IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v348y2025i3d10.1007_s10479-023-05349-8.html
   My bibliography  Save this article

A new model for production and distribution planning based on data envelopment analysis with respect to traffic congestion, Blockchain technology and uncertain conditions

Author

Listed:
  • Ardavan Babaei

    (Sharif University of Technology)

  • Majid Khedmati

    (Sharif University of Technology)

  • Mohammad Reza Akbari Jokar

    (Sharif University of Technology)

Abstract

The production and distribution planning problem, where incomplete information from the manufacturer factories is available to the distributor, is prevalent in the real world. Yet, researchers have not focused sufficiently on this field of research. Therefore, our paper offers a two-level multi-objective optimization of production and distribution planning for the two-stage supply chain based on the Data Envelopment Analysis method, which minimizes both the cost and the traffic congestion caused by the establishment of distributor warehouses. In addition, transparency through the blockchain technology and uncertain conditions through stochastic and fuzzy programming are considered in the supply chain design. The proposed model is first transformed by using Karush–Kuhn–Tucker into single-level linear programming and then solved by fuzzy goal programming. Next, the obtained solutions are measured and sorted in terms of efficiency. In this regard, the solutions are categorized in terms of cost-based strategies, traffic congestion, demand satisfaction, and optimality. For the purpose of enabling comprehensive supply chain management, the solutions are aggregated according to various strategies and the results show that two solutions are the most efficient, one focusing on cost and the other focusing on traffic congestion.

Suggested Citation

  • Ardavan Babaei & Majid Khedmati & Mohammad Reza Akbari Jokar, 2025. "A new model for production and distribution planning based on data envelopment analysis with respect to traffic congestion, Blockchain technology and uncertain conditions," Annals of Operations Research, Springer, vol. 348(3), pages 1145-1181, May.
  • Handle: RePEc:spr:annopr:v:348:y:2025:i:3:d:10.1007_s10479-023-05349-8
    DOI: 10.1007/s10479-023-05349-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-023-05349-8
    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/s10479-023-05349-8?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

    for a different version of it.

