IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v218y2019icp212-227.html
   My bibliography  Save this article

An integrated multi-echelon robust closed- loop supply chain under imperfect quality production

Author

Listed:
  • Almaraj, Ismail I.
  • Trafalis, Theodore B.

Abstract

In this paper, we consider a novel closed loop supply chain design consisting of multiple periods and multiple echelons. The models are considered under imperfect quality production with multiple uncertainties to provide meaningful solutions to practical problems. In addition, we assume that the screening is not always perfect, and inspection errors are more likely to take place in practice. We measure the amount of quality loss as conforming products deviate from the specification (target) value. In our study, we develop three robust counterparts models based on box, polyhedral, and combined interval and polyhedral uncertainty sets. We utilize different a priori probability bounds to approximate probabilistic constraints and provide a safe solution. The objective is to minimize the total cost of the supply chain network. Finally, numerical examples are provided to illustrate the proposed models. The paper is expected to provide more insights in managing this important problem.

Suggested Citation

  • Almaraj, Ismail I. & Trafalis, Theodore B., 2019. "An integrated multi-echelon robust closed- loop supply chain under imperfect quality production," International Journal of Production Economics, Elsevier, vol. 218(C), pages 212-227.
    • Handle: RePEc:eee:proeco:v:218:y:2019:i:c:p:212-227
    DOI: 10.1016/j.ijpe.2019.04.035
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527319301641
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijpe.2019.04.035?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. Rad, M. A. & Khoshalhan, F. & Glock, C. H., 2014. "Optimizing inventory and sales decisions in a two-stage supply chain with imperfect quality products and backorders," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 65264, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    2. 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.
    3. Bairamzadeh, Samira & Saidi-Mehrabad, Mohammad & Pishvaee, Mir Saman, 2018. "Modelling different types of uncertainty in biofuel supply network design and planning: A robust optimization approach," Renewable Energy, Elsevier, vol. 116(PA), pages 500-517.
    4. ,, 2000. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 16(2), pages 287-299, April.
    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. A. L. Soyster, 1973. "Technical Note—Convex Programming with Set-Inclusive Constraints and Applications to Inexact Linear Programming," Operations Research, INFORMS, vol. 21(5), pages 1154-1157, October.
    7. A. Ben-Tal & A. Nemirovski, 1998. "Robust Convex Optimization," Mathematics of Operations Research, INFORMS, vol. 23(4), pages 769-805, November.
    8. 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.
    9. 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.
    10. Dimitris Bertsimas & Melvyn Sim, 2004. "The Price of Robustness," Operations Research, INFORMS, vol. 52(1), pages 35-53, February.
    11. Dimitris Bertsimas & Aurélie Thiele, 2006. "A Robust Optimization Approach to Inventory Theory," Operations Research, INFORMS, vol. 54(1), pages 150-168, February.
    12. Khan, M. & Jaber, M.Y. & Guiffrida, A.L. & Zolfaghari, S., 2011. "A review of the extensions of a modified EOQ model for imperfect quality items," International Journal of Production Economics, Elsevier, vol. 132(1), pages 1-12, July.
    13. Aharon, Ben-Tal & Boaz, Golany & Shimrit, Shtern, 2009. "Robust multi-echelon multi-period inventory control," European Journal of Operational Research, Elsevier, vol. 199(3), pages 922-935, December.
    14. Wang, Dan & Qin, Zhongfeng & Kar, Samarjit, 2015. "A novel single-period inventory problem with uncertain random demand and its application," Applied Mathematics and Computation, Elsevier, vol. 269(C), pages 133-145.
    15. Wei, Cansheng & Li, Yongjian & Cai, Xiaoqiang, 2011. "Robust optimal policies of production and inventory with uncertain returns and demand," International Journal of Production Economics, Elsevier, vol. 134(2), pages 357-367, December.
    16. Masih-Tehrani, Behdad & Xu, Susan H. & Kumara, Soundar & Li, Haijun, 2011. "A single-period analysis of a two-echelon inventory system with dependent supply uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 45(8), pages 1128-1151, September.
    17. Lee, Chang Hwan & Rhee, Byong-Duk & Cheng, T.C.E., 2013. "Quality uncertainty and quality-compensation contract for supply chain coordination," European Journal of Operational Research, Elsevier, vol. 228(3), pages 582-591.
    18. 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.
    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. Ismail I. Almaraj & Theodore B. Trafalis, 2022. "A robust optimization approach in a multi-objective closed-loop supply chain model under imperfect quality production," Annals of Operations Research, Springer, vol. 319(2), pages 1479-1505, December.
    2. Ali Pedram & Shahryar Sorooshian & Freselam Mulubrhan & Afshin Abbaspour, 2023. "Incorporating Vehicle-Routing Problems into a Closed-Loop Supply Chain Network Using a Mixed-Integer Linear-Programming Model," Sustainability, MDPI, vol. 15(4), pages 1-24, February.
    3. Simonetto, Marco & Sgarbossa, Fabio & Battini, Daria & Govindan, Kannan, 2022. "Closed loop supply chains 4.0: From risks to benefits through advanced technologies. A literature review and research agenda," International Journal of Production Economics, Elsevier, vol. 253(C).

