IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v321y2023i1d10.1007_s10479-022-05080-w.html
   My bibliography  Save this article

A new branch and efficiency algorithm for an optimal design of the supply chain network in view of resilience, inequity and traffic congestion

Author

Listed:
  • Ardavan Babaei

    (Sharif University of Technology)

  • Majid Khedmati

    (Sharif University of Technology)

  • Mohammad Reza Akbari Jokar

    (Sharif University of Technology)

Abstract

Location and allocation problems in supply chain networks are considered as strategic decisions; because they both require investment and have long-term effects. On the other hand, the supply chain network is not protected against disruptions in the real world. In this paper, a branch and efficiency (B&E) algorithm is developed which integrates a multi-objective optimization model used for designing the supply chain network with an extended data envelopment analysis (EDEA) model. Through this integration, efficient solutions are obtained that lead to minimization of the costs. The objective functions of the optimization model include the operational costs, resilience costs and inequality in satisfying customer demand. Then, the efficiency of the solutions is measured using EDEA in terms of the costs, service level and traffic congestion. The solutions derived from EDEA are added to the multi-objective optimization model based on efficiency cuts. This iterative procedure continues until an efficient design is developed for the supply chain network. The proposed B&E algorithm is implemented on a real case using fuzzy goal programming to illustrate its applicability. The results show that the proposed algorithm has better performance in reducing the costs and measuring efficiency compared to the competing algorithms in the literature.

