IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v145y2013i1p139-149.html
   My bibliography  Save this article

Stochastic capacity planning and dynamic network design

Author

Listed:
  • Pimentel, Bruno S.
  • Mateus, Geraldo R.
  • Almeida, Franklin A.

Abstract

The present paper proposes a mathematical model and a solution approach to the Stochastic Capacity Planning and Dynamic Network Design Problem. Here, strategic decisions usually comprise developing the necessary capacity — through either incrementing capacity on existing assets (facilities or logistics channels) or establishing new capacity in the form of new assets — in order to satisfy increasing demand. Hence, throughout the planning horizon, decisions on which new assets to establish and where to increment capacity must be taken at minimal cost and in a timely manner. However, when demand varies nonmonotonically, decisions on which assets to temporarily shut down in times of decreasing demand and which of those to reopen when market conditions improve must also be taken into account. We propose a multi-stage stochastic mixed-integer programming approach to the problem as well as a Lagrangian Heuristic procedure to attain reasonably well bounded feasible solutions. The proposed method is evaluated in a Global Mining Supply Chain context which, due to the inherently large capital expenses, could have the outcome of its strategic decision making process significantly improved.

Suggested Citation

  • Pimentel, Bruno S. & Mateus, Geraldo R. & Almeida, Franklin A., 2013. "Stochastic capacity planning and dynamic network design," International Journal of Production Economics, Elsevier, vol. 145(1), pages 139-149.
  • Handle: RePEc:eee:proeco:v:145:y:2013:i:1:p:139-149
    DOI: 10.1016/j.ijpe.2013.01.019
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527313000376
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ijpe.2013.01.019?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. Schütz, Peter & Tomasgard, Asgeir & Ahmed, Shabbir, 2009. "Supply chain design under uncertainty using sample average approximation and dual decomposition," European Journal of Operational Research, Elsevier, vol. 199(2), pages 409-419, December.
    2. Panos Parpas & Berç Rustem, 2007. "Computational Assessment of Nested Benders and Augmented Lagrangian Decomposition for Mean-Variance Multistage Stochastic Problems," INFORMS Journal on Computing, INFORMS, vol. 19(2), pages 239-247, May.
    3. Azaron, A. & Brown, K.N. & Tarim, S.A. & Modarres, M., 2008. "A multi-objective stochastic programming approach for supply chain design considering risk," International Journal of Production Economics, Elsevier, vol. 116(1), pages 129-138, November.
    4. Tsiakis, Panagiotis & Papageorgiou, Lazaros G., 2008. "Optimal production allocation and distribution supply chain networks," International Journal of Production Economics, Elsevier, vol. 111(2), pages 468-483, February.
    5. Marshall L. Fisher, 1981. "The Lagrangian Relaxation Method for Solving Integer Programming Problems," Management Science, INFORMS, vol. 27(1), pages 1-18, January.
    6. Kavinesh J. Singh & Andy B. Philpott & R. Kevin Wood, 2009. "Dantzig-Wolfe Decomposition for Solving Multistage Stochastic Capacity-Planning Problems," Operations Research, INFORMS, vol. 57(5), pages 1271-1286, October.
    7. Yongpei Guan & Shabbir Ahmed & George L. Nemhauser, 2009. "Cutting Planes for Multistage Stochastic Integer Programs," Operations Research, INFORMS, vol. 57(2), pages 287-298, April.
    8. Shabbir Ahmed & Nikolaos V. Sahinidis, 2003. "An Approximation Scheme for Stochastic Integer Programs Arising in Capacity Expansion," Operations Research, INFORMS, vol. 51(3), pages 461-471, June.
    9. Santoso, Tjendera & Ahmed, Shabbir & Goetschalckx, Marc & Shapiro, Alexander, 2005. "A stochastic programming approach for supply chain network design under uncertainty," European Journal of Operational Research, Elsevier, vol. 167(1), pages 96-115, November.
    10. Melo, M.T. & Nickel, S. & Saldanha-da-Gama, F., 2009. "Facility location and supply chain management - A review," European Journal of Operational Research, Elsevier, vol. 196(2), pages 401-412, July.
    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. Crainic, Teodor Gabriel & Perboli, Guido & Rei, Walter & Rosano, Mariangela & Lerma, Veronica, 2024. "Capacity planning with uncertainty on contract fulfillment," European Journal of Operational Research, Elsevier, vol. 314(1), pages 152-175.
