IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v240y2016i2d10.1007_s10479-013-1421-5.html
   My bibliography  Save this article

A robust optimization model for agile and build-to-order supply chain planning under uncertainties

Author

Listed:
  • Morteza Lalmazloumian

    (Universiti Teknologi Malaysia)

  • Kuan Yew Wong

    (Universiti Teknologi Malaysia)

  • Kannan Govindan

    (University of Southern Denmark)

  • Devika Kannan

    (Aalborg University)

Abstract

Supply chain planning as one of the most important processes within the supply chain management concept, has a great impact on firms’ success or failure. This paper considers a supply chain planning problem of an agile manufacturing company operating in a build-to-order environment under various kinds of uncertainty. An integrated optimization approach of procurement, production and distribution costs associated with the supply chain members has been taken into account. A robust optimization scenario-based approach is used to absorb the influence of uncertain parameters and variables. The formulation is a robust optimization model with the objective of minimizing the expected total supply chain cost while maintaining customer service level. The developed multi-product, multi-period, multi-echelon robust mixed-integer linear programming model is then solved using the CPLEX optimization studio and guidance related to future areas of research is given.

Suggested Citation

  • Morteza Lalmazloumian & Kuan Yew Wong & Kannan Govindan & Devika Kannan, 2016. "A robust optimization model for agile and build-to-order supply chain planning under uncertainties," Annals of Operations Research, Springer, vol. 240(2), pages 435-470, May.
    • Handle: RePEc:spr:annopr:v:240:y:2016:i:2:d:10.1007_s10479-013-1421-5
    DOI: 10.1007/s10479-013-1421-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-013-1421-5
    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/s10479-013-1421-5?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. Feng, Yan & D'Amours, Sophie & Beauregard, Robert, 2008. "The value of sales and operations planning in oriented strand board industry with make-to-order manufacturing system: Cross functional integration under deterministic demand and spot market recourse," International Journal of Production Economics, Elsevier, vol. 115(1), pages 189-209, September.
    2. Shu-Hsien Liao & Chia-Lin Hsieh & Yu-Siang Lin, 2011. "A multi-objective evolutionary optimization approach for an integrated location-inventory distribution network problem under vendor-managed inventory systems," Annals of Operations Research, Springer, vol. 186(1), pages 213-229, June.
    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. Beamon, Benita M., 1998. "Supply chain design and analysis:: Models and methods," International Journal of Production Economics, Elsevier, vol. 55(3), pages 281-294, August.
    5. Yu, Chian-Son & Li, Han-Lin, 2000. "A robust optimization model for stochastic logistic problems," International Journal of Production Economics, Elsevier, vol. 64(1-3), pages 385-397, March.
    6. 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.
    7. Mula, J. & Poler, R. & Garcia-Sabater, J.P. & Lario, F.C., 2006. "Models for production planning under uncertainty: A review," International Journal of Production Economics, Elsevier, vol. 103(1), pages 271-285, September.
    8. Mohammadi Bidhandi, Hadi & Mohd. Yusuff, Rosnah & Megat Ahmad, Megat Mohamad Hamdan & Abu Bakar, Mohd Rizam, 2009. "Development of a new approach for deterministic supply chain network design," European Journal of Operational Research, Elsevier, vol. 198(1), pages 121-128, October.
    9. 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.
    10. Omar Ahumada & J. Villalobos, 2011. "A tactical model for planning the production and distribution of fresh produce," Annals of Operations Research, Springer, vol. 190(1), pages 339-358, October.
    11. Pyke, David F. & Cohen, Morris A., 1994. "Multiproduct integrated production--distribution systems," European Journal of Operational Research, Elsevier, vol. 74(1), pages 18-49, April.
    12. Petrovic, Dobrila & Roy, Rajat & Petrovic, Radivoj, 1998. "Modelling and simulation of a supply chain in an uncertain environment," European Journal of Operational Research, Elsevier, vol. 109(2), pages 299-309, September.
    13. Volling, Thomas & Spengler, Thomas S., 2011. "Modeling and simulation of order-driven planning policies in build-to-order automobile production," International Journal of Production Economics, Elsevier, vol. 131(1), pages 183-193, May.
    14. Wallace, Stein W. & Choi, Tsan-Ming, 2011. "Robust supply chain management," International Journal of Production Economics, Elsevier, vol. 134(2), pages 283-283, December.
