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Inventory routing for dynamic waste collection
Martijn Mes1, Marco Schutten1, Arturo Pérez Rivera1
1Department Industrial Engineering and Business Information Systems, School of Management and Governance, University of Twente, The Netherlands.
Waste Management (New York, N.Y.)
|June 10, 2014
Summary
Optimizing waste collection routes using a dynamic policy with tunable parameters can significantly reduce costs. This approach addresses uncertainties in waste volume, leading to potential savings of up to 40% for collection companies.
Area of Science:
- Operations Research
- Logistics Management
- Environmental Management
Background:
- Waste collection relies on dynamic policies enabled by sensor-equipped underground containers.
- The reverse inventory routing problem (RIRP) is critical for efficient waste management, especially in dense urban networks.
- Uncertainty in waste deposit volumes and daily/seasonal fluctuations necessitate proactive collection strategies.
Purpose of the Study:
- To develop and evaluate an anticipatory heuristic policy for dynamic waste collection.
- To optimize routing and container selection decisions within the RIRP framework.
- To balance workload over time and mitigate the impact of volume uncertainties.
Main Methods:
- A heuristic policy with day-of-the-week dependent tunable parameters was proposed.
- Optimal learning techniques combined with simulation were used for parameter tuning.
- The approach was validated using a real-world case study from a Dutch waste collection company.
Main Results:
- The proposed heuristic policy demonstrated significant cost-saving potential.
- Cost reductions of up to 40% were achieved in the case study through parameter optimization.
- Experiments on multiple instances confirmed the effectiveness of the approach.
Conclusions:
- Dynamic collection policies, informed by sensor data and optimized through learning techniques, offer substantial economic benefits.
- The developed heuristic provides an effective solution for the reverse inventory routing problem under uncertainty.
- This research offers a practical framework for improving the efficiency and cost-effectiveness of waste collection services.
