Vehicle Rerouting Control Using Real-Time Inventory Scoring
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Solution Overview
Problem
Current inventory management systems for vehicle fleets are limited in dynamic decision-making, often relying on stale information and randomly rerouting vehicles, which is suboptimal for optimizing delivery and processing efficiency, and do not consider site-specific inventory and load conditions effectively.
Innovation Solution
A system that uses real-time data and machine learning techniques to dynamically reroute vehicles by evaluating geolocation, vehicle inventory, site inventory, and processing times, optimizing vehicle routing through a neural network model that calculates destination scores based on item availability and predicted processing times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time data and machine learning techniques are used to dynamically reroute vehicles, then delivery efficiency and fleet operability are improved, but system complexity and computational requirements increase
Solution Approach 1:
The system pre-calculates routing options and evaluates potential destinations before vehicles need rerouting. By continuously gathering real-time data on inventory levels, vehicle locations, and processing times, the system prepares multiple candidate routes in advance, enabling rapid decision-making when rerouting is needed without overwhelming computational burden at the moment of decision
Solution Approach 2:
The system creates virtual models or digital twins of the physical fleet and inventory system. These digital copies allow the machine learning algorithms to simulate and evaluate routing scenarios without affecting actual operations, reducing the complexity of real-time decision-making by testing strategies in a virtual environment first
2Productivity
If neural network models calculate destination scores based on real-time inventory and processing times, then routing optimization is improved, but computational time and processing power requirements increase
Solution Approach 1:
The system evaluates only the most relevant features and a limited set of candidate destinations rather than analyzing all possible routes. By focusing computational resources on the most promising options based on preliminary filtering criteria, the neural network can produce optimized routing decisions faster without sacrificing overall solution quality
Solution Approach 2:
The system pre-processes and filters inventory data, vehicle information, and processing time estimates before they reach the neural network. By organizing and pre-calculating key metrics in advance, the system reduces the dimensionality of the problem the neural network must solve in real-time, thereby reducing computational time
3Loss of time
If the system continuously monitors and updates vehicle routing based on latest information, then delivery time is reduced, but energy consumption and computational load increase
Solution Approach 1:
The system implements periodic or event-triggered routing updates rather than continuous real-time optimization. Routing decisions are recalculated at scheduled intervals or when specific thresholds are met (e.g., inventory changes beyond a certain level, vehicle reaching specific checkpoints), reducing the frequency of computational operations and associated energy consumption while still maintaining timely deliveries
Solution Approach 2:
The system monitors all vehicle parameters continuously but only triggers rerouting decisions when critical changes are detected. By selectively activating the full optimization process only when necessary rather than continuously, the system reduces energy consumption while maintaining the ability to respond to urgent situations that could impact delivery time
Data Source
AI summary
Devices and techniques are generally described for dynamic vehicle control. In some examples, first instructions may be sent to an electronic control unit (ECU) of the vehicle effective to route the vehicle to a first destination. In some examples, a determination may be made, for a first item carried by the vehicle while the vehicle is in route to the first destination, a first number of units of the first item present at a second destination. In some examples, a first score may be determined for the second destination based at least in part on the first number of units. The second destination may be selected for re-routing the vehicle based at least in part on the first score. Second instructions effective to re-route the vehicle from the first destination to the second destination. The second instructions may be sent to the ECU of the vehicle.