IDEAS home Printed from https://ideas.repec.org/a/inm/orinte/v53y2023i6p451-464.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Federico Bertero & Manuela Cerdeiro & Guillermo A. Durán & Nazareno A. Faillace Mullen, 2023. "Developing Optimization Tools for Municipal Solid Waste Collection in the Argentine City of Berazategui," Interfaces, INFORMS, vol. 53(6), pages 451-464, November.
    • Handle: RePEc:inm:orinte:v:53:y:2023:i:6:p:451-464
    DOI: 10.1287/inte.2022.0042
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/inte.2022.0042
    Download Restriction: no
    File URL: https://libkey.io/10.1287/inte.2022.0042?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. G. H. Huang & B. W. Baetz & G. G. Patry, 1998. "Trash-Flow Allocation: Planning Under Uncertainty," Interfaces, INFORMS, vol. 28(6), pages 36-55, December.
    2. Mourao, M. Candida & Almeida, M. Teresa, 2000. "Lower-bounding and heuristic methods for a refuse collection vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 121(2), pages 420-434, March.
    3. Robert M. Nauss, 2003. "Solving the Generalized Assignment Problem: An Optimizing and Heuristic Approach," INFORMS Journal on Computing, INFORMS, vol. 15(3), pages 249-266, August.
    4. Edoardo Fadda & Luca Gobbato & Guido Perboli & Mariangela Rosano & Roberto Tadei, 2018. "Waste Collection in Urban Areas: A Case Study," Interfaces, INFORMS, vol. 48(4), pages 307-322, August.
    5. Marius M. Solomon, 1987. "Algorithms for the Vehicle Routing and Scheduling Problems with Time Window Constraints," Operations Research, INFORMS, vol. 35(2), pages 254-265, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Maximilian Schiffer & Michael Schneider & Grit Walther & Gilbert Laporte, 2019. "Vehicle Routing and Location Routing with Intermediate Stops: A Review," Transportation Science, INFORMS, vol. 53(2), pages 319-343, March.
    2. Tan, K.C. & Chew, Y.H. & Lee, L.H., 2006. "A hybrid multi-objective evolutionary algorithm for solving truck and trailer vehicle routing problems," European Journal of Operational Research, Elsevier, vol. 172(3), pages 855-885, August.
    3. Erfan Ghorbani & Mahdi Alinaghian & Gevork. B. Gharehpetian & Sajad Mohammadi & Guido Perboli, 2020. "A Survey on Environmentally Friendly Vehicle Routing Problem and a Proposal of Its Classification," Sustainability, MDPI, vol. 12(21), pages 1-71, October.
    4. Yi-Kuei Lin & Cheng-Fu Huang & Yi-Chieh Liao, 2019. "Reliability of a stochastic intermodal logistics network under spoilage and time considerations," Annals of Operations Research, Springer, vol. 277(1), pages 95-118, June.
    5. Gerald Senarclens de Grancy & Marc Reimann, 2015. "Evaluating two new heuristics for constructing customer clusters in a VRPTW with multiple service workers," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 23(2), pages 479-500, June.
    6. Filippo Focacci & Andrea Lodi & Michela Milano, 2002. "A Hybrid Exact Algorithm for the TSPTW," INFORMS Journal on Computing, INFORMS, vol. 14(4), pages 403-417, November.
    7. Nicola Bianchessi & Michael Drexl & Stefan Irnich, 2019. "The Split Delivery Vehicle Routing Problem with Time Windows and Customer Inconvenience Constraints," Transportation Science, INFORMS, vol. 53(4), pages 1067-1084, March.
    8. M Büther, 2010. "Reducing the elastic generalized assignment problem to the standard generalized assignment problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(11), pages 1582-1595, November.
    9. Zhang, Ying & Qi, Mingyao & Miao, Lixin & Liu, Erchao, 2014. "Hybrid metaheuristic solutions to inventory location routing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 70(C), pages 305-323.
    10. Alberto Ceselli & Giovanni Righini, 2006. "A Branch-and-Price Algorithm for the Multilevel Generalized Assignment Problem," Operations Research, INFORMS, vol. 54(6), pages 1172-1184, December.
    11. Ann Melissa Campbell & Martin Savelsbergh, 2004. "Efficient Insertion Heuristics for Vehicle Routing and Scheduling Problems," Transportation Science, INFORMS, vol. 38(3), pages 369-378, August.
    12. Mutsunori Yagiura & Toshihide Ibaraki & Fred Glover, 2004. "An Ejection Chain Approach for the Generalized Assignment Problem," INFORMS Journal on Computing, INFORMS, vol. 16(2), pages 133-151, May.
    13. Nicolas Rincon-Garcia & Ben J. Waterson & Tom J. Cherrett, 2018. "Requirements from vehicle routing software: perspectives from literature, developers and the freight industry," Transport Reviews, Taylor & Francis Journals, vol. 38(1), pages 117-138, January.
    14. Edirisinghe, N.C.P. & James, R.J.W., 2014. "Fleet routing position-based model for inventory pickup under production shutdown," European Journal of Operational Research, Elsevier, vol. 236(2), pages 736-747.
    15. Koç, Çağrı & Bektaş, Tolga & Jabali, Ola & Laporte, Gilbert, 2016. "The fleet size and mix location-routing problem with time windows: Formulations and a heuristic algorithm," European Journal of Operational Research, Elsevier, vol. 248(1), pages 33-51.
    16. Lu, Quan & Dessouky, Maged M., 2006. "A new insertion-based construction heuristic for solving the pickup and delivery problem with time windows," European Journal of Operational Research, Elsevier, vol. 175(2), pages 672-687, December.
    17. Babagolzadeh, Mahla & Zhang, Yahua & Abbasi, Babak & Shrestha, Anup & Zhang, Anming, 2022. "Promoting Australian regional airports with subsidy schemes: Optimised downstream logistics using vehicle routing problem," Transport Policy, Elsevier, vol. 128(C), pages 38-51.
    18. Goos Kant & Michael Jacks & Corné Aantjes, 2008. "Coca-Cola Enterprises Optimizes Vehicle Routes for Efficient Product Delivery," Interfaces, INFORMS, vol. 38(1), pages 40-50, February.
    19. Zehtabian, Shohre & Larsen, Christian & Wøhlk, Sanne, 2022. "Estimation of the arrival time of deliveries by occasional drivers in a crowd-shipping setting," European Journal of Operational Research, Elsevier, vol. 303(2), pages 616-632.
    20. Ramon Faganello Fachini & Vinícius Amaral Armentano & Franklina Maria Bragion Toledo, 2022. "A Granular Local Search Matheuristic for a Heterogeneous Fleet Vehicle Routing Problem with Stochastic Travel Times," Networks and Spatial Economics, Springer, vol. 22(1), pages 33-64, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orinte:v:53:y:2023:i:6:p:451-464. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.