Skip to main content
SpringerLink
Log in

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

  • Original Research
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Ait-alla, A., Teucke, M., Lütjen, M., Beheshti-kashi, S., & Reza, H. (2014). Robust production planning in fashion apparel industry under demand uncertainty via conditional value at risk. Mathematical Problems in Engineering.. https://doi.org/10.1155/2014/901861.

    Article  Google Scholar 

  • Awudu, I., & Zhang, J. (2013). Stochastic production planning for a biofuel supply chain under demand and price uncertainties. Applied Energy, 103, 189–196.

    Article  Google Scholar 

  • Birge, J., & Louveaux, F. (1997). Introduction to stochastic programming. Berlin: Springer.

    Google Scholar 

  • Bonfill, A., Bagajewicz, M., Espun, A., & Puigjaner, L. (2004). Risk management in the scheduling of batch plants under uncertain market demand. Industrial and Engineering Chemistry Research, 43, 741–750.

    Article  Google Scholar 

  • Chen, C., & Lee, W. (2004). Multi-objective optimization of multi-echelon supply chain networks with uncertain product demands and prices. Computers and Chemical Engineering, 28, 1131–1144.

    Article  Google Scholar 

  • Chiu, C.-H., & Choi, T.-M. (2016). Supply chain risk analysis with mean-variance models: A technical review. Annals of Operations Research, 240(2), 489–507.

    Article  Google Scholar 

  • Chopra, S., & Meindl, P. (2010). Supply chain management: Strategy, planning, and operation (4th ed.). Upper Saddle River, NJ: Pearson Education, Inc.

    Google Scholar 

  • DuHadway, S., Carnovale, S. & Hazen, B., (2017). Understanding risk management for intentional supply chain disruptions: Risk detection, risk mitigation, and risk recovery. Annals of Operations Research, pp. 1–20. https://doi.org/10.1007/s10479-017-2452-0.

  • Esmaeilikia, M., et al. (2016). Tactical supply chain planning models with inherent flexibility: Definition and review. Annals of Operations Research, 244(2), 407–427.

    Article  Google Scholar 

  • Felfel, H., Ayadi, O., & Masmoudi, F. (2016a). A decision making approach for a multi-objective multi-site supply network planning problem. International Journal of Computer Integrated Manufacturing, 29(7), 754–767.

    Article  Google Scholar 

  • Felfel, H., Ayadi, O., & Masmoudi, F. (2016b). Multi-objective stochastic multi-site supply chain planning under demand uncertainty considering downside risk. Computers and Industrial Engineering, 102(2016), 268–279.

    Article  Google Scholar 

  • Gebreslassie, B. H., Yao, Y., & You, F. (2012). Design under uncertainty of hydrocarbon biorefinery supply chains: Multiobjective stochastic programming models, decomposition algorithm, and a comparison between CVaR and downside Risk. AIChE Journal, 58(7), 2155–2179.

    Article  Google Scholar 

  • Haimes, Y. Y., Lasdon, L. S., & Wismer, D. A. (1971). On a bicriterion formulation of the problems of integrated system identification and system optimization. IEEE Trans on Systems, Man and Cybernetics, 1, 296–297.

    Google Scholar 

  • Jatuphatwarodom, N., Jones, D.F. & Ouelhadj, D. (2018). A mixed-model multi-objective analysis of strategic supply chain decision support in the Thai silk industry. Annals of Operations Research. https://doi.org/10.1007/s10479-018-2774-6.

    Article  Google Scholar 

  • Karabuk, S. (2008). Production planning under uncertainty in textile manufacturing. Journal of the Operational Research Society, 59(4), 510–520.

    Article  Google Scholar 

  • Kong, W. M. (2008). A multi-stage stochastic linear programming model for apparel production planning. PhD thesis, City University of Hong Kong.

  • Leung, S. C. H., Tsang, S. O. S., Ng, W. L., & Wu, Y. (2007). A robust optimization model for multi-site production planning problem in an uncertain environment. European Journal of Operational Research, 181(1), 224–238.

    Article  Google Scholar 

  • Leung, S. C. H., Wu, Y., & Lai, K. K. (2003). Multi-site aggregate production planning with multiple objectives: A goal programming approach. Production Planning and Control: The Management of Operations, 14(5), 425–436.

    Article  Google Scholar 

  • Leung, S. C. H., Wu, Y., & Lai, K. K. (2005). A stochastic programming approach for multi-site aggregate production planning. Journal of the Operational Research Society, 57, 123–132.

    Article  Google Scholar 

  • Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91.

    Google Scholar 

  • Mok, P. Y., Cheung, T. Y., Wong, W. K., Leung, S. Y. S., & Fan, J. T. (2013). Intelligent production planning for complex garment manufacturing. Journal of Intelligent Manufacturing, 24, 133–145.

    Article  Google Scholar 

  • Ren, S., Chan, H.-L., & Ram, P. (2017). A comparative study on fashion demand forecasting models with multiple sources of uncertainty. Annals of Operations Research, 257(1–2), 335–355.

    Article  Google Scholar 

  • Rockafellar, R. T., & Uryasev, S. (2002). Conditional value-at-risk for general loss distributions. Journal of Banking Finance, 26, 1443–1471.

    Article  Google Scholar 

  • Safra, I. (2013). Vers une approche intégrée de planification de la production et de la distribution: Cas de l’industrie textile. PhD thesis, Central School of Paris.

  • Sarykalin, S., Serraino, G., & Uryasev, S. (2008). Value-at-risk vs. conditional value-at-risk in risk management and optimization. In Z.-L. Chen & S. Raghavan (eds.), Tutorials in operations research (pp. 270–294). INFORMS.

  • Toni, A. D., & Meneghetti, A. (2000). The production planning process for a network of Firms in the textile-apparel industry. International Journal of Production Economics, 65, 17–32.

    Article  Google Scholar 

  • Uryasev, S., & Rockafellar, R. T. (2001). Conditional value-at-risk: Optimization approach. Stochastic Optimization: Algorithms and Applications, 54, 411–435.

    Google Scholar 

  • Wang, R., & Fang, H. (2001). Aggregate production planning with multiple objectives in a fuzzy environment. European Journal of Operational Research, 133(3), 521–536.

    Article  Google Scholar 

Download references

Acknowledgements

Financial support received from the Mobility of researchers and research for the creation of value (MOBIDOC) is fully appreciated. LINDO Systems, Inc is also acknowledged for giving us a free educational research license of the extended version of LINGO 15.0 software package.

Author information

Authors and Affiliations

  1. Mechanics, Modeling and Production Laboratory, University of Sfax, National Engineering School of Sfax (ENIS), Road Soukra, 1173 - 3038, Sfax, Tunisia

    Houssem Felfel, Wafa Ben Yahia, Omar Ayadi & Faouzi Masmoudi

Authors
  1. Houssem Felfel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wafa Ben Yahia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Omar Ayadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Faouzi Masmoudi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Houssem Felfel.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Felfel, H., Yahia, W.B., Ayadi, O. et al. Stochastic multi-site supply chain planning in textile and apparel industry under demand and price uncertainties with risk aversion. Ann Oper Res 271, 551–574 (2018). https://doi.org/10.1007/s10479-018-2980-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-018-2980-2

Keywords

Search

Navigation