IDEAS home Printed from https://ideas.repec.org/a/spr/joheur/v28y2022i3d10.1007_s10732-022-09497-1.html
   My bibliography  Save this article

A memetic algorithm for the inventory routing problem

Author

Listed:
  • Mohamed Salim Amri Sakhri

    (University of Tunis)

  • Mounira Tlili

    (University of Sousse)

  • Ouajdi Korbaa

    (University of Sousse)

Abstract

In this article, we study an Inventory Routing Problem with deterministic customer demand in a two-tier supply chain. The supply chain network consists of a supplier using a single vehicle with a given capacity to deliver a single product type to multiple customers. We are interested in population-based algorithms to solve our problem. A Memetic Algorithm (MA) is developed based on the Genetic Algorithm (GA) and Variable Neighborhood Search methods. The proposed meta-heuristics are tested on small and large reference benchmarks. The results of the MA are compared to those of the classical GA and to the optimal solutions in the literature. The comparison shows the efficiency of using MA and its ability to generate high quality solutions in a reasonable computation time.

Suggested Citation

  • Mohamed Salim Amri Sakhri & Mounira Tlili & Ouajdi Korbaa, 2022. "A memetic algorithm for the inventory routing problem," Journal of Heuristics, Springer, vol. 28(3), pages 351-375, June.
    • Handle: RePEc:spr:joheur:v:28:y:2022:i:3:d:10.1007_s10732-022-09497-1
    DOI: 10.1007/s10732-022-09497-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10732-022-09497-1
    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/s10732-022-09497-1?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. Mehmet Onur Olgun & Erdal Aydemir, 2021. "A new cooperative depot sharing approach for inventory routing problem," Annals of Operations Research, Springer, vol. 307(1), pages 417-441, December.
    2. Hadi Jahangir & Mohammad Mohammadi & Seyed Hamid Reza Pasandideh & Neda Zendehdel Nobari, 2019. "Comparing performance of genetic and discrete invasive weed optimization algorithms for solving the inventory routing problem with an incremental delivery," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2327-2353, August.
    3. Luca Bertazzi & Giuseppe Paletta & M. Grazia Speranza, 2002. "Deterministic Order-Up-To Level Policies in an Inventory Routing Problem," Transportation Science, INFORMS, vol. 36(1), pages 119-132, February.
    4. Rau, Hsin & Budiman, Syarif Daniel & Widyadana, Gede Agus, 2018. "Optimization of the multi-objective green cyclical inventory routing problem using discrete multi-swarm PSO method," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 120(C), pages 51-75.
    5. Leandro C. Coelho & Gilbert Laporte, 2015. "An optimised target-level inventory replenishment policy for vendor-managed inventory systems," International Journal of Production Research, Taylor & Francis Journals, vol. 53(12), pages 3651-3660, June.
    6. Luca Bertazzi & Maria Grazia Speranza, 2002. "Continuous and Discrete Shipping Strategies for the Single Link Problem," Transportation Science, INFORMS, vol. 36(3), pages 314-325, August.
    7. Claudia Archetti & Luca Bertazzi & Gilbert Laporte & Maria Grazia Speranza, 2007. "A Branch-and-Cut Algorithm for a Vendor-Managed Inventory-Routing Problem," Transportation Science, INFORMS, vol. 41(3), pages 382-391, August.
    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. Leandro C. Coelho & Jean-François Cordeau & Gilbert Laporte, 2014. "Thirty Years of Inventory Routing," Transportation Science, INFORMS, vol. 48(1), pages 1-19, February.
    2. Lap Mui Ann Chan & M. Grazia Speranza & Luca Bertazzi, 2013. "Asymptotic analysis of periodic policies for the inventory routing problem," Naval Research Logistics (NRL), John Wiley & Sons, vol. 60(7), pages 525-540, October.
    3. Bertazzi, Luca & Moezi, Sarem Deilami & Maggioni, Francesca, 2021. "The value of integration of full container load, less than container load and air freight shipments in vendor–managed inventory systems," International Journal of Production Economics, Elsevier, vol. 241(C).
