IDEAS home Printed from https://ideas.repec.org/a/spr/operea/v21y2021i3d10.1007_s12351-019-00538-5.html
   My bibliography  Save this article

A Bi-objective stochastic programming model for the household waste collection and transportation problem: case of the city of Sousse

Author

Listed:
  • Haifa Jammeli

    (Institute of Higher Studies Tunis)

  • Majdi Argoubi

    (University of Sousse)

  • Hatem Masri

    (University of Bahrain)

Abstract

This paper’s aim is to develop a model for the household waste collection and transportation problem in the city of Sousse, one of the largest cities in Tunisia. Several vehicles with a finite capacity are located at the depot. The vehicles must collect the waste accumulated in all bins. The waste is then delivered to a transfer center, before vehicles return to the depot. The proposed model determines the routes of the vehicles and the number of bins to be assigned to each potential location, while minimizing the collection costs and the environmental impact. The problem can be considered as a bi-objective optimization problem, as cost minimization will be ensured by the optimal assignment of the determined minimum number of bins. We also consider the stochastic aspect of population size, which is supposed to follow a normal distribution. Our model is then a stochastic bi-objective programming model. A solution is obtained with reasonable computational effort using a hierarchical approach consisting of two stages as “cluster-first route-second”. In the first stage, a set of n locations of bins is assigned into k disjoint clusters using the K-means clustering algorithm. In the second stage, a certainty equivalent program to the bi-objective stochastic program is proposed, based on a chance-constrained, recourse and a goal programming approach. The model is tested and implemented using real data from the municipality of Sousse. The study shows that our model leads to lower environmental impact and an almost 38% reduction in the economic costs.

Suggested Citation

  • Haifa Jammeli & Majdi Argoubi & Hatem Masri, 2021. "A Bi-objective stochastic programming model for the household waste collection and transportation problem: case of the city of Sousse," Operational Research, Springer, vol. 21(3), pages 1613-1639, September.
  • Handle: RePEc:spr:operea:v:21:y:2021:i:3:d:10.1007_s12351-019-00538-5
    DOI: 10.1007/s12351-019-00538-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12351-019-00538-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s12351-019-00538-5?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Surya Sahoo & Seongbae Kim & Byung-In Kim & Bob Kraas & Alexander Popov, 2005. "Routing Optimization for Waste Management," Interfaces, INFORMS, vol. 35(1), pages 24-36, February.
    2. Ganesh, K. & Narendran, T.T., 2007. "CLOVES: A cluster-and-search heuristic to solve the vehicle routing problem with delivery and pick-up," European Journal of Operational Research, Elsevier, vol. 178(3), pages 699-717, May.
    3. De Meyer, Annelies & Cattrysse, Dirk & Van Orshoven, Jos, 2015. "A generic mathematical model to optimise strategic and tactical decisions in biomass-based supply chains (OPTIMASS)," European Journal of Operational Research, Elsevier, vol. 245(1), pages 247-264.
    4. Richard Bellman, 1954. "Some Applications of the Theory of Dynamic Programming---A Review," Operations Research, INFORMS, vol. 2(3), pages 275-288, August.
    5. Abdelaziz, Fouad Ben & Aouni, Belaid & Fayedh, Rimeh El, 2007. "Multi-objective stochastic programming for portfolio selection," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1811-1823, March.
    6. Toso, Eli Angela V. & Alem, Douglas, 2014. "Effective location models for sorting recyclables in public management," European Journal of Operational Research, Elsevier, vol. 234(3), pages 839-860.
    7. Listes, Ovidiu & Dekker, Rommert, 2005. "A stochastic approach to a case study for product recovery network design," European Journal of Operational Research, Elsevier, vol. 160(1), pages 268-287, January.
    8. K. Ganesh & T.T. Narendran, 2007. "CLASH: a heuristic to solve vehicle routing problems with delivery, pick-up and time windows," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 3(4), pages 460-477.
    9. Richard Bellman, 1954. "On some applications of the theory of dynamic programming to logistics," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 1(2), pages 141-153, June.
    10. Yunna Wu & Meng Yang & Haobo Zhang & Kaifeng Chen & Yang Wang, 2016. "Optimal Site Selection of Electric Vehicle Charging Stations Based on a Cloud Model and the PROMETHEE Method," Energies, MDPI, vol. 9(3), pages 1-20, March.
    11. A. Charnes & W. W. Cooper, 1959. "Chance-Constrained Programming," Management Science, INFORMS, vol. 6(1), pages 73-79, October.
    12. Robert M. Clark & James I. Gillean, 1975. "Analysis of Solid Waste Management Operations in Cleveland, Ohio: A Case Study," Interfaces, INFORMS, vol. 6(1-part-2), pages 32-42, November.
    13. Alçada-Almeida, Luís & Coutinho-Rodrigues, João & Current, John, 2009. "A multiobjective modeling approach to locating incinerators," Socio-Economic Planning Sciences, Elsevier, vol. 43(2), pages 111-120, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ivan Eryganov & Radovan Šomplák & Dušan Hrabec & Josef Jadrný, 2023. "Bilevel programming methods in waste-to-energy plants' price-setting game," Operational Research, Springer, vol. 23(2), pages 1-37, June.

