IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v8y2020i5p712-d353608.html
   My bibliography  Save this article

An Efficient Hybrid Genetic Approach for Solving the Two-Stage Supply Chain Network Design Problem with Fixed Costs

Author

Listed:
  • Ovidiu Cosma

    (Department of Mathematics and Computer Science, North University Center of Baia Mare, Technical University of Cluj-Napoca, 430083 Baia Mare, Romania)

  • Petrică C. Pop

    (Department of Mathematics and Computer Science, North University Center of Baia Mare, Technical University of Cluj-Napoca, 430083 Baia Mare, Romania)

  • Cosmin Sabo

    (Department of Mathematics and Computer Science, North University Center of Baia Mare, Technical University of Cluj-Napoca, 430083 Baia Mare, Romania)

Abstract

This paper deals with a complex optimization problem, more specifically the two-stage transportation problem with fixed costs. In our investigated transportation problem, we are modeling a distribution network in a two-stage supply chain. The considered two-stage supply chain includes manufacturers, distribution centers, and customers, and its principal feature is that in addition to the variable transportation costs, we have fixed costs for the opening of the distribution centers, as well as associated with the routes. In this paper, we describe a different approach for solving the problem, which is an effective hybrid genetic algorithm. Our proposed hybrid genetic algorithm is constructed to fit the challenges of the investigated supply chain network design problem, and it is achieved by incorporating a linear programming optimization problem within the framework of a genetic algorithm. Our achieved computational results are compared with the existing solution approaches on a set of 150 benchmark instances from the literature and on a set of 50 new randomly generated instances of larger sizes. The outputs proved that we have developed a very competitive approach as compared to the methods that one can find in the literature.

