On-Board Server Route Planning for Service Vehicles
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Solution Overview
Problem
Current navigation systems for optimizing vehicle routes are inadequate for determining the optimum order of traveling to multiple destinations and fail to account for dynamic changes in traffic and customer availability, leading to increased costs and inefficient last mile delivery.
Innovation Solution
An on-board server in the vehicle uses a combination of static and dynamic information, including real-time traffic and customer presence data, to execute an Optimum Route Planning Algorithm, such as Ant Colony Optimization or Dijkstra, to determine and dynamically adjust the most efficient route, reducing the need for extensive wireless data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If navigation systems use static information only for route planning, then the system complexity is reduced, but the route optimization accuracy deteriorates due to inability to account for dynamic traffic conditions
Solution Approach 1:
The system pre-calculates multiple possible routes using static information before dynamic conditions change, storing them for quick retrieval and comparison when real-time data becomes available, thus avoiding complex real-time calculations
Solution Approach 2:
The route planning system transitions from static to dynamic operation by integrating real-time traffic data, allowing the previously static pre-calculated routes to be dynamically selected and adjusted based on current conditions, achieving both computational efficiency and optimization accuracy
2Manufacturing precision
If the system transmits extensive route data wirelessly from control center to vehicle, then the route planning accuracy is improved, but the wireless network congestion increases and service fees rise
Solution Approach 1:
The system extracts and transmits only the essential dynamic parameters (traffic conditions, customer presence) needed for route optimization rather than complete route data, allowing the vehicle's onboard system to generate full routes locally, thus reducing wireless data transmission volume while maintaining planning accuracy
Solution Approach 2:
The control center acts as an intermediary that provides condensed dynamic information rather than complete route solutions, enabling the vehicle's onboard computer to perform final route determination, thus distributing computational load and reducing network traffic
3Speed
If the navigation system calculates routes between only two locations, then the calculation speed is improved, but the ability to optimize multi-destination delivery routes deteriorates
Solution Approach 1:
The multi-destination delivery problem is segmented into multiple two-point route calculations, where the system sequentially optimizes routes between consecutive delivery locations while considering overall delivery sequence, thus maintaining calculation speed while achieving multi-destination optimization
Solution Approach 2:
The system transitions from optimizing only spatial routes to optimizing the temporal sequence of deliveries as well, adding the dimension of delivery order optimization to the traditional route calculation, enabling comprehensive multi-destination route planning while maintaining computational efficiency
4Reliability
If more carriers are employed to meet delivery time requirements, then the delivery service reliability is improved, but the operational cost increases
Solution Approach 1:
The system dynamically adjusts delivery routes and sequences in real-time based on traffic conditions and customer availability, enabling carriers to maintain reliable delivery schedules with optimized routing that reduces total delivery time and allows fewer carriers to handle the same workload
Solution Approach 2:
The system changes the optimization parameters from fixed route patterns to dynamic, condition-based routing, allowing carriers to adapt to real-time conditions and complete deliveries more efficiently, thus maintaining service reliability with reduced carrier requirements
Data Source
AI summary
Transportation costs are minimized with a novel system for optimizing a route for a service vehicle, for example, a package delivery van. Based upon static and dynamic information, a computer on board the service vehicle determines an optimum route for the vehicle to travel between multiple locations. The computer on board the service vehicle communicates wirelessly to receive any pertinent dynamic information which has changed (e.g., traffic congestion, the presence of a customer at a deliver location, or a modification of location). The computer then recalculates the optimum route for the vehicle and guides the driver of the vehicle in accordance with the newly determined optimum route. The on board computer continues to check for any updates, and recalculates the optimum route throughout the day. Thus, the optimum route may be selected based upon the latest available information white avoiding heavy signal traffic on the wireless network.