    2. 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).
    3. Clavijo López, Christian & Crama, Yves & Pironet, Thierry & Semet, Frédéric, 2024. "Multi-period distribution networks with purchase commitment contracts," European Journal of Operational Research, Elsevier, vol. 312(2), pages 556-572.
    4. Fattahi, Mohammad & Mosadegh, Hadi & Hasani, Aliakbar, 2021. "Sustainable planning in mining supply chains with renewable energy integration: A real-life case study," Resources Policy, Elsevier, vol. 74(C).
    5. Blossey, Gregor & Hahn, Gerd J. & Koberstein, Achim, 2022. "Planning pharmaceutical manufacturing networks in the light of uncertain production approval times," International Journal of Production Economics, Elsevier, vol. 244(C).
    6. Fattahi, Mohammad & Mahootchi, Masoud & Govindan, Kannan & Moattar Husseini, Seyed Mohammad, 2015. "Dynamic supply chain network design with capacity planning and multi-period pricing," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 81(C), pages 169-202.
    7. Lin, James T. & Chen, Tzu-Li & Chu, Hsiao-Ching, 2014. "A stochastic dynamic programming approach for multi-site capacity planning in TFT-LCD manufacturing under demand uncertainty," International Journal of Production Economics, Elsevier, vol. 148(C), pages 21-36.
    8. Govindan, Kannan & Fattahi, Mohammad, 2017. "Investigating risk and robustness measures for supply chain network design under demand uncertainty: A case study of glass supply chain," International Journal of Production Economics, Elsevier, vol. 183(PC), pages 680-699.
    9. Yang, Yi & Yuan, Quan & Xue, Weili & Zhou, Yun, 2014. "Analysis of batch ordering inventory models with setup cost and capacity constraint," International Journal of Production Economics, Elsevier, vol. 155(C), pages 340-350.
    10. M. Fattahi & M. Mahootchi & S. M. Moattar Husseini, 2016. "Integrated strategic and tactical supply chain planning with price-sensitive demands," Annals of Operations Research, Springer, vol. 242(2), pages 423-456, July.
    11. Ben Mohamed, Imen & Klibi, Walid & Sadykov, Ruslan & Şen, Halil & Vanderbeck, François, 2023. "The two-echelon stochastic multi-period capacitated location-routing problem," European Journal of Operational Research, Elsevier, vol. 306(2), pages 645-667.
    12. Sun, Yanshuo & Schonfeld, Paul, 2015. "Stochastic capacity expansion models for airport facilities," Transportation Research Part B: Methodological, Elsevier, vol. 80(C), pages 1-18.
    13. Ruozhen Qiu & Shunpeng Shi & Yue Sun, 2019. "A p -Robust Green Supply Chain Network Design Model under Uncertain Carbon Price and Demand," Sustainability, MDPI, vol. 11(21), pages 1-22, October.
    14. Zeng, Lanyan & Liu, Shi Qiang & Kozan, Erhan & Corry, Paul & Masoud, Mahmoud, 2021. "A comprehensive interdisciplinary review of mine supply chain management," Resources Policy, Elsevier, vol. 74(C).
    15. Jahani, Hamed & Abbasi, Babak & Sheu, Jiuh-Biing & Klibi, Walid, 2024. "Supply chain network design with financial considerations: A comprehensive review," European Journal of Operational Research, Elsevier, vol. 312(3), pages 799-839.
    16. Kargar, Bahareh & MohajerAnsari, Pedram & Esra Büyüktahtakın, İ. & Jahani, Hamed & Talluri, Sri, 2024. "Data-driven modeling for designing a sustainable and efficient vaccine supply chain: A COVID-19 case study," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 184(C).
    17. Menezes, Gustavo Campos & dos Santos Corrêa, Juliano, 2022. "Model and algorithms applied to Short-Term Integrated Programming Problem in Mines," Resources Policy, Elsevier, vol. 79(C).
    18. Majid Taghavi & Kai Huang, 2016. "A multi‐stage stochastic programming approach for network capacity expansion with multiple sources of capacity," Naval Research Logistics (NRL), John Wiley & Sons, vol. 63(8), pages 600-614, December.
    19. Sobhani, A. & Wahab, M.I.M. & Neumann, W.P., 2017. "Incorporating human factors-related performance variation in optimizing a serial system," European Journal of Operational Research, Elsevier, vol. 257(1), pages 69-83.
    20. Fattahi, Mohammad & Govindan, Kannan & Keyvanshokooh, Esmaeil, 2017. "Responsive and resilient supply chain network design under operational and disruption risks with delivery lead-time sensitive customers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 101(C), pages 176-200.

