LYNCXX Waste Management
Overview
Our LYNCXX Waste Management System intelligently calculates the optimal routes for waste collection trucks by utilizing advanced filling sensor data from within the containers. This innovative solution guarantees timely emptying of waste containers, fostering a cleaner and more pleasant urban environment for residents. By optimizing routes, our system not only reduces the number of waste trucks required but also minimizes their mileage, paving the way for a more efficient and sustainable waste collection process.
Solution advantages
User-friendly interface
The LYNCXX Waste Management System features an intuitive, user-friendly interface for drivers and planners, streamlining waste collection with seamless route planning and execution.
Self-learning system
Our system is dynamic and self-learning, automatically adapting to real-time traffic conditions and accurately predicting container fill rates for optimal performance.
Futureproof
LYNCXX Waste Management is a futureproof solution, seamlessly integrating with other smart systems and utilizing IoT like for continuous improvement and adaptability.
Cost reduction
Our system optimally allocates waste trucks and minimizes mileage, resulting in substantial cost savings and enhanced operational efficiency.
Case study
Avalex
Avalex, a waste management corporation serving several Dutch municipalities near Delft, partnered with ARS T&TT to optimize waste collection and enhance their services for citizens. The goal was to route waste collection trucks in a manner that minimizes transport costs, prevents traffic congestion, and ensures monitored underground waste containers do not overflow.
To achieve this, ARS T&TT implemented the LYNCXX Waste Management Solution. By leveraging historical data and real-time fill rate information (provided by sensors in the underground waste containers), the system determines the optimal moment to empty each container. The LYNCXX platform delivers optimal planning and routing for waste collection trucks, taking into account real-time fill rate data, historical patterns, and physical restrictions such as school zones, weight, and height limitations.
