Developing Optimization Tools for Municipal Solid Waste Collection in the Argentine City of Berazategui

Federico Bertero (), Manuela Cerdeiro (), Guillermo A. Durán () and Nazareno A. Faillace Mullen ()
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Federico Bertero: Findo, Buenos Aires C1002ABH, Argentina
Manuela Cerdeiro: Departamento de Computación, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires C1428EGA, Argentina
Guillermo A. Durán: Departamento de Matemática, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires C1428EGA, Argentina; Instituto de Cálculo, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires C1428EGA, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires C1425FQB, Argentina; Departamento de Ingeniería Industrial, Facultad de Ciencias Físicas y Matemáticas, Universidad de Chile, Santiago 837-0456, Chile
Nazareno A. Faillace Mullen: Instituto de Cálculo, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires C1428EGA, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires C1425FQB, Argentina

Interfaces, 2023, vol. 53, issue 6, 451-464

Abstract: We apply mathematical and computational techniques to the development of an approach for optimizing the waste collection system of the Argentine city of Berazategui, 26 km south of Buenos Aires. Taking full account of the city’s particular characteristics, our objective is to not only improve the system’s efficiency but also ensure equitable workloads for waste collection truck crews, including both drivers and collectors. The optimization problem is partitioned into three stages. In the first stage, a heuristic constructs structurally simple collection zones that are balanced in terms of collectors’ walking distances. In the second stage, a mixed-integer linear programming model designs a collection truck route for each zone and minimizes its length. In the third and final stage, each truck is assigned to two zones in such a way as to equalize to the extent possible the length of drivers’ working day. Because working-day length is influenced by multiple factors, we formulate this objective as a biobjective optimization problem and solve it by integer linear programming coupled with an iterative algorithm. The city implemented the approach in early 2020, resulting in a markedly more equitable workload distribution and significant fuel savings and maintenance expense for the city.

Keywords: municipal solid waste; vehicle routing; integer lineal programming; multiobjective optimization; heuristics (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orinte:v:53:y:2023:i:6:p:451-464

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