1736. Urban Waste Collection Planning: From Flexible Scheduling to Data-Driven Tour Improvements
Invited abstract in session TC-15: Heuristic Search 3, stream Combinatorial Optimization.
Tuesday, 12:30-14:00Room: Esther Simpson 1.08
Authors (first author is the speaker)
| 1. | Christina Hess
|
| Business Decisions and Analytics, University of Vienna | |
| 2. | Alina-Gabriela Dragomir
|
| Business Decisions and Analytics, University of Vienna | |
| 3. | Karl Doerner
|
| Department of Business Decisions and Analytics, University of Vienna |
Abstract
Efficient solid waste collection is critical for urban sustainability, yet operational challenges persist due to variability in waste generation and logistical constraints. In collaboration with a practical partner, our research aims to generate actionable insights for real-world waste collection operations.
In our real-world problem, waste must be collected periodically from several thousand locations which have different types. In the first phase of our project, we conducted a computational study on flexibility both in the scheduling of collection visits and the utilization of intermediate facilities where vehicles need to unload whenever they are full and at the end of each tour. We solve the underlying operational problem, which is modelled as a Periodic Vehicle Routing Problem with Intermediate Facilities, with an Adaptive Large Neighborhood Search.
Additionally, we leverage fill-level sensor data collected over an extended period to improve decision-making. We address sensor errors and use forecasting models to predict bin fill levels. In ongoing work, we aim to integrate these forecasts into planning.
The proposed methods offer practical improvements in waste collection efficiency and adaptability, and contribute to cost reduction, service reliability, and sustainability in urban waste management.
Keywords
- Logistics
- Vehicle Routing
- Analytics and Data Science
Status: accepted
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