    4. Schmid, Verena & Doerner, Karl F. & Laporte, Gilbert, 2013. "Rich routing problems arising in supply chain management," European Journal of Operational Research, Elsevier, vol. 224(3), pages 435-448.
    5. 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).
    6. Song, Ruidian & Zhao, Lei & Van Woensel, Tom & Fransoo, Jan C., 2019. "Coordinated delivery in urban retail," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 126(C), pages 122-148.
    7. Zhenzhen Zhang & Zhixing Luo & Roberto Baldacci & Andrew Lim, 2021. "A Benders Decomposition Approach for the Multivehicle Production Routing Problem with Order-up-to-Level Policy," Transportation Science, INFORMS, vol. 55(1), pages 160-178, 1-2.
    8. Diabat, Ali & Bianchessi, Nicola & Archetti, Claudia, 2024. "On the zero-inventory-ordering policy in the inventory routing problem," European Journal of Operational Research, Elsevier, vol. 312(3), pages 1024-1038.
    9. Yves Crama & Mahmood Rezaei & Martin Savelsbergh & Tom Van Woensel, 2018. "Stochastic Inventory Routing for Perishable Products," Transportation Science, INFORMS, vol. 52(3), pages 526-546, June.
    10. Og̀uz Solyalı & Haldun Süral & Meltem Denizel, 2010. "The one‐warehouse multiretailer problem with an order‐up‐to level inventory policy," Naval Research Logistics (NRL), John Wiley & Sons, vol. 57(7), pages 653-666, October.
    11. Guy Desaulniers & Jørgen G. Rakke & Leandro C. Coelho, 2016. "A Branch-Price-and-Cut Algorithm for the Inventory-Routing Problem," Transportation Science, INFORMS, vol. 50(3), pages 1060-1076, August.
    12. Zhouxing Su & Zhipeng Lü & Zhuo Wang & Yanmin Qi & Una Benlic, 2020. "A Matheuristic Algorithm for the Inventory Routing Problem," Transportation Science, INFORMS, vol. 54(2), pages 330-354, March.
    13. Paweł Hanczar, 2014. "Solving IRP using location based heuristics," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 24(2), pages 81-96.
    14. S. Michel & F. Vanderbeck, 2012. "A Column-Generation Based Tactical Planning Method for Inventory Routing," Operations Research, INFORMS, vol. 60(2), pages 382-397, April.
    15. Manousakis, Eleftherios & Repoussis, Panagiotis & Zachariadis, Emmanouil & Tarantilis, Christos, 2021. "Improved branch-and-cut for the Inventory Routing Problem based on a two-commodity flow formulation," European Journal of Operational Research, Elsevier, vol. 290(3), pages 870-885.
    16. Mirzapour Al-e-hashem, S.M.J. & Rekik, Yacine, 2014. "Multi-product multi-period Inventory Routing Problem with a transshipment option: A green approach," International Journal of Production Economics, Elsevier, vol. 157(C), pages 80-88.
    17. Mirzapour Al-e-hashem, Seyed M.J. & Rekik, Yacine & Mohammadi Hoseinhajlou, Ebrahim, 2019. "A hybrid L-shaped method to solve a bi-objective stochastic transshipment-enabled inventory routing problem," International Journal of Production Economics, Elsevier, vol. 209(C), pages 381-398.
    18. Agra, Agostinho & Christiansen, Marielle & Wolsey, Laurence, 2022. "Improved models for a single vehicle continuous-time inventory routing problem with pickups and deliveries," European Journal of Operational Research, Elsevier, vol. 297(1), pages 164-179.
    19. Oğuz Solyalı & Haldun Süral, 2011. "A Branch-and-Cut Algorithm Using a Strong Formulation and an A Priori Tour-Based Heuristic for an Inventory-Routing Problem," Transportation Science, INFORMS, vol. 45(3), pages 335-345, August.
    20. Archetti, Claudia & Coelho, Leandro C. & Grazia Speranza, M., 2019. "An exact algorithm for the inventory routing problem with logistic ratio," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 131(C), pages 96-107.

    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:joheur:v:28:y:2022:i:3:d:10.1007_s10732-022-09497-1. 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.