Vehicle Route Overlap Optimization via Node Projection
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Solution Overview
Problem
Current vehicle routing solutions inadequately address the optimization of route overlap, which is a critical aspect of modern supply chain optimization, and fail to effectively combine additional objectives with conventional constraints, leading to increased complexity and difficulty in finding feasible solutions.
Innovation Solution
A computer-based system and method that optimizes vehicle route overlap by using a UI/API to input parameters such as vehicle capacity, shift limits, tasks, and optimization objectives, and employs a route generator to determine feasible solutions, followed by an overlap optimizer that calculates and reduces route overlap metrics by reallocating destination nodes onto a line passing through route centroids, ensuring capacity and timing parameters are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional vehicle routing solutions focus on minimizing total distance traveled, then the primary routing objective is achieved, but route overlap is not adequately reduced
Solution Approach 1:
The patent segments the routing optimization process into distinct phases: initial route generation focusing on distance minimization, followed by a separate overlap optimization phase that processes the generated routes to reduce overlaps. This segmentation allows each phase to optimize for its specific objective without compromising the other.
Solution Approach 2:
The system performs preliminary route generation and optimization for distance before addressing route overlap. By establishing efficient routes first and then applying overlap reduction techniques, the system ensures that the foundation of routing efficiency is maintained while subsequently reducing energy losses from overlaps.
2Adaptability or versatility
If additional objectives such as reducing route overlap are combined with conventional routing objectives, then more comprehensive optimization is achieved, but problem complexity increases
Solution Approach 1:
The patent divides the multi-objective optimization problem into separate sequential tasks: first optimizing for distance, then optimizing for overlap reduction. This segmentation transforms a complex simultaneous multi-objective problem into manageable sequential steps, reducing computational complexity while achieving comprehensive optimization.
Solution Approach 2:
The system establishes feasible routes based on conventional objectives first, then applies overlap reduction as a subsequent refinement step. This preliminary action approach allows the system to handle multiple objectives by addressing them in a structured sequence rather than simultaneously, managing complexity effectively.
3Manufacturing precision
If exact minimum routing solutions are sought for NP-complete vehicle routing problems, then optimal solutions are achieved, but computational time exceeds commercial time-frames
Solution Approach 1:
The patent applies partial optimization by focusing on the most significant improvement opportunities: first achieving distance minimization, then applying overlap reduction to the most problematic route pairs. This selective approach delivers substantial optimization benefits without requiring exhaustive computation of all possible routes.
Solution Approach 2:
The system replaces exhaustive exact optimization methods with heuristic and approximation algorithms that provide near-optimal solutions within commercial time-frames. By substituting computationally intensive exact methods with efficient approximation techniques, the system achieves practical optimality without excessive computational time.
4Productivity
If route overlap is reduced by reallocating destination nodes, then route efficiency is improved, but capacity and timing constraints may be violated
Solution Approach 1:
The patent incorporates feedback mechanisms where the overlap optimization process continuously monitors capacity and timing constraints. When reallocation of destination nodes threatens to violate constraints, the system adjusts the optimization to maintain feasibility, ensuring that efficiency improvements do not compromise constraint satisfaction.
Solution Approach 2:
The system applies preliminary checks and adjustments to prevent constraint violations before they occur. By anticipating potential capacity and timing issues during the overlap reduction process, the system proactively modifies reallocation strategies to maintain feasibility while still achieving efficiency improvements.
Data Source
AI summary
Apparatuses, methods, and systems for optimizing vehicle route overlap in vehicle routing plans are disclosed. A method comprises selecting a first vehicle route and a second vehicle route of a plurality of vehicle routes using a plurality of overlap metrics, reallocating destination nodes of the first vehicle route and the second vehicle route to a first new route and a second new route using an ordered projection of the destination nodes onto a line passing through the first vehicle route and the second vehicle route, and determining an overlap-optimized vehicle routing plan including the first new route and the second new route.