IDEAS home Printed from https://ideas.repec.org/a/wut/journl/v2y2019p103-116id1389.html
   My bibliography  Save this article

Optimisation model for a chain logistics problem involving chilled food under conditions of uncertainty

Author

Listed:
  • Yaser Taghinezhad

Abstract

In the last two decades, food safety has become one of the main concerns in the area of logistics and supply chain management and also in the refrigeration or freezing of goods. Safety is a critically sensitive area in this field, as if the required safety conditions are not satisfied during the logistics process, foods will soon deteriorate and probably become unsafe for consumption by customers. Thus, the problem of ensuring the safety of chilled food has received serious attention among logistics practitioners. However, because of the complex nature of such problems, research so far has been limited to quantitative models with deterministic parameters and the robustness of the results from such models should be examined. In this paper, a robust optimisation model has been developed with the aim of optimising food safety aspects and thus minimising the logistics cost of a chilled chain system under various types of uncertainty and constraints on customers’ time windows. Realizations of the model are solved by an algorithm based on artificial bee colony intelligence using MATLAB R2016a software. Finally, the results are analysed for possible real world considerations in order to propose some key practical highlights.

Suggested Citation

  • Yaser Taghinezhad, 2019. "Optimisation model for a chain logistics problem involving chilled food under conditions of uncertainty," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 29(2), pages 103-116.
    • Handle: RePEc:wut:journl:v:2:y:2019:p:103-116:id:1389
    DOI: 10.37190/ord190207
    as

    Download full text from publisher

    File URL: https://ord.pwr.edu.pl/assets/papers_archive/1389%20-%20published.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.37190/ord190207?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. Witold Kosiński & Rafał Muniak & Witold Konrad Kosiński, 2013. "A model for optimizing enterprise’s inventory costs. A fuzzy approach," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 23(4), pages 39-54.
    2. Ewa Marchwicka & Dorota Kuchta, 2017. "Modified optimization model for selecting project risk response strategies," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 27(2), pages 77-90.
    3. Soysal, Mehmet & Bloemhof, Jacqueline M. & van der Vorst, Jack G.A.J., 2012. "A Review of Quantitative Models for Sustainable Food Logistics Management: Challenges and Issues," 2012 International European Forum, February 13-17, 2012, Innsbruck-Igls, Austria 144974, International European Forum on System Dynamics and Innovation in Food Networks.
    4. Soysal, Mehmet & Bloemhof-Ruwaard, Jacqueline.M. & Meuwissen, Miranda P.M. & van der Vorst, Jack G.A.J., 2012. "A Review on Quantitative Models for Sustainable Food Logistics Management," International Journal on Food System Dynamics, International Center for Management, Communication, and Research, vol. 3(2), pages 1-20, December.
    5. Yu, Chian-Son & Li, Han-Lin, 2000. "A robust optimization model for stochastic logistic problems," International Journal of Production Economics, Elsevier, vol. 64(1-3), pages 385-397, March.
    6. John M. Mulvey & Robert J. Vanderbei & Stavros A. Zenios, 1995. "Robust Optimization of Large-Scale Systems," Operations Research, INFORMS, vol. 43(2), pages 264-281, April.
    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. Jadwiga Zarod, 2020. "Agricultural Production Planning Using a Multicriteria Optimization Model," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 481-490.