    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. Xiaoyue Li & John M. Mulvey, 2023. "Optimal Portfolio Execution in a Regime-switching Market with Non-linear Impact Costs: Combining Dynamic Program and Neural Network," Papers 2306.08809, arXiv.org.
    2. Mahmoud Mahfouz & Angelos Filos & Cyrine Chtourou & Joshua Lockhart & Samuel Assefa & Manuela Veloso & Danilo Mandic & Tucker Balch, 2019. "On the Importance of Opponent Modeling in Auction Markets," Papers 1911.12816, arXiv.org.
    3. Dawei Chen & Fangxu Mo & Ye Chen & Jun Zhang & Xinyu You, 2022. "Optimization of Ramp Locations along Freeways: A Dynamic Programming Approach," Sustainability, MDPI, vol. 14(15), pages 1-13, August.
    4. Harrold, Daniel J.B. & Cao, Jun & Fan, Zhong, 2022. "Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning," Energy, Elsevier, vol. 238(PC).
    5. Wadi Khalid Anuar & Lai Soon Lee & Hsin-Vonn Seow & Stefan Pickl, 2021. "A Multi-Depot Vehicle Routing Problem with Stochastic Road Capacity and Reduced Two-Stage Stochastic Integer Linear Programming Models for Rollout Algorithm," Mathematics, MDPI, vol. 9(13), pages 1-44, July.
    6. Hatem Masri, 2017. "A multiple stochastic goal programming approach for the agent portfolio selection problem," Annals of Operations Research, Springer, vol. 251(1), pages 179-192, April.
    7. Matthias Breuer & David Windisch, 2019. "Investment Dynamics and Earnings‐Return Properties: A Structural Approach," Journal of Accounting Research, Wiley Blackwell, vol. 57(3), pages 639-674, June.
    8. Diefenbach, Heiko & Emde, Simon & Glock, Christoph H., 2020. "Loading tow trains ergonomically for just-in-time part supply," European Journal of Operational Research, Elsevier, vol. 284(1), pages 325-344.
    9. Michael J. Pennock & William B. Rouse & Diane L. Kollar, 2007. "Transforming the Acquisition Enterprise: A Framework for Analysis and a Case Study of Ship Acquisition," Systems Engineering, John Wiley & Sons, vol. 10(2), pages 99-117, June.
    10. Quetschlich, Mathias & Moetz, André & Otto, Boris, 2021. "Optimisation model for multi-item multi-echelon supply chains with nested multi-level products," European Journal of Operational Research, Elsevier, vol. 290(1), pages 144-158.
    11. Sasanka Adikari & Norou Diawara, 2024. "Utility in Time Description in Priority Best–Worst Discrete Choice Models: An Empirical Evaluation Using Flynn’s Data," Stats, MDPI, vol. 7(1), pages 1-18, February.
    12. Coit, David W. & Zio, Enrico, 2019. "The evolution of system reliability optimization," Reliability Engineering and System Safety, Elsevier, vol. 192(C).
    13. Gamberini, Rita & Gebennini, Elisa & Manzini, Riccardo & Ziveri, Andrea, 2010. "On the integration of planning and environmental impact assessment for a WEEE transportation network—A case study," Resources, Conservation & Recycling, Elsevier, vol. 54(11), pages 937-951.
    14. Peng, Hujun & Li, Jianxiang & Löwenstein, Lars & Hameyer, Kay, 2020. "A scalable, causal, adaptive energy management strategy based on optimal control theory for a fuel cell hybrid railway vehicle," Applied Energy, Elsevier, vol. 267(C).
    15. Vincent Huang & James Unwin, 2019. "Markov Chain Models of Refugee Migration Data," Papers 1903.08255, arXiv.org.
    16. Esmaeili Aliabadi, Danial & Chan, Katrina, 2022. "The emerging threat of artificial intelligence on competition in liberalized electricity markets: A deep Q-network approach," Applied Energy, Elsevier, vol. 325(C).
    17. Alex Sharp & Ryan Browne, 2021. "Functional data clustering by projection into latent generalized hyperbolic subspaces," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(3), pages 735-757, September.
    18. Thomas L. Magnanti, 2021. "Optimization: From Its Inception," Management Science, INFORMS, vol. 67(9), pages 5349-5363, September.
    19. G., Mauricio Contreras & Peña, Juan Pablo, 2019. "The quantum dark side of the optimal control theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 450-473.
    20. Yann Disser & John Fearnley & Martin Gairing & Oliver Göbel & Max Klimm & Daniel Schmand & Alexander Skopalik & Andreas Tönnis, 2020. "Hiring Secretaries over Time: The Benefit of Concurrent Employment," Mathematics of Operations Research, INFORMS, vol. 45(1), pages 323-352, February.

    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:spr:operea:v:21:y:2021:i:3:d:10.1007_s12351-019-00538-5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.