IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i2p191-d720493.html
   My bibliography  Save this article

A Rich Vehicle Routing Problem for a City Logistics Problem

Author

Listed:
  • Daniela Ambrosino

    (Department of Economics and Business Studies, University of Genova, 16126 Genova, Italy)

  • Carmine Cerrone

    (Department of Economics and Business Studies, University of Genova, 16126 Genova, Italy)

Abstract

In this work, a Rich Vehicle Routing Problem (RVRP) is faced for solving city logistic problems. In particular, we deal with the problem of a logistic company that has to define the best distribution strategy for obtaining an efficient usage of vehicles and for reducing transportation costs while serving customers with different priority demands during a given planning horizon. Thus, we deal with a multi-period vehicle routing problem with a heterogeneous fleet of vehicles, with customers’ requirements and company restrictions to satisfy, in which the fleet composition has to be daily defined. In fact, the company has a fleet of owned vehicles and the possibility to select, day by day, a certain number of vehicles from the fleet of a third-party company. Routing costs must be minimized together with the number of vehicles used. A mixed integer programming model is proposed, and an experimental campaign is presented for validating it. Tests have been used for evaluating the quality of the solutions in terms of both model behavior and service level to grant to the customers. Moreover, the benefits that can be obtained by postponing deliveries are evaluated. Results are discussed, and some conclusions are highlighted, including the possibility of formulating this problem in such a way as to use the general solver proposed in the recent literature. This seems to be the most interesting challenge to permit companies to improve the distribution activities.

Suggested Citation

  • Daniela Ambrosino & Carmine Cerrone, 2022. "A Rich Vehicle Routing Problem for a City Logistics Problem," Mathematics, MDPI, vol. 10(2), pages 1-13, January.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:2:p:191-:d:720493
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/2/191/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/2/191/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Thibaut Vidal & Teodor Gabriel Crainic & Michel Gendreau & Nadia Lahrichi & Walter Rei, 2012. "A Hybrid Genetic Algorithm for Multidepot and Periodic Vehicle Routing Problems," Operations Research, INFORMS, vol. 60(3), pages 611-624, June.
    2. Lahyani, Rahma & Khemakhem, Mahdi & Semet, Frédéric, 2015. "Rich vehicle routing problems: From a taxonomy to a definition," European Journal of Operational Research, Elsevier, vol. 241(1), pages 1-14.
    3. Techane Bosona, 2020. "Urban Freight Last Mile Logistics—Challenges and Opportunities to Improve Sustainability: A Literature Review," Sustainability, MDPI, vol. 12(21), pages 1-20, October.
    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. Zsolt Tibor Kosztyán & Zoltán Kovács, 2023. "Preface to the Special Issue on “Mathematical Methods and Operation Research in Logistics, Project Planning, and Scheduling”," Mathematics, MDPI, vol. 11(1), pages 1-3, January.

