IDEAS home Printed from https://ideas.repec.org/a/pal/jorsoc/v59y2008i4d10.1057_palgrave.jors.2602370.html
   My bibliography  Save this article

Production planning under uncertainty in textile manufacturing

Author

Listed:
  • S Karabuk

    (University of Oklahoma)

Abstract

Textile manufacturing consists of yarn production, fabric formation, and finishing and dyeing stages. The subject of this paper is the yarn production planning problem, although the approach is directly applicable to the fabric production planning problem due to similarities in the respective models. Our experience at an international textile manufacturer indicates that demand uncertainty is a major challenge in developing yarn production plans. We develop a stochastic programming model that explicitly includes uncertainty in the form of discrete demand scenarios. This results in a large-scale mixed integer model that is difficult to solve with off-the-shelf commercial solvers. We develop a two-step preprocessing algorithm that improves the linear programming relaxation of the model and reduces its size, consequently improving the computational requirements. We illustrate the benefits of a stochastic programming approach over a deterministic model and share our initial application experience.

Suggested Citation

  • 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.
    • Handle: RePEc:pal:jorsoc:v:59:y:2008:i:4:d:10.1057_palgrave.jors.2602370
    DOI: 10.1057/palgrave.jors.2602370
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/palgrave.jors.2602370
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/palgrave.jors.2602370?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. K Darby-Dowman & S Barker & E Audsley & D Parsons, 2000. "A two-stage stochastic programming with recourse model for determining robust planting plans in horticulture," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(1), pages 83-89, January.
    2. G Barbarosoǧlu & Y Arda, 2004. "A two-stage stochastic programming framework for transportation planning in disaster response," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(1), pages 43-53, January.
    3. Suleyman Karabuk & S. David Wu, 2003. "Coordinating Strategic Capacity Planning in the Semiconductor Industry," Operations Research, INFORMS, vol. 51(6), pages 839-849, December.
    4. Gary D. Eppen & R. Kipp Martin & Linus Schrage, 1989. "OR Practice—A Scenario Approach to Capacity Planning," Operations Research, INFORMS, vol. 37(4), pages 517-527, August.
    5. M A H Dempster & N Hicks Pedrón & E A Medova & J E Scott & A Sembos, 2000. "Planning logistics operations in the oil industry," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(11), pages 1271-1288, November.
    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. 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).
    2. Palmowski, Zbigniew & Sidorowicz, Aleksandra, 2020. "An application of dynamic programming to assign pressing tanks at wineries," European Journal of Operational Research, Elsevier, vol. 287(1), pages 293-305.
    3. 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.
    4. M Kazemi Zanjani & M Nourelfath & D Ait-Kadi, 2011. "Production planning with uncertainty in the quality of raw materials: a case in sawmills," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(7), pages 1334-1343, July.
    5. Körpeoglu, Ersin & Yaman, Hande & Selim Aktürk, M., 2011. "A multi-stage stochastic programming approach in master production scheduling," European Journal of Operational Research, Elsevier, vol. 213(1), pages 166-179, August.
    6. Bohle, Carlos & Maturana, Sergio & Vera, Jorge, 2010. "A robust optimization approach to wine grape harvesting scheduling," European Journal of Operational Research, Elsevier, vol. 200(1), pages 245-252, January.

