Delivery Route Planning
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Solution Overview
Problem
Inefficient delivery routes continue to add unnecessary costs to delivery businesses due to suboptimal route planning and optimization methods.
Innovation Solution
A route determination system that converts customer addresses to geocoordinates, applies convex hull analysis, and uses genetic optimization to identify and plot the most efficient delivery routes, incorporating features like stop insertion, mutation, and crossover to refine candidate routes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional route planning methods are used, then route creation is simple, but delivery efficiency is low and travel distance is excessive
Solution Approach 1:
The patent replaces traditional manual or simple algorithmic route planning with a genetic algorithm-based optimization system. The system uses computational intelligence (genetic algorithms with selection, crossover, and mutation operations) to automatically optimize delivery routes, substituting mechanical/manual planning with intelligent computational systems that can evaluate multiple route combinations and identify optimal paths.
Solution Approach 2:
The system optimizes routes by dynamically adjusting route parameters such as stop sequence, path selection, and delivery order. The genetic algorithm modifies route parameters across generations, selecting and refining route configurations to minimize total travel distance and time while ensuring all deliveries are completed.
2Manufacturing precision
If traditional route planning methods are used, then computational resources are saved, but route optimization is insufficient
Solution Approach 1:
The patent segments the route optimization problem into discrete genetic algorithm operations: initialization of candidate routes, evaluation of route fitness, selection of parent routes, crossover to generate new routes, and mutation to introduce variations. This segmentation allows the complex optimization problem to be solved through manageable, iterative computational steps.
Solution Approach 2:
The system employs dynamic optimization where route configurations evolve over multiple generations. The genetic algorithm continuously adapts route solutions based on fitness evaluation, allowing the system to dynamically adjust to find optimal routes rather than relying on static or predetermined paths.
Data Source
AI summary
Disclosed are various embodiments for determining and plotting delivery routes in a computing device. A set of solutions to the traveling salesman problem may be determined by applying a convex hull to determine a set of initial solutions. Computational complexity may be reduced by decreasing the dimensions of the initial solutions. The set of initial solutions may be further optimized by applying genetic optimization to determine the most efficient solutions.