    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. Longinidis, Pantelis & Georgiadis, Michael C., 2014. "Integration of sale and leaseback in the optimal design of supply chain networks," Omega, Elsevier, vol. 47(C), pages 73-89.
    2. Roba W. Salem & Mohamed Haouari, 2017. "A simulation-optimisation approach for supply chain network design under supply and demand uncertainties," International Journal of Production Research, Taylor & Francis Journals, vol. 55(7), pages 1845-1861, April.
    3. Farahani, Reza Zanjirani & Rezapour, Shabnam & Drezner, Tammy & Fallah, Samira, 2014. "Competitive supply chain network design: An overview of classifications, models, solution techniques and applications," Omega, Elsevier, vol. 45(C), pages 92-118.
    4. Sahling, Florian & Kayser, Ariane, 2016. "Strategic supply network planning with vendor selection under consideration of risk and demand uncertainty," Omega, Elsevier, vol. 59(PB), pages 201-214.
    5. Fattahi, Mohammad & Govindan, Kannan & Keyvanshokooh, Esmaeil, 2017. "Responsive and resilient supply chain network design under operational and disruption risks with delivery lead-time sensitive customers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 101(C), pages 176-200.
    6. Nickel, Stefan & Saldanha-da-Gama, Francisco & Ziegler, Hans-Peter, 2012. "A multi-stage stochastic supply network design problem with financial decisions and risk management," Omega, Elsevier, vol. 40(5), pages 511-524.
    7. Govindan, Kannan & Fattahi, Mohammad, 2017. "Investigating risk and robustness measures for supply chain network design under demand uncertainty: A case study of glass supply chain," International Journal of Production Economics, Elsevier, vol. 183(PC), pages 680-699.
    8. Rezapour, Shabnam & Allen, Janet K. & Mistree, Farrokh, 2015. "Uncertainty propagation in a supply chain or supply network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 73(C), pages 185-206.
    9. Blossey, Gregor & Hahn, Gerd J. & Koberstein, Achim, 2022. "Planning pharmaceutical manufacturing networks in the light of uncertain production approval times," International Journal of Production Economics, Elsevier, vol. 244(C).
    10. Mingqiang Yin & Min Huang & Xiaohu Qian & Dazhi Wang & Xingwei Wang & Loo Hay Lee, 2023. "Fourth-party logistics network design with service time constraint under stochastic demand," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1203-1227, March.
    11. Zhen, Lu & He, Xueting & Zhuge, Dan & Wang, Shuaian, 2024. "Primal decomposition for berth planning under uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 183(C).
    12. M. Melo & S. Nickel & F. Saldanha-da-Gama, 2014. "An efficient heuristic approach for a multi-period logistics network redesign problem," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(1), pages 80-108, April.
    13. Ben Mohamed, Imen & Klibi, Walid & Sadykov, Ruslan & Şen, Halil & Vanderbeck, François, 2023. "The two-echelon stochastic multi-period capacitated location-routing problem," European Journal of Operational Research, Elsevier, vol. 306(2), pages 645-667.
    14. Snoeck, André & Udenio, Maximiliano & Fransoo, Jan C., 2019. "A stochastic program to evaluate disruption mitigation investments in the supply chain," European Journal of Operational Research, Elsevier, vol. 274(2), pages 516-530.
    15. Fattahi, Mohammad & Mosadegh, Hadi & Hasani, Aliakbar, 2021. "Sustainable planning in mining supply chains with renewable energy integration: A real-life case study," Resources Policy, Elsevier, vol. 74(C).
    16. Huang, Edward & Goetschalckx, Marc, 2014. "Strategic robust supply chain design based on the Pareto-optimal tradeoff between efficiency and risk," European Journal of Operational Research, Elsevier, vol. 237(2), pages 508-518.
    17. Nezamoddini, Nasim & Gholami, Amirhosein & Aqlan, Faisal, 2020. "A risk-based optimization framework for integrated supply chains using genetic algorithm and artificial neural networks," International Journal of Production Economics, Elsevier, vol. 225(C).
    18. Mohammad Fattahi, 2020. "A data-driven approach for supply chain network design under uncertainty with consideration of social concerns," Annals of Operations Research, Springer, vol. 288(1), pages 265-284, May.
    19. Jahani, Hamed & Abbasi, Babak & Sheu, Jiuh-Biing & Klibi, Walid, 2024. "Supply chain network design with financial considerations: A comprehensive review," European Journal of Operational Research, Elsevier, vol. 312(3), pages 799-839.
    20. Mohammaddust, Faeghe & Rezapour, Shabnam & Farahani, Reza Zanjirani & Mofidfar, Mohammad & Hill, Alex, 2017. "Developing lean and responsive supply chains: A robust model for alternative risk mitigation strategies in supply chain designs," International Journal of Production Economics, Elsevier, vol. 183(PC), pages 632-653.

    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:eee:proeco:v:145:y:2013:i:1:p:139-149. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    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.