Machine-Learning Route Management for Dynamic Refuse Collection
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Solution Overview
Problem
Current logistics management systems for refuse collection and distribution are inadequate in handling unforeseen changes, traffic, weather delays, and other logistical challenges, particularly when subject to time constraints, leading to inefficiencies and increased costs.
Innovation Solution
A dynamic route management system using machine learning techniques to adjust transport unit routes in real-time, incorporating static and dynamic rerouting algorithms to optimize routes based on historical data, environmental changes, and unpredictable circumstances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static collection routes are used, then route planning is simple, but the system cannot account for unforeseen changes, traffic, weather delays, and other logistical challenges
Solution Approach 1:
The patent implements dynamic route management by transitioning from static to dynamic collection routes. The system continuously monitors real-time conditions including traffic, weather, and logistical challenges, and automatically adjusts routes accordingly. This dynamic approach enables the system to adapt to unforeseen changes while maintaining operational efficiency through automated decision-making.
Solution Approach 2:
The system incorporates feedback mechanisms where real-time data from GPS tracking, traffic conditions, and weather forecasts is continuously fed back into the route management algorithm. This feedback loop enables the system to learn from actual performance and refine future route decisions, improving adaptability without requiring overly complex manual intervention.
2Productivity
If dynamic route adjustment is implemented, then service level and efficiency improve, but computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary route optimization by pre-calculating multiple potential routes and preparing adjustment strategies in advance. Historical data and machine learning models are used to predict optimal paths before actual collection/distribution operations begin, reducing the computational burden during real-time execution while maintaining high efficiency.
Solution Approach 2:
The patent employs parameter change techniques where the route management system adjusts key parameters such as collection timing, route sequencing, and vehicle allocation based on real-time conditions. By changing these parameters dynamically rather than recalculating entire routes from scratch, the system improves productivity while controlling computational complexity.
3Reliability
If real-time monitoring and adjustment are implemented, then response to changes improves, but system cost and resource consumption increase
Solution Approach 1:
The route management system operates autonomously by self-adjusting routes based on real-time conditions without requiring constant human intervention. The machine learning models and automated algorithms handle route optimization independently, enabling reliable response to changes while minimizing the operational costs associated with manual monitoring and adjustment.
Solution Approach 2:
The system optimizes resource consumption by making targeted parameter changes only when necessary based on real-time conditions. Rather than continuously adjusting all route parameters, the system identifies and responds to significant changes in traffic, weather, or logistical challenges, adjusting routes only when needed to maintain reliability while minimizing energy and operational costs.
Data Source
AI summary
Methods and systems for the dynamic management of logistics in the collection/distribution of items, materials, and/or other distributables/collectables are disclosed that include performing a route management process that manages a plurality of routes travelled by a plurality of transport units performing refuse collection that comprises, during performance of one or more transport operations by one or more of the transport units, identifying a change in route management information and, in response to the change in the dynamic route management information, performing rerouting of at least one of the plurality of transport units.