Suggested Citation

  • Ovidiu Cosma & Petrică C. Pop & Cosmin Sabo, 2020. "An Efficient Hybrid Genetic Approach for Solving the Two-Stage Supply Chain Network Design Problem with Fixed Costs," Mathematics, MDPI, vol. 8(5), pages 1-20, May.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:5:p:712-:d:353608
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/8/5/712/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/8/5/712/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Herminia I. Calvete & Carmen Galé & José A. Iranzo, 2016. "An improved evolutionary algorithm for the two-stage transportation problem with fixed charge at depots," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 38(1), pages 189-206, January.
    2. Hong, Jiangtao & Diabat, Ali & Panicker, Vinay V. & Rajagopalan, Sridharan, 2018. "A two-stage supply chain problem with fixed costs: An ant colony optimization approach," International Journal of Production Economics, Elsevier, vol. 204(C), pages 214-226.
    3. Melo, M.T. & Nickel, S. & Saldanha-da-Gama, F., 2009. "Facility location and supply chain management - A review," European Journal of Operational Research, Elsevier, vol. 196(2), pages 401-412, July.
    4. Erika Buson & Roberto Roberti & Paolo Toth, 2014. "A Reduced-Cost Iterated Local Search Heuristic for the Fixed-Charge Transportation Problem," Operations Research, INFORMS, vol. 62(5), pages 1095-1106, October.
    5. Jawahar, N. & Balaji, A.N., 2009. "A genetic algorithm for the two-stage supply chain distribution problem associated with a fixed charge," European Journal of Operational Research, Elsevier, vol. 194(2), pages 496-537, April.
    6. Michel L. Balinski, 1961. "Fixed‐cost transportation problems," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 8(1), pages 41-54, March.
    7. Wang, H.S., 2009. "A two-phase ant colony algorithm for multi-echelon defective supply chain network design," European Journal of Operational Research, Elsevier, vol. 192(1), pages 243-252, January.
    8. Klibi, Walid & Martel, Alain & Guitouni, Adel, 2010. "The design of robust value-creating supply chain networks: A critical review," European Journal of Operational Research, Elsevier, vol. 203(2), pages 283-293, June.
    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. Jahani, Hamed & Abbasi, Babak & Sheu, Jiuh-Biing & Klibi, Walid, 2024. "Supply chain network design with financial considerations: A comprehensive review," European Journal of Operational Research, Elsevier, vol. 312(3), pages 799-839.
    2. Becker, Tristan & Lier, Stefan & Werners, Brigitte, 2019. "Value of modular production concepts in future chemical industry production networks," European Journal of Operational Research, Elsevier, vol. 276(3), pages 957-970.
    3. Longinidis, Pantelis & Georgiadis, Michael C., 2014. "Integration of sale and leaseback in the optimal design of supply chain networks," Omega, Elsevier, vol. 47(C), pages 73-89.
    4. Mallidis, I. & Vlachos, D. & Dekker, R., 2010. "Greening Supply Chains: Impact on Cost and Design," Econometric Institute Research Papers EI 2010-39a, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    5. Jabbarzadeh, Armin & Fahimnia, Behnam & Sheu, Jiuh-Biing & Moghadam, Hani Shahmoradi, 2016. "Designing a supply chain resilient to major disruptions and supply/demand interruptions," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 121-149.
    6. Varsei, Mohsen & Polyakovskiy, Sergey, 2017. "Sustainable supply chain network design: A case of the wine industry in Australia," Omega, Elsevier, vol. 66(PB), pages 236-247.
    7. Roba W. Salem & Mohamed Haouari, 2017. "A simulation-optimisation approach for supply chain network design under supply and demand uncertainties," International Journal of Production Research, Taylor & Francis Journals, vol. 55(7), pages 1845-1861, April.
    8. Farahani, Reza Zanjirani & Rezapour, Shabnam & Drezner, Tammy & Fallah, Samira, 2014. "Competitive supply chain network design: An overview of classifications, models, solution techniques and applications," Omega, Elsevier, vol. 45(C), pages 92-118.
    9. Gilani, Hani & Sahebi, Hadi, 2022. "A data-driven robust optimization model by cutting hyperplanes on vaccine access uncertainty in COVID-19 vaccine supply chain," Omega, Elsevier, vol. 110(C).
    10. Blossey, Gregor & Hahn, Gerd J. & Koberstein, Achim, 2022. "Planning pharmaceutical manufacturing networks in the light of uncertain production approval times," International Journal of Production Economics, Elsevier, vol. 244(C).
    11. Margolis, Joshua T. & Sullivan, Kelly M. & Mason, Scott J. & Magagnotti, Mariah, 2018. "A multi-objective optimization model for designing resilient supply chain networks," International Journal of Production Economics, Elsevier, vol. 204(C), pages 174-185.
    12. Mohseni, Shayan & Pishvaee, Mir Saman & Sahebi, Hadi, 2016. "Robust design and planning of microalgae biomass-to-biodiesel supply chain: A case study in Iran," Energy, Elsevier, vol. 111(C), pages 736-755.
    13. Van Engeland, Jens & Beliën, Jeroen & De Boeck, Liesje & De Jaeger, Simon, 2020. "Literature review: Strategic network optimization models in waste reverse supply chains," Omega, Elsevier, vol. 91(C).
    14. Sahling, Florian & Kayser, Ariane, 2016. "Strategic supply network planning with vendor selection under consideration of risk and demand uncertainty," Omega, Elsevier, vol. 59(PB), pages 201-214.
    15. Snoeck, André & Udenio, Maximiliano & Fransoo, Jan C., 2019. "A stochastic program to evaluate disruption mitigation investments in the supply chain," European Journal of Operational Research, Elsevier, vol. 274(2), pages 516-530.
    16. Bairamzadeh, Samira & Saidi-Mehrabad, Mohammad & Pishvaee, Mir Saman, 2018. "Modelling different types of uncertainty in biofuel supply network design and planning: A robust optimization approach," Renewable Energy, Elsevier, vol. 116(PA), pages 500-517.
    17. Shiva Zokaee & Armin Jabbarzadeh & Behnam Fahimnia & Seyed Jafar Sadjadi, 2017. "Robust supply chain network design: an optimization model with real world application," Annals of Operations Research, Springer, vol. 257(1), pages 15-44, October.
    18. Ahmed Mostafa & Kamal Moustafa & Raafat Elshaer, 2023. "Impact of Fixed Cost Increase on the Optimization of Two-Stage Sustainable Supply Chain Networks," Sustainability, MDPI, vol. 15(18), pages 1-15, September.
    19. Brandenburg, Marcus, 2017. "A hybrid approach to configure eco-efficient supply chains under consideration of performance and risk aspects," Omega, Elsevier, vol. 70(C), pages 58-76.
    20. Guazzelli, Cauê Sauter & Cunha, Claudio B., 2018. "Exploring K-best solutions to enrich network design decision-making," Omega, Elsevier, vol. 78(C), pages 139-164.

    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:8:y:2020:i:5:p:712-:d:353608. 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.