IDEAS home Printed from https://ideas.repec.org/p/iim/iimawp/12882.html
   My bibliography  Save this paper

A Multi-Period Two Stage Stochastic Programming Based Decision Support System for Strategic Planning in Process Industries: A Case of an Integrated Iron and Steel Company

Author

Listed:
  • Gupta, Narain
  • Dutta, Goutam
  • Fourer, Robert

Abstract

The paper introduces the application of a generic, multiple period, two stage stochastic programming based Decision Support System (DSS) in an integrated steel company. We demonstrate that a generic, user friendly stochastic optimization based DSS can be used for planning in a probabilistic demand situation. We conduct a set of experiments based on the stochastic variability of the demand of finished steel. A two stage stochastic programming with recourse model is implemented in the DSS, and tested with real data from a steel company in North America. This application demonstrates the need for stochastic optimization in the process industry. The value of stochastic solution resulted from the implementation of steel company real data in the DSS is 1.61%.

Suggested Citation

  • Gupta, Narain & Dutta, Goutam & Fourer, Robert, 2014. "A Multi-Period Two Stage Stochastic Programming Based Decision Support System for Strategic Planning in Process Industries: A Case of an Integrated Iron and Steel Company," IIMA Working Papers WP2014-04-04, Indian Institute of Management Ahmedabad, Research and Publication Department.
    • Handle: RePEc:iim:iimawp:12882
    as

    Download full text from publisher

    File URL: https://www.iima.ac.in/sites/default/files/rnpfiles/14350561982014-04-04.pdf
    File Function: English Version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. S C H Leung & Y Wu & K K Lai, 2006. "A stochastic programming approach for multi-site aggregate production planning," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(2), pages 123-132, February.
    2. G Dutta & N Gupta & R Fourer, 2011. "An optimization-based decision support system for strategic planning in a process industry: the case of aluminium company in India," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(4), pages 616-626, April.
    3. Summerfield, Nichalin S. & Dror, Moshe, 2013. "Biform game: Reflection as a stochastic programming problem," International Journal of Production Economics, Elsevier, vol. 142(1), pages 124-129.
    4. Guan, Z. & Philpott, A.B., 2011. "A multistage stochastic programming model for the New Zealand dairy industry," International Journal of Production Economics, Elsevier, vol. 134(2), pages 289-299, December.
    5. George B. Dantzig, 1955. "Linear Programming under Uncertainty," Management Science, INFORMS, vol. 1(3-4), pages 197-206, 04-07.
    6. John M. Mulvey & Hercules Vladimirou, 1992. "Stochastic Network Programming for Financial Planning Problems," Management Science, INFORMS, vol. 38(11), pages 1642-1664, November.
    7. Sodhi, ManMohan S. & Tang, Christopher S., 2009. "Modeling supply-chain planning under demand uncertainty using stochastic programming: A survey motivated by asset-liability management," International Journal of Production Economics, Elsevier, vol. 121(2), pages 728-738, October.
    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. João Flávio de Freitas Almeida & Samuel Vieira Conceição & Luiz Ricardo Pinto & Ricardo Saraiva de Camargo & Gilberto de Miranda Júnior, 2018. "Flexibility evaluation of multiechelon supply chains," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-27, March.
    2. Andrea Beltratti & Andrea Consiglio & Stavros Zenios, 1999. "Scenario modeling for the management ofinternational bond portfolios," Annals of Operations Research, Springer, vol. 85(0), pages 227-247, January.
    3. Yueyue Fan & Changzheng Liu, 2010. "Solving Stochastic Transportation Network Protection Problems Using the Progressive Hedging-based Method," Networks and Spatial Economics, Springer, vol. 10(2), pages 193-208, June.
    4. Sodhi, ManMohan S. & Tang, Christopher S., 2009. "Modeling supply-chain planning under demand uncertainty using stochastic programming: A survey motivated by asset-liability management," International Journal of Production Economics, Elsevier, vol. 121(2), pages 728-738, October.
    5. Shih-Pin Chen & Wen-Lung Huang, 2014. "Solving Fuzzy Multiproduct Aggregate Production Planning Problems Based on Extension Principle," International Journal of Mathematics and Mathematical Sciences, Hindawi, vol. 2014, pages 1-18, August.
    6. Chen, Chien-Wei & Fan, Yueyue, 2012. "Bioethanol supply chain system planning under supply and demand uncertainties," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 150-164.
    7. Tang, Christopher S. & Davarzani, Hoda & Sarkis, Joseph, 2015. "Quantitative models for managing supply chain risks: A reviewAuthor-Name: Fahimnia, Behnam," European Journal of Operational Research, Elsevier, vol. 247(1), pages 1-15.
    8. Xide Zhu & Peijun Guo, 2020. "Bilevel programming approaches to production planning for multiple products with short life cycles," 4OR, Springer, vol. 18(2), pages 151-175, June.
    9. 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.
    10. M S Sodhi & C S Tang, 2011. "Determining supply requirement in the sales-and-operations-planning (S&OP) process under demand uncertainty: a stochastic programming formulation and a spreadsheet implementation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 526-536, March.
    11. Gulpinar, Nalan & Rustem, Berc & Settergren, Reuben, 2004. "Simulation and optimization approaches to scenario tree generation," Journal of Economic Dynamics and Control, Elsevier, vol. 28(7), pages 1291-1315, April.
    12. Michael Freimer & Jeffrey Linderoth & Douglas Thomas, 2012. "The impact of sampling methods on bias and variance in stochastic linear programs," Computational Optimization and Applications, Springer, vol. 51(1), pages 51-75, January.
    13. Aghayi, Nazila & Maleki, Bentolhoda, 2016. "Efficiency measurement of DMUs with undesirable outputs under uncertainty based on the directional distance function: Application on bank industry," Energy, Elsevier, vol. 112(C), pages 376-387.
    14. 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.
    15. Wu, Dexiang & Wu, Desheng Dash, 2020. "A decision support approach for two-stage multi-objective index tracking using improved lagrangian decomposition," Omega, Elsevier, vol. 91(C).
    16. Kanudia, Amit & Shukla, PR, 1998. "Modelling of Uncertainties and Price Elastic Demands in Energy-environment Planning for India," Omega, Elsevier, vol. 26(3), pages 409-423, June.
    17. Fan, Yingjie & Schwartz, Frank & Voß, Stefan, 2017. "Flexible supply chain planning based on variable transportation modes," International Journal of Production Economics, Elsevier, vol. 183(PC), pages 654-666.
    18. van Beek, Andries & Malmberg, Benjamin & Borm, Peter & Quant, Marieke & Schouten, Jop, 2021. "Cooperation and Competition in Linear Production and Sequencing Processes," Discussion Paper 2021-011, Tilburg University, Center for Economic Research.
    19. R. Baldacci & M. Boschetti & N. Christofides & S. Christofides, 2009. "Exact methods for large-scale multi-period financial planning problems," Computational Management Science, Springer, vol. 6(3), pages 281-306, August.
    20. Arie M. C. A. Koster & Michael Poss, 2018. "Special issue on: robust combinatorial optimization," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 6(3), pages 207-209, September.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:iim:iimawp:12882. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/eciimin.html .

    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.