    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. Hashem Omrani & Farzane Adabi & Narges Adabi, 2017. "Designing an efficient supply chain network with uncertain data: a robust optimization—data envelopment analysis approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(7), pages 816-828, July.
    2. Tsai, Jung-Fa, 2007. "An optimization approach for supply chain management models with quantity discount policy," European Journal of Operational Research, Elsevier, vol. 177(2), pages 982-994, March.
    3. Antonio G. Martín & Manuel Díaz-Madroñero & Josefa Mula, 2020. "Master production schedule using robust optimization approaches in an automobile second-tier supplier," 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. 28(1), pages 143-166, March.
    4. Rohmer, S.U.K. & Gerdessen, J.C. & Claassen, G.D.H., 2019. "Sustainable supply chain design in the food system with dietary considerations: A multi-objective analysis," European Journal of Operational Research, Elsevier, vol. 273(3), pages 1149-1164.
    5. Seyed Babak Ebrahimi & Ehsan Bagheri, 2022. "A multi-objective formulation for the closed-loop plastic supply chain under uncertainty," Operational Research, Springer, vol. 22(5), pages 4725-4768, November.
    6. Lai, K.K. & Wang, Ming & Liang, L., 2007. "A stochastic approach to professional services firms' revenue optimization," European Journal of Operational Research, Elsevier, vol. 182(3), pages 971-982, November.
    7. João Flávio de Freitas Almeida & Samuel Vieira Conceição & Luiz Ricardo Pinto & Ricardo Saraiva de Camargo & Gilberto de Miranda Júnior, 2018. "Flexibility evaluation of multiechelon supply chains," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-27, March.
    8. Golpîra, Hêriş & Khan, Syed Abdul Rehman, 2019. "A multi-objective risk-based robust optimization approach to energy management in smart residential buildings under combined demand and supply uncertainty," Energy, Elsevier, vol. 170(C), pages 1113-1129.
    9. Erfan Hassannayebi & Seyed Hessameddin Zegordi & Mohammad Reza Amin-Naseri & Masoud Yaghini, 2017. "Train timetabling at rapid rail transit lines: a robust multi-objective stochastic programming approach," Operational Research, Springer, vol. 17(2), pages 435-477, July.
    10. Xu, Y. & Huang, G.H. & Qin, X.S. & Cao, M.F., 2009. "SRCCP: A stochastic robust chance-constrained programming model for municipal solid waste management under uncertainty," Resources, Conservation & Recycling, Elsevier, vol. 53(6), pages 352-363.
    11. Azaron, A. & Brown, K.N. & Tarim, S.A. & Modarres, M., 2008. "A multi-objective stochastic programming approach for supply chain design considering risk," International Journal of Production Economics, Elsevier, vol. 116(1), pages 129-138, November.
    12. Xie, Y.L. & Huang, G.H. & Li, W. & Ji, L., 2014. "Carbon and air pollutants constrained energy planning for clean power generation with a robust optimization model—A case study of Jining City, China," Applied Energy, Elsevier, vol. 136(C), pages 150-167.
    13. Aalaei, Amin & Davoudpour, Hamid, 2017. "A robust optimization model for cellular manufacturing system into supply chain management," International Journal of Production Economics, Elsevier, vol. 183(PC), pages 667-679.
    14. Soto-Silva, Wladimir E. & Nadal-Roig, Esteve & González-Araya, Marcela C. & Pla-Aragones, Lluis M., 2016. "Operational research models applied to the fresh fruit supply chain," European Journal of Operational Research, Elsevier, vol. 251(2), pages 345-355.
    15. Masoud Hekmatfar & M. R. M. Aliha & Mir Saman Pishvaee & Tomasz Sadowski, 2023. "A Robust Flexible Optimization Model for 3D-Layout of Interior Equipment in a Multi-Floor Satellite," Mathematics, MDPI, vol. 11(24), pages 1-41, December.
    16. Gilani Larimi, Niloofar & Yaghoubi, Saeed & Hosseini-Motlagh, Seyyed-Mahdi, 2019. "Itemized platelet supply chain with lateral transshipment under uncertainty evaluating inappropriate output in laboratories," Socio-Economic Planning Sciences, Elsevier, vol. 68(C).
    17. Faraz Salehi & Masoud Mahootchi & Seyed Mohammad Moattar Husseini, 2019. "Developing a robust stochastic model for designing a blood supply chain network in a crisis: a possible earthquake in Tehran," Annals of Operations Research, Springer, vol. 283(1), pages 679-703, December.
    18. Mohammadi, Mehrdad & Dehghan, Milad & Pirayesh, Amir & Dolgui, Alexandre, 2022. "Bi‐objective optimization of a stochastic resilient vaccine distribution network in the context of the COVID‐19 pandemic," Omega, Elsevier, vol. 113(C).
    19. Javid Jouzdani & Mohammad Fathian & Ahmad Makui & Mehdi Heydari, 2020. "Robust design and planning for a multi-mode multi-product supply network: a dairy industry case study," Operational Research, Springer, vol. 20(3), pages 1811-1840, September.
    20. Dayanna Rodrigues da Cunha Nunes & Orivalde Soares da Silva Júnior & Renata Albergaria de Mello Bandeira & Yesus Emmanuel Medeiros Vieira, 2023. "A Robust Stochastic Programming Model for the Well Location Problem: The Case of The Brazilian Northeast Region," Sustainability, MDPI, vol. 15(14), pages 1-21, July.

    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:wut:journl:v:2:y:2019:p:103-116:id:1389. 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: Adam Kasperski (email available below). General contact details of provider: https://edirc.repec.org/data/iopwrpl.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.