    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. Bochra Rabbouch & Foued Saâdaoui & Rafaa Mraihi, 2021. "Efficient implementation of the genetic algorithm to solve rich vehicle routing problems," Operational Research, Springer, vol. 21(3), pages 1763-1791, September.
    2. Campelo, Pedro & Neves-Moreira, Fábio & Amorim, Pedro & Almada-Lobo, Bernardo, 2019. "Consistent vehicle routing problem with service level agreements: A case study in the pharmaceutical distribution sector," European Journal of Operational Research, Elsevier, vol. 273(1), pages 131-145.
    3. Max Leyerer & Marc-Oliver Sonneberg & Maximilian Heumann & Tim Kammann & Michael H. Breitner, 2019. "Individually Optimized Commercial Road Transport: A Decision Support System for Customizable Routing Problems," Sustainability, MDPI, vol. 11(20), pages 1-21, October.
    4. Schönberger, Jörn, 2017. "Implicit time windows and multi-commodity mixed-fleet vehicle routing," Discussion Papers 1/2017, Technische Universität Dresden, "Friedrich List" Faculty of Transport and Traffic Sciences, Institute of Transport and Economics.
    5. Yuwen Yang & Jayant Rajgopal, 2021. "Outreach Strategies for Vaccine Distribution: A Multi-period Stochastic Modeling Approach," SN Operations Research Forum, Springer, vol. 2(2), pages 1-26, June.
    6. Filip Škultéty & Dominika Beňová & Jozef Gnap, 2021. "City Logistics as an Imperative Smart City Mechanism: Scrutiny of Clustered EU27 Capitals," Sustainability, MDPI, vol. 13(7), pages 1-16, March.
    7. Ostermeier, Manuel & Henke, Tino & Hübner, Alexander & Wäscher, Gerhard, 2021. "Multi-compartment vehicle routing problems: State-of-the-art, modeling framework and future directions," European Journal of Operational Research, Elsevier, vol. 292(3), pages 799-817.
    8. Pahlevani, Delaram & Abbasi, Babak & Hearne, John W. & Eberhard, Andrew, 2022. "A cluster-based algorithm for home health care planning: A case study in Australia," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
    9. A. Mor & M. G. Speranza, 2020. "Vehicle routing problems over time: a survey," 4OR, Springer, vol. 18(2), pages 129-149, June.
    10. Olcay Polat & Duygu Topaloğlu, 2022. "Collection of different types of milk with multi-tank tankers under uncertainty: a real case study," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(1), pages 1-33, April.
    11. Yang Xia & Wenjia Zeng & Xinjie Xing & Yuanzhu Zhan & Kim Hua Tan & Ajay Kumar, 2023. "Joint optimisation of drone routing and battery wear for sustainable supply chain development: a mixed-integer programming model based on blockchain-enabled fleet sharing," Annals of Operations Research, Springer, vol. 327(1), pages 89-127, August.
    12. Polten, Lukas & Emde, Simon, 2022. "Multi-shuttle crane scheduling in automated storage and retrieval systems," European Journal of Operational Research, Elsevier, vol. 302(3), pages 892-908.
    13. Ramos, Tânia Rodrigues Pereira & Gomes, Maria Isabel & Barbosa-Póvoa, Ana Paula, 2014. "Assessing and improving management practices when planning packaging waste collection systems," Resources, Conservation & Recycling, Elsevier, vol. 85(C), pages 116-129.
    14. David Raba & Rafael D. Tordecilla & Pedro Copado & Angel A. Juan & Daniel Mount, 2022. "A Digital Twin for Decision Making on Livestock Feeding," Interfaces, INFORMS, vol. 52(3), pages 267-282, May.
    15. M. Angélica Salazar-Aguilar & Vincent Boyer & Romeo Sanchez Nigenda & Iris A. Martínez-Salazar, 2019. "The sales force sizing problem with multi-period workload assignments, and service time windows," 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. 27(1), pages 199-218, March.
    16. Li, Zhaotong & Wu, Min & Teo, Chee-Chong & Yuen, Kum Fai, 2024. "An investigation of consumer switching intention on the use of automated courier station from a signaling perspective," Journal of Retailing and Consumer Services, Elsevier, vol. 78(C).
    17. Rahma Lahyani & Leandro C. Coelho & Jacques Renaud, 2018. "Alternative formulations and improved bounds for the multi-depot fleet size and mix vehicle routing problem," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(1), pages 125-157, January.
    18. Coelho, Leandro Callegari & De Maio, Annarita & Laganà, Demetrio, 2020. "A variable MIP neighborhood descent for the multi-attribute inventory routing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 144(C).
    19. Li, Gang & Jiang, Hongxun & He, Tian, 2015. "A genetic algorithm-based decomposition approach to solve an integrated equipment-workforce-service planning problem," Omega, Elsevier, vol. 50(C), pages 1-17.
    20. Zhang, Lele & Ding, Pengyuan & Thompson, Russell G., 2023. "A stochastic formulation of the two-echelon vehicle routing and loading bay reservation problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).

    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:gam:jmathe:v:10:y:2022:i:2:p:191-:d:720493. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.