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Routing in waste collection: A simulated annealing algorithm for an Argentinean case study
Diego G Rossit1,2, Adrián A Toncovich1, Matías Fermani1
1Department of Engineering, Universidad Nacional del Sur, Alem Av. 1253, Bahía Blanca 8000, Argentina.
Mathematical Biosciences and Engineering : MBE
|November 24, 2021
Summary
A new simulated annealing algorithm optimizes municipal solid waste collection routes, improving efficiency and reducing costs for local governments. This computer-aided tool offers a viable solution, especially for resource-limited developing countries.
Area of Science:
- Operations Research
- Computer Science
- Environmental Engineering
Background:
- Municipal Solid Waste (MSW) collection is a significant budgetary challenge for local governments worldwide.
- Optimizing waste collection routes can enhance system efficiency and reduce operational costs, particularly in resource-constrained developing nations.
- Decision-support tools are crucial for improving the management of complex urban services like waste collection.
Purpose of the Study:
- To propose and evaluate a simulated annealing algorithm for optimizing municipal solid waste collection vehicle routing.
- To compare the performance of the proposed simulated annealing algorithm against a mixed-integer programming solver, a large neighborhood search, and a genetic algorithm.
- To assess the algorithm's effectiveness on both benchmark instances and real-world data from Bahía Blanca, Argentina.
Main Methods:
- Development of a simulated annealing algorithm tailored for the waste collection vehicle routing problem.
- Comparative analysis using a commercial mixed-integer programming solver.
- Benchmarking against a state-of-the-art large neighborhood search and a genetic algorithm.
- Validation on a standard literature benchmark and real-world instances from Bahía Blanca, Argentina.
Main Results:
- The simulated annealing algorithm successfully solved all tested instances.
- Its performance was comparable to the large neighborhood search algorithm.
- The genetic algorithm demonstrated the poorest performance among the metaheuristics.
- The simulated annealing approach improved upon the solutions provided by the commercial solver for many real-world instances.
Conclusions:
- Simulated annealing presents an effective metaheuristic for optimizing municipal solid waste collection routes.
- The algorithm offers a competitive and efficient alternative to existing methods, including commercial solvers.
- This approach holds significant potential for improving waste management efficiency and cost-effectiveness, especially in developing countries.
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