    References listed on IDEAS

    as
    1. William W. Cooper & Lawrence M. Seiford & Joe Zhu (ed.), 2011. "Handbook on Data Envelopment Analysis," International Series in Operations Research and Management Science, Springer, number 978-1-4419-6151-8, December.
    2. Bai, Yun & Hwang, Taesung & Kang, Seungmo & Ouyang, Yanfeng, 2011. "Biofuel refinery location and supply chain planning under traffic congestion," Transportation Research Part B: Methodological, Elsevier, vol. 45(1), pages 162-175, January.
    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. Sara Saberi & Mahtab Kouhizadeh & Joseph Sarkis & Lejia Shen, 2019. "Blockchain technology and its relationships to sustainable supply chain management," International Journal of Production Research, Taylor & Francis Journals, vol. 57(7), pages 2117-2135, April.
    5. Mohsen Lashgari & Ata Taleizadeh & Abbas Ahmadi, 2016. "Partial up-stream advanced payment and partial down-stream delayed payment in a three-level supply chain," Annals of Operations Research, Springer, vol. 238(1), pages 329-354, March.
    6. Feng Yang & Dexiang Wu & Liang Liang & Gongbing Bi & Desheng Wu, 2011. "Supply chain DEA: production possibility set and performance evaluation model," Annals of Operations Research, Springer, vol. 185(1), pages 195-211, May.
    7. Mehrdad Mehrbod & Nan Tu & Lixin Miao & Dai Wenjing, 2012. "Interactive fuzzy goal programming for a multi-objective closed-loop logistics network," Annals of Operations Research, Springer, vol. 201(1), pages 367-381, December.
    8. Ahmad Rezaee & Farzad Dehghanian & Behnam Fahimnia & Benita Beamon, 2017. "Green supply chain network design with stochastic demand and carbon price," Annals of Operations Research, Springer, vol. 250(2), pages 463-485, March.
    9. Boussofiane, A. & Dyson, R. G. & Thanassoulis, E., 1991. "Applied data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 52(1), pages 1-15, May.
    10. Persson, Fredrik & Olhager, Jan, 2002. "Performance simulation of supply chain designs," International Journal of Production Economics, Elsevier, vol. 77(3), pages 231-245, June.
    11. Nishizaki, Ichiro & Hayashida, Tomohiro & Sekizaki, Shinya & Okabe, Junya, 2022. "Data envelopment analysis approaches for two-level production and distribution planning problems," European Journal of Operational Research, Elsevier, vol. 300(1), pages 255-268.
    12. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    13. Kshetri, Nir, 2021. "Blockchain and sustainable supply chain management in developing countries," International Journal of Information Management, Elsevier, vol. 60(C).
    14. Ayvaz, Berk & Bolat, Bersam & Aydın, Nezir, 2015. "Stochastic reverse logistics network design for waste of electrical and electronic equipment," Resources, Conservation & Recycling, Elsevier, vol. 104(PB), pages 391-404.
    15. Céline Gicquel & Jianqiang Cheng, 2018. "A joint chance-constrained programming approach for the single-item capacitated lot-sizing problem with stochastic demand," Annals of Operations Research, Springer, vol. 264(1), pages 123-155, May.
    16. Huang, Qian & Xu, Jiuping, 2020. "Bi-level multi-objective programming approach for carbon emission quota allocation towards co-combustion of coal and sewage sludge," Energy, Elsevier, vol. 211(C).
    17. Arpita Roy & Shib Sankar Sana & Kripasindhu Chaudhuri, 2018. "Optimal Pricing of competing retailers under uncertain demand-a two layer supply chain model," Annals of Operations Research, Springer, vol. 260(1), pages 481-500, January.
    18. Beamon, Benita M., 1998. "Supply chain design and analysis:: Models and methods," International Journal of Production Economics, Elsevier, vol. 55(3), pages 281-294, August.
    19. Khishtandar, Soheila, 2019. "Simulation based evolutionary algorithms for fuzzy chance-constrained biogas supply chain design," Applied Energy, Elsevier, vol. 236(C), pages 183-195.
    20. 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.
    21. Hong, Jae-Dong & Mwakalonge, Judith L., 2020. "Biofuel logistics network scheme design with combined data envelopment analysis approach," Energy, Elsevier, vol. 209(C).
    22. Liang Liang & Feng Yang & Wade Cook & Joe Zhu, 2006. "DEA models for supply chain efficiency evaluation," Annals of Operations Research, Springer, vol. 145(1), pages 35-49, July.
    23. V.G. Venkatesh & K. Kang & B. Wang & R.Y. Zhong & A. Zhang, 2020. "System Architecture for Blockchain Based Transparency of Supply Chain Social Sustainability," Post-Print hal-04457147, HAL.
    24. Sina Nayeri & Mahdieh Tavakoli & Mehrab Tanhaeean & Fariborz Jolai, 2022. "A robust fuzzy stochastic model for the responsive-resilient inventory-location problem: comparison of metaheuristic algorithms," Annals of Operations Research, Springer, vol. 315(2), pages 1895-1935, August.
    25. Quddus, Md Abdul & Chowdhury, Sudipta & Marufuzzaman, Mohammad & Yu, Fei & Bian, Linkan, 2018. "A two-stage chance-constrained stochastic programming model for a bio-fuel supply chain network," International Journal of Production Economics, Elsevier, vol. 195(C), pages 27-44.
    26. 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.
    27. Agrawal, Saurabh & Singh, Rajesh K. & Murtaza, Qasim, 2015. "A literature review and perspectives in reverse logistics," Resources, Conservation & Recycling, Elsevier, vol. 97(C), pages 76-92.
    28. Surya Prakash & Sameer Kumar & Gunjan Soni & Vipul Jain & Ajay Pal Singh Rathore, 2020. "Closed-loop supply chain network design and modelling under risks and demand uncertainty: an integrated robust optimization approach," Annals of Operations Research, Springer, vol. 290(1), pages 837-864, July.
    29. A. Charnes & W. W. Cooper, 1963. "Deterministic Equivalents for Optimizing and Satisficing under Chance Constraints," Operations Research, INFORMS, vol. 11(1), pages 18-39, February.
    30. Lozano, S., 2013. "DEA production games," European Journal of Operational Research, Elsevier, vol. 231(2), pages 405-413.
    31. Mohsen Lashgari & Ata Allah Taleizadeh & Abbas Ahmadi, 2016. "Partial up-stream advanced payment and partial down-stream delayed payment in a three-level supply chain," Annals of Operations Research, Springer, vol. 238(1), pages 329-354, March.