    15. Katayama, Hiroshi & Bennett, David, 1999. "Agility, adaptability and leanness: A comparison of concepts and a study of practice," International Journal of Production Economics, Elsevier, vol. 60(1), pages 43-51, April.
    16. John M. Mulvey & Robert J. Vanderbei & Stavros A. Zenios, 1995. "Robust Optimization of Large-Scale Systems," Operations Research, INFORMS, vol. 43(2), pages 264-281, April.
    17. Choi, Tsan-Ming & Chow, Pui-Sze, 2008. "Mean-variance analysis of Quick Response Program," International Journal of Production Economics, Elsevier, vol. 114(2), pages 456-475, August.
    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. Surya Prakash & Sameer Kumar & Gunjan Soni & Vipul Jain & Ajay Pal Singh Rathore, 2020. "Closed-loop supply chain network design and modelling under risks and demand uncertainty: an integrated robust optimization approach," Annals of Operations Research, Springer, vol. 290(1), pages 837-864, July.
    2. Hêris Golpîra, 2017. "Robust bi-level optimization for an opportunistic supply chain network design problem in an uncertain and risky environment," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 27(1), pages 21-41.
    3. Soheyl Khalilpourazari & Saman Khalilpourazary & Aybike Özyüksel Çiftçioğlu & Gerhard-Wilhelm Weber, 2021. "Designing energy-efficient high-precision multi-pass turning processes via robust optimization and artificial intelligence," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1621-1647, August.
    4. Yarong Chen & Hongming Zhou & Peiyu Huang & FuhDer Chou & Shenquan Huang, 2022. "A refined order release method for achieving robustness of non-repetitive dynamic manufacturing system performance," Annals of Operations Research, Springer, vol. 311(1), pages 65-79, April.
    5. Jabbarzadeh, Armin & Haughton, Michael & Pourmehdi, Fahime, 2019. "A robust optimization model for efficient and green supply chain planning with postponement strategy," International Journal of Production Economics, Elsevier, vol. 214(C), pages 266-283.
    6. 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.
    7. Sazvar, Z. & Mirzapour Al-e-hashem, S.M.J. & Govindan, K. & Bahli, B., 2016. "A novel mathematical model for a multi-period, multi-product optimal ordering problem considering expiry dates in a FEFO system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 93(C), pages 232-261.
    8. Tsan-Ming Choi & Kannan Govindan & Xiang Li & Yongjian Li, 2017. "Innovative supply chain optimization models with multiple uncertainty factors," Annals of Operations Research, Springer, vol. 257(1), pages 1-14, October.
    9. Diéssica Oliveira-Dias & José Moyano-Fuentes & Juan Manuel Maqueira-Marín, 2022. "Understanding the relationships between information technology and lean and agile supply chain strategies: a systematic literature review," Annals of Operations Research, Springer, vol. 312(2), pages 973-1005, May.
    10. Amirhosein Gholami & Nasim Nezamoddini & Mohammad T. Khasawneh, 2023. "Customized orders management in connected make-to-order supply chains," Operations Management Research, Springer, vol. 16(3), pages 1428-1443, September.
    11. Na Liu & Pui-Sze Chow & Hongshan Zhao, 2020. "Challenges and critical successful factors for apparel mass customization operations: recent development and case study," Annals of Operations Research, Springer, vol. 291(1), pages 531-563, August.
    12. Issam Laguir & Sachin Modgil & Indranil Bose & Shivam Gupta & Rebecca Stekelorum, 2023. "Performance effects of analytics capability, disruption orientation, and resilience in the supply chain under environmental uncertainty," Annals of Operations Research, Springer, vol. 324(1), pages 1269-1293, May.
    13. Andrew J. Keith & Darryl K. Ahner, 2021. "A survey of decision making and optimization under uncertainty," Annals of Operations Research, Springer, vol. 300(2), pages 319-353, May.
    14. Andrea Borenich & Peter Greistorfer & Marc Reimann, 2020. "Model-based production cost estimation to support bid processes: an automotive case study," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(3), pages 841-868, September.
    15. Diabat, Ali & Jabbarzadeh, Armin & Khosrojerdi, Amir, 2019. "A perishable product supply chain network design problem with reliability and disruption considerations," International Journal of Production Economics, Elsevier, vol. 212(C), pages 125-138.
    16. Lin Chen & Ting Dong & Jin Peng & Dan Ralescu, 2023. "Uncertainty Analysis and Optimization Modeling with Application to Supply Chain Management: A Systematic Review," Mathematics, MDPI, vol. 11(11), pages 1-45, May.
    17. Morteza Lalmazloumian & M. Fazle Baki & Majid Ahmadi, 2023. "A two-stage stochastic optimization framework to allocate operating room capacity in publicly-funded hospitals under uncertainty," Health Care Management Science, Springer, vol. 26(2), pages 238-260, June.