    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. Kai Huang & Shabbir Ahmed, 2009. "The Value of Multistage Stochastic Programming in Capacity Planning Under Uncertainty," Operations Research, INFORMS, vol. 57(4), pages 893-904, August.
    2. Van-Anh Truong & Robin O. Roundy, 2011. "Multidimensional Approximation Algorithms for Capacity-Expansion Problems," Operations Research, INFORMS, vol. 59(2), pages 313-327, April.
    3. Jakubovskis, Aldis, 2017. "Flexible production resources and capacity utilization rates: A robust optimization perspective," International Journal of Production Economics, Elsevier, vol. 189(C), pages 77-85.
    4. Bakker, Hannah & Dunke, Fabian & Nickel, Stefan, 2020. "A structuring review on multi-stage optimization under uncertainty: Aligning concepts from theory and practice," Omega, Elsevier, vol. 96(C).
    5. 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).
    6. Yang, Jiawei & Li, Yuanyu & Fang, Lei, 2023. "Financing capacity planning with environmental considerations: A non-parametric analysis," Omega, Elsevier, vol. 118(C).
    7. Fuyu Wang & Xuefei Ge & Yan Li & Jingjing Zheng & Weichen Zheng, 2023. "Optimising the Distribution of Multi-Cycle Emergency Supplies after a Disaster," Sustainability, MDPI, vol. 15(2), pages 1-26, January.
    8. Alqahtani, Mohammed & Hu, Mengqi, 2022. "Dynamic energy scheduling and routing of multiple electric vehicles using deep reinforcement learning," Energy, Elsevier, vol. 244(PA).
    9. Dilsu Binnaz Ozkapici & Mustafa Alp Ertem & Haluk Aygüneş, 2016. "Intermodal humanitarian logistics model based on maritime transportation in Istanbul," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 83(1), pages 345-364, August.
    10. Lu, Chung-Cheng & Ying, Kuo-Ching & Chen, Hui-Ju, 2016. "Real-time relief distribution in the aftermath of disasters – A rolling horizon approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 93(C), pages 1-20.
    11. Wilson, Duncan T. & Hawe, Glenn I. & Coates, Graham & Crouch, Roger S., 2013. "A multi-objective combinatorial model of casualty processing in major incident response," European Journal of Operational Research, Elsevier, vol. 230(3), pages 643-655.
    12. Rafiei, Rezvan & Huang, Kai & Verma, Manish, 2022. "Cash versus in-kind transfer programs in humanitarian operations: An optimization program and a case study," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    13. P. Daniel Wright & Matthew J. Liberatore & Robert L. Nydick, 2006. "A Survey of Operations Research Models and Applications in Homeland Security," Interfaces, INFORMS, vol. 36(6), pages 514-529, December.
    14. Yagci Sokat, Kezban & Dolinskaya, Irina S. & Smilowitz, Karen & Bank, Ryan, 2018. "Incomplete information imputation in limited data environments with application to disaster response," European Journal of Operational Research, Elsevier, vol. 269(2), pages 466-485.
    15. Pengxia Zhao & Tie Li & Biao Wang & Ming Li & Yu Wang & Xiahui Guo & Yue Yu, 2022. "The Scenario Construction and Evolution Method of Casualties in Liquid Ammonia Leakage Based on Bayesian Network," IJERPH, MDPI, vol. 19(24), pages 1-22, December.
    16. Eghbal Akhlaghi, Vahid & Campbell, Ann Melissa, 2022. "The two-echelon island fuel distribution problem," European Journal of Operational Research, Elsevier, vol. 302(3), pages 999-1017.
    17. Sabet, Ehsan & Yazdani, Baback & Kian, Ramez & Galanakis, Kostas, 2020. "A strategic and global manufacturing capacity management optimisation model: A Scenario-based multi-stage stochastic programming approach," Omega, Elsevier, vol. 93(C).
    18. Wei Zhang & Sriram Dasu & Reza Ahmadi, 2017. "Higher Prices for Larger Quantities? Nonmonotonic Price–Quantity Relations in B2B Markets," Management Science, INFORMS, vol. 63(7), pages 2108-2126, July.
    19. Nguyet Thi Tran & Dirk Weichgrebe, 2020. "Regional material flow behaviors of agro‐food processing craft villages in Red River Delta, Vietnam," Journal of Industrial Ecology, Yale University, vol. 24(3), pages 707-725, June.
    20. Hongmin Li & Stephen C. Graves & Woonghee Tim Huh, 2014. "Optimal Capacity Conversion for Product Transitions Under High Service Requirements," Manufacturing & Service Operations Management, INFORMS, vol. 16(1), pages 46-60, February.

    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:pal:jorsoc:v:59:y:2008:i:4:d:10.1057_palgrave.jors.2602370. 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.palgrave-journals.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.