Suggested Citation

  • Ardavan Babaei & Majid Khedmati & Mohammad Reza Akbari Jokar, 2023. "A new branch and efficiency algorithm for an optimal design of the supply chain network in view of resilience, inequity and traffic congestion," Annals of Operations Research, Springer, vol. 321(1), pages 49-78, February.
  • Handle: RePEc:spr:annopr:v:321:y:2023:i:1:d:10.1007_s10479-022-05080-w
    DOI: 10.1007/s10479-022-05080-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-022-05080-w
    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/s10479-022-05080-w?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. Ardavan Babaei & Majid Khedmati & Mohammad Reza Akbari Jokar & Erfan Babaee Tirkolaee, 2022. "Performance Evaluation of Omni-Channel Distribution Network Configurations considering Green and Transparent Criteria under Uncertainty," Sustainability, MDPI, vol. 14(19), pages 1-15, October.
    2. Alireza Amirteimoori, 2011. "An extended transportation problem: a DEA-based approach," 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. 19(4), pages 513-521, December.
    3. Hong, Jae-Dong & Mwakalonge, Judith L., 2020. "Biofuel logistics network scheme design with combined data envelopment analysis approach," Energy, Elsevier, vol. 209(C).
    4. Hosseini, Seyedmohsen & Ivanov, Dmitry & Dolgui, Alexandre, 2019. "Review of quantitative methods for supply chain resilience analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 285-307.
    5. Grigoroudis, Evangelos & Petridis, Konstantinos & Arabatzis, Garyfallos, 2014. "RDEA: A recursive DEA based algorithm for the optimal design of biomass supply chain networks," Renewable Energy, Elsevier, vol. 71(C), pages 113-122.
    6. 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.
    7. Konstantinos Petridis, 2015. "Optimal design of multi-echelon supply chain networks under normally distributed demand," Annals of Operations Research, Springer, vol. 227(1), pages 63-91, April.
    8. Ghasemi, Mohammad Reza & Ignatius, Joshua & Rezaee, Babak, 2019. "Improving discriminating power in data envelopment models based on deviation variables framework," European Journal of Operational Research, Elsevier, vol. 278(2), pages 442-447.
    9. Darmawan, Agus & Wong, Hartanto & Thorstenson, Anders, 2021. "Supply chain network design with coordinated inventory control," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    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. Saldanha-da-Gama, Francisco, 2022. "Facility Location in Logistics and Transportation: An enduring relationship," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
    2. Konstantinos Petridis & Garyfallos Arabatzis & Angelo Sifaleras, 2020. "Mathematical optimization models for fuelwood production," Annals of Operations Research, Springer, vol. 294(1), pages 59-74, November.
    3. Ardavan Babaei & Majid Khedmati & Mohammad Reza Akbari Jokar & Erfan Babaee Tirkolaee, 2022. "Performance Evaluation of Omni-Channel Distribution Network Configurations considering Green and Transparent Criteria under Uncertainty," Sustainability, MDPI, vol. 14(19), pages 1-15, October.
    4. Konstantinos Petridis & Prasanta Kumar Dey & Ali Emrouznejad, 2017. "A branch and efficiency algorithm for the optimal design of supply chain networks," Annals of Operations Research, Springer, vol. 253(1), pages 545-571, June.
    5. da Silva, Aneirson Francisco & Miranda, Rafael de Carvalho & Marins, Fernando Augusto Silva & Dias, Erica Ximenes, 2024. "A new multiple criteria data envelopment analysis with variable return to scale: Applying bi-dimensional representation and super-efficiency analysis," European Journal of Operational Research, Elsevier, vol. 314(1), pages 308-322.
    6. Liu, Liwei & Ye, Junhong & Zhao, Yufei & Zhao, Erdong, 2015. "The plight of the biomass power generation industry in China – A supply chain risk perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 680-692.
    7. Ghanei, Shima & Contreras, Ivan & Cordeau, Jean-François, 2023. "A two-stage stochastic collaborative intertwined supply network design problem under multiple disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 170(C).
    8. Zou, Qiling & Chen, Suren, 2019. "Enhancing resilience of interdependent traffic-electric power system," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    9. Antonio Zavala-Alcívar & María-José Verdecho & Juan-José Alfaro-Saiz, 2020. "A Conceptual Framework to Manage Resilience and Increase Sustainability in the Supply Chain," Sustainability, MDPI, vol. 12(16), pages 1-38, August.
    10. Ivanov, Dmitry & Dolgui, Alexandre & Sokolov, Boris, 2022. "Cloud supply chain: Integrating Industry 4.0 and digital platforms in the “Supply Chain-as-a-Service”," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    11. Rozhkov, Maxim & Ivanov, Dmitry & Blackhurst, Jennifer & Nair, Anand, 2022. "Adapting supply chain operations in anticipation of and during the COVID-19 pandemic," Omega, Elsevier, vol. 110(C).
    12. Khalilullah Mayar & David G. Carmichael & Xuesong Shen, 2022. "Resilience and Systems—A Review," Sustainability, MDPI, vol. 14(14), pages 1-22, July.
    13. Lorenzo Bruno Prataviera & Alessandro Creazza & Marco Melacini & Fabrizio Dallari, 2022. "Heading for Tomorrow: Resilience Strategies for Post-COVID-19 Grocery Supply Chains," Sustainability, MDPI, vol. 14(4), pages 1-17, February.
    14. Zhizhu Lai & Qun Yue & Zheng Wang & Dongmei Ge & Yulong Chen & Zhihong Zhou, 2022. "The min-p robust optimization approach for facility location problem under uncertainty," Journal of Combinatorial Optimization, Springer, vol. 44(2), pages 1134-1160, September.
    15. Samani, Mohammad Reza Ghatreh & Hosseini-Motlagh, Seyyed-Mahdi & Homaei, Shamim, 2020. "A reactive phase against disruptions for designing a proactive platelet supply network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 140(C).
    16. Petridis, Konstantinos & Tampakoudis, Ioannis & Drogalas, George & Kiosses, Nikolaos, 2022. "A Support Vector Machine model for classification of efficiency: An application to M&A," Research in International Business and Finance, Elsevier, vol. 61(C).
    17. Sawik, Tadeusz, 2022. "Stochastic optimization of supply chain resilience under ripple effect: A COVID-19 pandemic related study," Omega, Elsevier, vol. 109(C).
    18. Clavijo-Buritica, Nicolás & Triana-Sanchez, Laura & Escobar, John Willmer, 2023. "A hybrid modeling approach for resilient agri-supply network design in emerging countries: Colombian coffee supply chain," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).
    19. Bo Yan & Zhuo Chen & Hongyuan Li, 2019. "Evaluation of agri-product supply chain competitiveness based on extension theory," Operational Research, Springer, vol. 19(2), pages 543-570, June.
    20. El Baz, Jamal & Ruel, Salomée, 2021. "Can supply chain risk management practices mitigate the disruption impacts on supply chains’ resilience and robustness? Evidence from an empirical survey in a COVID-19 outbreak era," International Journal of Production Economics, Elsevier, vol. 233(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:spr:annopr:v:321:y:2023:i:1:d:10.1007_s10479-022-05080-w. 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.