IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v271y2018i2d10.1007_s10479-018-2980-2.html
   My bibliography  Save this article

Stochastic multi-site supply chain planning in textile and apparel industry under demand and price uncertainties with risk aversion

Author

Listed:
  • Houssem Felfel

    (University of Sfax, National Engineering School of Sfax (ENIS))

  • Wafa Ben Yahia

    (University of Sfax, National Engineering School of Sfax (ENIS))

  • Omar Ayadi

    (University of Sfax, National Engineering School of Sfax (ENIS))

  • Faouzi Masmoudi

    (University of Sfax, National Engineering School of Sfax (ENIS))

Abstract

A multi-product, multi-period, multi-site supply chain production and transportation planning problem, in the textile and apparel industry, under demand and price uncertainties is considered in this paper. The problem is formulated using a two-stage stochastic programming model taking into account the production amount, the inventory and backorder levels as well as the amounts of products to be transported between the different plants and customers in each period. Risk management is addressed by incorporating a risk measure into the stochastic programming model as a second objective function, which leads to a multi-objective optimization model. The objectives aim to simultaneously maximize the expected net profit and minimize the financial risk measured. Two risk measures are compared: the conditional-value-at-risk and the downside risk. As the considered objective functions conflict with each other’s, the problem solution is a front of Pareto optimal robust alternatives, which represents the trade-off among the different objective functions. A case study using real data from textile and apparel industry in Tunisia is presented to illustrate the effectiveness of the proposed model and the robustness of the obtained solutions.

Suggested Citation

  • Houssem Felfel & Wafa Ben Yahia & Omar Ayadi & Faouzi Masmoudi, 2018. "Stochastic multi-site supply chain planning in textile and apparel industry under demand and price uncertainties with risk aversion," Annals of Operations Research, Springer, vol. 271(2), pages 551-574, December.
    • Handle: RePEc:spr:annopr:v:271:y:2018:i:2:d:10.1007_s10479-018-2980-2
    DOI: 10.1007/s10479-018-2980-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-018-2980-2
    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/s10479-018-2980-2?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. Masoud Esmaeilikia & Behnam Fahimnia & Joeseph Sarkis & Kannan Govindan & Arun Kumar & John Mo, 2016. "Tactical supply chain planning models with inherent flexibility: definition and review," Annals of Operations Research, Springer, vol. 244(2), pages 407-427, September.
    2. Natawat Jatuphatwarodom & Dylan F. Jones & Djamila Ouelhadj, 2018. "A mixed-model multi-objective analysis of strategic supply chain decision support in the Thai silk industry," Annals of Operations Research, Springer, vol. 267(1), pages 221-247, August.
    3. De Toni, Alberto & Meneghetti, Antonella, 2000. "The production planning process for a network of firms in the textile-apparel industry," International Journal of Production Economics, Elsevier, vol. 65(1), pages 17-32, April.
    4. S Karabuk, 2008. "Production planning under uncertainty in textile manufacturing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(4), pages 510-520, April.
    5. Awudu, Iddrisu & Zhang, Jun, 2013. "Stochastic production planning for a biofuel supply chain under demand and price uncertainties," Applied Energy, Elsevier, vol. 103(C), pages 189-196.
    6. Chun-Hung Chiu & Tsan-Ming Choi, 2016. "Supply chain risk analysis with mean-variance models: a technical review," Annals of Operations Research, Springer, vol. 240(2), pages 489-507, May.
    7. Shuyun Ren & Hau-Ling Chan & Pratibha Ram, 2017. "A Comparative Study on Fashion Demand Forecasting Models with Multiple Sources of Uncertainty," Annals of Operations Research, Springer, vol. 257(1), pages 335-355, October.
    8. Leung, Stephen C.H. & Tsang, Sally O.S. & Ng, W.L. & Wu, Yue, 2007. "A robust optimization model for multi-site production planning problem in an uncertain environment," European Journal of Operational Research, Elsevier, vol. 181(1), pages 224-238, August.
    9. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
    10. Wang, Reay-Chen & Fang, Hsiao-Hua, 2001. "Aggregate production planning with multiple objectives in a fuzzy environment," European Journal of Operational Research, Elsevier, vol. 133(3), pages 521-536, September.
    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. M. J. Hermoso-Orzáez & J. Garzón-Moreno, 2022. "Risk management methodology in the supply chain: a case study applied," Annals of Operations Research, Springer, vol. 313(2), pages 1051-1075, June.
    2. J. F. F. Almeida & S. V. Conceição & L. R. Pinto & B. R. P. Oliveira & L. F. Rodrigues, 2022. "Optimal sales and operations planning for integrated steel industries," Annals of Operations Research, Springer, vol. 315(2), pages 773-790, August.