    32. Zhiyuan Wang & Zhiqiang (Eric) Zheng & Wei Jiang & Shaojie Tang, 2021. "Blockchain‐Enabled Data Sharing in Supply Chains: Model, Operationalization, and Tutorial," Production and Operations Management, Production and Operations Management Society, vol. 30(7), pages 1965-1985, July.
    33. 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.
    34. Lijo John & Anand Gurumurthy & Arqum Mateen & Gopalakrishnan Narayanamurthy, 2022. "Improving the coordination in the humanitarian supply chain: exploring the role of options contract," Annals of Operations Research, Springer, vol. 319(1), pages 15-40, December.
    35. Mula, Josefa & Peidro, David & Poler, Raul, 2010. "The effectiveness of a fuzzy mathematical programming approach for supply chain production planning with fuzzy demand," International Journal of Production Economics, Elsevier, vol. 128(1), pages 136-143, November.
    36. Tamiz, Mehrdad & Jones, Dylan & Romero, Carlos, 1998. "Goal programming for decision making: An overview of the current state-of-the-art," European Journal of Operational Research, Elsevier, vol. 111(3), pages 569-581, December.
    37. S. Khodaparasti & H. R. Maleki & S. Jahedi & M. E. Bruni & P. Beraldi, 2017. "Enhancing community based health programs in Iran: a multi-objective location-allocation model," Health Care Management Science, Springer, vol. 20(4), pages 485-499, December.
    38. Jacob Lohmer & Rainer Lasch, 2021. "Production planning and scheduling in multi-factory production networks: a systematic literature review," International Journal of Production Research, Taylor & Francis Journals, vol. 59(7), pages 2028-2054, April.
    39. Riccardo Aldrighetti & Ilenia Zennaro & Serena Finco & Daria Battini, 2019. "Healthcare Supply Chain Simulation with Disruption Considerations: A Case Study from Northern Italy," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 20(1), pages 81-102, December.
    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. Ching-Chin Chern & Tzi-Yuan Chou & Bo Hsiao, 2016. "Assessing the efficiency of supply chain scheduling algorithms using data envelopment analysis," Information Systems and e-Business Management, Springer, vol. 14(4), pages 823-856, November.
    2. 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.
    3. Somayeh Soheilirad & Kannan Govindan & Abbas Mardani & Edmundas Kazimieras Zavadskas & Mehrbakhsh Nilashi & Norhayati Zakuan, 2018. "Application of data envelopment analysis models in supply chain management: a systematic review and meta-analysis," Annals of Operations Research, Springer, vol. 271(2), pages 915-969, December.
    4. 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.
    5. Ang, Sheng & Liu, Pei & Yang, Feng, 2020. "Intra-Organizational and inter-organizational resource allocation in two-stage network systems," Omega, Elsevier, vol. 91(C).
    6. Li, Yongjun & Chen, Yao & Liang, Liang & Xie, Jianhui, 2012. "DEA models for extended two-stage network structures," Omega, Elsevier, vol. 40(5), pages 611-618.
    7. Petridis, Konstantinos & Malesios, Chrisovalantis & Arabatzis, Garyfallos & Thanassoulis, Emmanuel, 2013. "Efficiency analysis of forestry journals: Suggestions for improving journals’ quality," Journal of Informetrics, Elsevier, vol. 7(2), pages 505-521.
    8. Davide Lanfranchi & Laura Grassi, 2021. "Translating technological innovation into efficiency: the case of US public P&C insurance companies," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 11(4), pages 565-585, December.
    9. Athanasia Mavrommati & Alexandra Pliakoura, 2025. "Performance dynamics in Greek wine sector: a study of technical efficiency and strategic implications," Operational Research, Springer, vol. 25(1), pages 1-22, March.
    10. Halkos, George & Petrou, Kleoniki Natalia, 2018. "Assessment of national waste generation in EU Member States’ efficiency," MPRA Paper 84590, University Library of Munich, Germany.
    11. Tatiana Bencova & Andrea Bohacikova, 2022. "DEA in Performance Measurement of Two-Stage Processes: Comparative Overview of the Literature," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 5, pages 111-129.
    12. Sebastian Lozano & Belarmino Adenso-Diaz, 2018. "Network DEA-based biobjective optimization of product flows in a supply chain," Annals of Operations Research, Springer, vol. 264(1), pages 307-323, May.
    13. Rizwan Manzoor & B. S. Sahay & Sujeet Kumar Singh, 2025. "Blockchain technology in supply chain management: an organizational theoretic overview and research agenda," Annals of Operations Research, Springer, vol. 348(3), pages 1307-1354, May.
    14. Kao, Chiang, 2014. "Network data envelopment analysis: A review," European Journal of Operational Research, Elsevier, vol. 239(1), pages 1-16.
    15. Muhammad Imran & Muhammad Salman Habib & Amjad Hussain & Naveed Ahmed & Abdulrahman M. Al-Ahmari, 2020. "Inventory Routing Problem in Supply Chain of Perishable Products under Cost Uncertainty," Mathematics, MDPI, vol. 8(3), pages 1-29, March.
    16. Congcong Yang & Alfred Taudes & Guozhi Dong, 2017. "Efficiency analysis of European Freight Villages: three peers for benchmarking," 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. 25(1), pages 91-122, March.
    17. Qingxian An & Fanyong Meng & Sheng Ang & Xiaohong Chen, 2018. "A new approach for fair efficiency decomposition in two-stage structure system," Operational Research, Springer, vol. 18(1), pages 257-272, April.
    18. Jianhui Xie & Qiwei Xie & Yongjun Li & Liang Liang, 2021. "Solving data envelopment analysis models with sum-of-fractional objectives: a global optimal approach based on the multiparametric disaggregation technique," Annals of Operations Research, Springer, vol. 304(1), pages 453-480, September.
    19. Javad Gerami & Reza Kiani Mavi & Reza Farzipoor Saen & Neda Kiani Mavi, 2023. "A novel network DEA-R model for evaluating hospital services supply chain performance," Annals of Operations Research, Springer, vol. 324(1), pages 1041-1066, May.
    20. HATAMI-MARBINI, Adel & AGRELL, Per & AGHAYI, Nazila, 2013. "Imprecise data envelopment analysis for the two-stage process," LIDAM Discussion Papers CORE 2013004, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:348:y:2025:i:3:d:10.1007_s10479-023-05349-8. 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.