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Comparison of waste bin placement optimization methods: A case study in Zurich
Silas Schweizer1, Nicolas Seemann-Ricard1, Jakub Tkaczuk1
1Global Health Engineering, Department of Mechanical and Process Engineering, ETH Zürich, Zürich 8092, Switzerland.
Waste Management (New York, N.Y.)
|June 13, 2026
Summary
To improve recycling participation, this study optimized locations for new collection points. Using a mixed-integer programming model, 12 new sites significantly reduced the underserved population by 30%, enhancing accessibility for residents.
Area of Science:
- Urban Planning
- Environmental Science
- Operations Research
Background:
- Recycling participation is sensitive to the convenience of collection points.
- In Zurich, over 29,000 residents exceed the 10-minute walking distance to current recycling facilities.
- Inequitable access to recycling services can lead to lower participation rates.
Purpose of the Study:
- To develop and evaluate an accessibility-based framework for optimal placement of additional Recycling Collection Points (RCPs).
- To identify the minimum number and best locations for new RCPs to meet Zurich's 10-minute walking accessibility target.
- To compare the effectiveness of spatial clustering and mathematical optimization methods for siting RCPs.
Main Methods:
- Developed an accessibility-based siting framework using elevation-aware network routing.
- Compared two Density-Based Spatial Clustering of Applications with Noise (DBSCAN) methods against a Mixed Integer Linear Programming (MILP) p-median formulation.
- Evaluated site selection from a screened set of 305 candidate car park locations.
Main Results:
- Mixed Integer Linear Programming (MILP) optimization achieved 2-5% better coverage than DBSCAN methods.
- Adding 12 new Recycling Collection Points (RCPs) reduced the underserved population by approximately 30% (from 29,320 to 20,451 residents).
- Average walking time decreased marginally, indicating concentrated improvements for the most underserved populations.
Conclusions:
- The MILP optimization approach is more effective for selecting optimal Recycling Collection Points (RCPs) compared to clustering methods.
- The proposed framework successfully identifies strategic locations to improve pedestrian accessibility to recycling services.
- Future research should incorporate multi-objective optimization including operational factors and agent-based demand modeling.
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