    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. Li, Yanhai & Gu, Chaocheng & Ou, Jinwen, 2020. "Supporting a financially constrained supplier under spectral risk measures: The efficiency of buyer lending," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    2. Donya Rahmani & Arash Zandi & Sara Behdad & Arezou Entezaminia, 2021. "A light robust model for aggregate production planning with consideration of environmental impacts of machines," Operational Research, Springer, vol. 21(1), pages 273-297, March.
    3. Pereira, Daniel Filipe & Oliveira, José Fernando & Carravilla, Maria Antónia, 2023. "Design of a sales plan in a hybrid contractual and non-contractual context in a setting of limited capacity: A robust approach," International Journal of Production Economics, Elsevier, vol. 260(C).
    4. Jahani, Hamed & Abbasi, Babak & Alavifard, Farzad & Talluri, Srinivas, 2018. "Supply chain network redesign with demand and price uncertainty," International Journal of Production Economics, Elsevier, vol. 205(C), pages 287-312.
    5. Li, Bo & An, Si-min & Song, Dong-ping, 2018. "Selection of financing strategies with a risk-averse supplier in a capital-constrained supply chain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 118(C), pages 163-183.
    6. Mirzapour Al-e-hashem, S.M.J. & Malekly, H. & Aryanezhad, M.B., 2011. "A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty," International Journal of Production Economics, Elsevier, vol. 134(1), pages 28-42, November.
    7. Ren, Shuyun & Choi, Tsan-Ming & Lee, Ka-Man & Lin, Lei, 2020. "Intelligent service capacity allocation for cross-border-E-commerce related third-party-forwarding logistics operations: A deep learning approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 134(C).
    8. Behzadi, Golnar & O’Sullivan, Michael Justin & Olsen, Tava Lennon & Zhang, Abraham, 2018. "Agribusiness supply chain risk management: A review of quantitative decision models," Omega, Elsevier, vol. 79(C), pages 21-42.
    9. Shuihua Han & Yue Jiang & Ling Zhao & Stephen C. H. Leung & Zongwei Luo, 2020. "Weight reduction technology and supply chain network design under carbon emission restriction," Annals of Operations Research, Springer, vol. 290(1), pages 567-590, July.
    10. Yue Chen & Sai-Ho Chung & Shu Guo, 2020. "Franchising contracts in fashion supply chain operations: models, practices, and real case study," Annals of Operations Research, Springer, vol. 291(1), pages 83-128, August.
    11. Cui, Xueting & Zhu, Shushang & Sun, Xiaoling & Li, Duan, 2013. "Nonlinear portfolio selection using approximate parametric Value-at-Risk," Journal of Banking & Finance, Elsevier, vol. 37(6), pages 2124-2139.
    12. Zhi Chen & Melvyn Sim & Huan Xu, 2019. "Distributionally Robust Optimization with Infinitely Constrained Ambiguity Sets," Operations Research, INFORMS, vol. 67(5), pages 1328-1344, September.
    13. Dominique Guégan & Wayne Tarrant, 2012. "On the necessity of five risk measures," Annals of Finance, Springer, vol. 8(4), pages 533-552, November.
    14. Giovanni Masala & Filippo Petroni, 2023. "Drawdown risk measures for asset portfolios with high frequency data," Annals of Finance, Springer, vol. 19(2), pages 265-289, June.
    15. Ke Zhou & Jiangjun Gao & Duan Li & Xiangyu Cui, 2017. "Dynamic mean–VaR portfolio selection in continuous time," Quantitative Finance, Taylor & Francis Journals, vol. 17(10), pages 1631-1643, October.
    16. 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.
    17. Malavasi, Matteo & Ortobelli Lozza, Sergio & Trück, Stefan, 2021. "Second order of stochastic dominance efficiency vs mean variance efficiency," European Journal of Operational Research, Elsevier, vol. 290(3), pages 1192-1206.
    18. Rostagno, Luciano Martin, 2005. "Empirical tests of parametric and non-parametric Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) measures for the Brazilian stock market index," ISU General Staff Papers 2005010108000021878, Iowa State University, Department of Economics.
    19. Alois Pichler, 2013. "Premiums And Reserves, Adjusted By Distortions," Papers 1304.0490, arXiv.org.
    20. Alexander, Gordon J. & Baptista, Alexandre M. & Yan, Shu, 2013. "A comparison of the original and revised Basel market risk frameworks for regulating bank capital," Journal of Economic Behavior & Organization, Elsevier, vol. 85(C), pages 249-268.

    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:271:y:2018:i:2:d:10.1007_s10479-018-2980-2. 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.