    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. 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.
    2. 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).
    3. Julian Englberger & Frank Herrmann & Michael Manitz, 2016. "Two-stage stochastic master production scheduling under demand uncertainty in a rolling planning environment," International Journal of Production Research, Taylor & Francis Journals, vol. 54(20), pages 6192-6215, October.
    4. Aalaei, Amin & Davoudpour, Hamid, 2017. "A robust optimization model for cellular manufacturing system into supply chain management," International Journal of Production Economics, Elsevier, vol. 183(PC), pages 667-679.
    5. Shuihua Han & Weina Ma & Ling Zhao & Xuelian Zhang & Ming K. Lim & Shuangyuan Yang & Stephen Leung, 2016. "A robust optimisation model for hybrid remanufacturing and manufacturing systems under uncertain return quality and market demand," International Journal of Production Research, Taylor & Francis Journals, vol. 54(17), pages 5056-5072, September.
    6. 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.
    7. Baghalian, Atefeh & Rezapour, Shabnam & Farahani, Reza Zanjirani, 2013. "Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case," European Journal of Operational Research, Elsevier, vol. 227(1), pages 199-215.
    8. Meysam Hosseini & Arsalan Rahmani & F. Hooshmand, 2022. "A robust model for recharging station location problem," Operational Research, Springer, vol. 22(4), pages 4397-4440, September.
    9. Hashem Omrani & Farzane Adabi & Narges Adabi, 2017. "Designing an efficient supply chain network with uncertain data: a robust optimization—data envelopment analysis approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(7), pages 816-828, July.
    10. Shiva Zokaee & Armin Jabbarzadeh & Behnam Fahimnia & Seyed Jafar Sadjadi, 2017. "Robust supply chain network design: an optimization model with real world application," Annals of Operations Research, Springer, vol. 257(1), pages 15-44, October.
    11. Dehghani, Ehsan & Jabalameli, Mohammad Saeed & Jabbarzadeh, Armin, 2018. "Robust design and optimization of solar photovoltaic supply chain in an uncertain environment," Energy, Elsevier, vol. 142(C), pages 139-156.
    12. Jabbarzadeh, Armin & Haughton, Michael & Pourmehdi, Fahime, 2019. "A robust optimization model for efficient and green supply chain planning with postponement strategy," International Journal of Production Economics, Elsevier, vol. 214(C), pages 266-283.
    13. Xu, Y. & Huang, G.H. & Qin, X.S. & Cao, M.F., 2009. "SRCCP: A stochastic robust chance-constrained programming model for municipal solid waste management under uncertainty," Resources, Conservation & Recycling, Elsevier, vol. 53(6), pages 352-363.
    14. Xie, Y.L. & Huang, G.H. & Li, W. & Ji, L., 2014. "Carbon and air pollutants constrained energy planning for clean power generation with a robust optimization model—A case study of Jining City, China," Applied Energy, Elsevier, vol. 136(C), pages 150-167.
    15. Masoud Hekmatfar & M. R. M. Aliha & Mir Saman Pishvaee & Tomasz Sadowski, 2023. "A Robust Flexible Optimization Model for 3D-Layout of Interior Equipment in a Multi-Floor Satellite," Mathematics, MDPI, vol. 11(24), pages 1-41, December.
    16. Häntsch, Marius & Huchzermeier, Arnd, 2016. "Transparency of risk for global and complex network decisions in the automotive industry," International Journal of Production Economics, Elsevier, vol. 175(C), pages 81-95.
    17. Javid Jouzdani & Mohammad Fathian & Ahmad Makui & Mehdi Heydari, 2020. "Robust design and planning for a multi-mode multi-product supply network: a dairy industry case study," Operational Research, Springer, vol. 20(3), pages 1811-1840, September.
    18. Ratanakuakangwan, Sudlop & Morita, Hiroshi, 2021. "Hybrid stochastic robust optimization and robust optimization for energy planning – A social impact-constrained case study," Applied Energy, Elsevier, vol. 298(C).
    19. Jabbarzadeh, Armin & Fahimnia, Behnam & Seuring, Stefan, 2014. "Dynamic supply chain network design for the supply of blood in disasters: A robust model with real world application," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 70(C), pages 225-244.
    20. Pereira, Daniel Filipe & Oliveira, José Fernando & Carravilla, Maria Antónia, 2020. "Tactical sales and operations planning: A holistic framework and a literature review of decision-making models," International Journal of Production Economics, Elsevier, vol. 228(C).

    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:240:y:2016:i:2:d:10.1007_s10479-013-1421-5. 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.