LAPSE:2020.0782
Published Article
LAPSE:2020.0782
Modeling the Municipal Waste Collection Using Genetic Algorithms
July 2, 2020
Calculating adequate vehicle routes for collecting municipal waste is still an unsolved issue, even though many solutions for this process can be found in the literature. A gap still exists between academics and practitioners in the field. One of the apparent reasons why this rift exists is that academic tools often are not easy to handle and maintain by actual users. In this work, the problem of municipal waste collection is modeled using a simple but efficient and especially easy to maintain solution. Real data have been used, and it has been solved using a Genetic Algorithm (GA). Computations have been done in two different ways: using a complete random initial population, and including a seed in this initial population. In order to guarantee that the solution is efficient, the performance of the genetic algorithm has been compared with another well-performing algorithm, the Variable Neighborhood Search (VNS). Three problems of different sizes have been solved and, in all cases, a significant improvement has been obtained. A total reduction of 40% of itineraries is attained with the subsequent reduction of emissions and costs.
Keywords
genetic algorithms, traveling salesman problem, waste collection route planning
Suggested Citation
Alberdi E, Urrutia L, Goti A, Oyarbide-Zubillaga A. Modeling the Municipal Waste Collection Using Genetic Algorithms. (2020). LAPSE:2020.0782
Author Affiliations
Alberdi E: Department of Applied Mathematics, University of the Basque Country UPV/EHU, 48013 Bilbao, Bizkaia, Spain [ORCID]
Urrutia L: Department of Applied Mathematics, University of the Basque Country UPV/EHU, 48013 Bilbao, Bizkaia, Spain
Goti A: Deusto Digital Industry Chair, University of Deusto, 48007 Bilbao, Bizkaia, Spain [ORCID]
Oyarbide-Zubillaga A: Department of Industrial Mechanics, Design and Organization, University of Deusto, 48007 Bilbao, Bizkaia, Spain [ORCID]
Journal Name
Processes
Volume
8
Issue
5
Article Number
E513
Year
2020
Publication Date
2020-04-27
Published Version
ISSN
2227-9717
Version Comments
Original Submission
Other Meta
PII: pr8050513, Publication Type: Journal Article
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LAPSE:2020.0782
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doi:10.3390/pr8050513
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Jul 2, 2020
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CC BY 4.0
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Jul 2, 2020
 
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Jul 2, 2020
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Original Submitter
Calvin Tsay
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