Public Sector

Smart Waste Collection with AI-Empowered Planning

SDSC Team:
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Context

Urban waste management faces inefficiencies from fixed collection schedules, often requiring unnecessary trips to half-full containers. Our collaboration focused on creating an AI-empowered routing system to help truck drivers dynamically adapt to real-time conditions, improving operational efficiency in the city of Burgdorf’s waste collection procedures.

Objectives

Working with the Swiss Data Science Center (SDSC) under an InnoSuisse mandate, the City of Burgdorf envisioned to gain efficiencies and resource-savings in their waste collection. The aim of the collaboration was to replace static schedules with adaptive algorithms that optimize waste collection based on container fill patterns, vehicle capacity, and other constraints. The goals included reducing distances, minimizing CO2 emissions, and enabling quick, reliable routes accessible to drivers with one click.

Solution

Based on existing schedule data, the team developed adaptive AI algorithms to address routing challenges. Adopting the Vehicle Routing Problem (VRP) to prioritizing full containers and incorporating partially filled ones emerged as a critical solution to increase efficiency. Using deep reinforcement learning (RL) further helped to minimize travel distances, container overfilling, and truck overloading. These innovative techniques produced optimal routes in under five seconds, ensuring real-time adaptability.

Simulations included diverse scenarios from Burgdorf, and for reasons of comparison were expanded to Emmental district, and Zurich City later in the project. Parameters like vehicle types, depot locations, and regional constraints were additionally integrated. As a result, the adaptive algorithms clearly outperformed static models, reducing collection distances  by 24% in Burgdorf and up to 38% in Emmental.

The user-friendly platform Switzercloud by Brunata AG complemented the algorithms, offering real-time route planning via an app. Drivers could adjust routes dynamically, enhancing flexibility and resource optimization.

Impact

The dynamic routing system achieved a 24% reduction in collection distances (~500 km per year) in the City of Burgdorf, significantly lowering fuel consumption, CO2 emissions (~ 250 kg CO2 per year), and operational costs. The system’s real-time adaptability ensured efficient resource use without compromising service quality. A user-friendly interface allowed the operators and drivers to generate a dynamic schedule and route with a few clicks.

Collaboration

Next to their data, the city of Burgdorf team led by Andreas Rössler, Chief Digital Officer, provided valuable insights and testing grounds, ensuring that the solutions were aligned with practical needs. The collaboration between Brunata AG, Burgdorf, and SDSC played a central role in the project’s success: Brunata AG developed a user-friendly app to interface with the algorithms created by the SDSC. This partnership exemplifies how data-driven innovations can address real-world challenges, offering scalable and impactful solutions for urban waste management.

Figure1: Screenshot of real-time dashboard view in mobile app:the operators can see which containers they are recommended to include in theirvehicle routing depending on sufficient fill levels.
Figure 2: Filling and emptying patterns of all containersin Burgdorf. Before the AI-powered optimization all containers got collectedevery two weeks. Many containers were less than 40% fullwhen collected (0.4 mark).
Figure 3: With the optimized, dynamic collection schedulethe container capacity is better leveraged. Not every container is emptied every second week, and thecontainers get collected when they have higher fill levels.

Cover image source: Stadtmarketing Burgdorf

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