    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. 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.
    2. Antonio G. Martín & Manuel Díaz-Madroñero & Josefa Mula, 2020. "Master production schedule using robust optimization approaches in an automobile second-tier supplier," 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(1), pages 143-166, March.
    3. Oğuz Solyalı & Jean-François Cordeau & Gilbert Laporte, 2016. "The Impact of Modeling on Robust Inventory Management Under Demand Uncertainty," Management Science, INFORMS, vol. 62(4), pages 1188-1201, April.
    4. Ghazaleh Ahmadi & Reza Tavakkoli-Moghaddam & Armand Baboli & Mehdi Najafi, 2022. "A decision support model for robust allocation and routing of search and rescue resources after earthquake: a case study," Operational Research, Springer, vol. 22(2), pages 1039-1081, April.
    5. Shin, Youngchul & Lee, Sangyoon & Moon, Ilkyeong, 2021. "Robust multiperiod inventory model with a new type of buy one get one promotion: “My Own Refrigerator”," Omega, Elsevier, vol. 99(C).
    6. 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.
    7. Roya Soltani & Seyed J Sadjadi, 2014. "Reliability optimization through robust redundancy allocation models with choice of component type under fuzziness," Journal of Risk and Reliability, , vol. 228(5), pages 449-459, October.
    8. Oğuz Solyalı & Jean-François Cordeau & Gilbert Laporte, 2012. "Robust Inventory Routing Under Demand Uncertainty," Transportation Science, INFORMS, vol. 46(3), pages 327-340, August.
    9. Nikulin, Yury, 2006. "Robustness in combinatorial optimization and scheduling theory: An extended annotated bibliography," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 606, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    10. Varas, Mauricio & Maturana, Sergio & Pascual, Rodrigo & Vargas, Ignacio & Vera, Jorge, 2014. "Scheduling production for a sawmill: A robust optimization approach," International Journal of Production Economics, Elsevier, vol. 150(C), pages 37-51.
    11. 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.
    12. Shishebori, Davood & Yousefi Babadi, Abolghasem, 2015. "Robust and reliable medical services network design under uncertain environment and system disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 77(C), pages 268-288.
    13. Vahid Nazari-Ghanbarloo & Ali Ghodratnama, 2021. "Optimizing a robust tri-objective multi-period reliable supply chain network considering queuing system and operational and disruption risks," Operational Research, Springer, vol. 21(3), pages 1963-2020, September.
    14. Minjiao Zhang & Simge Küçükyavuz & Saumya Goel, 2014. "A Branch-and-Cut Method for Dynamic Decision Making Under Joint Chance Constraints," Management Science, INFORMS, vol. 60(5), pages 1317-1333, May.
    15. Xuejie Bai & Yankui Liu, 2016. "Robust optimization of supply chain network design in fuzzy decision system," Journal of Intelligent Manufacturing, Springer, vol. 27(6), pages 1131-1149, December.
    16. Hanks, Robert W. & Weir, Jeffery D. & Lunday, Brian J., 2017. "Robust goal programming using different robustness echelons via norm-based and ellipsoidal uncertainty sets," European Journal of Operational Research, Elsevier, vol. 262(2), pages 636-646.
    17. Roberto Gomes de Mattos & Fabricio Oliveira & Adriana Leiras & Abdon Baptista de Paula Filho & Paulo Gonçalves, 2019. "Robust optimization of the insecticide-treated bed nets procurement and distribution planning under uncertainty for malaria prevention and control," Annals of Operations Research, Springer, vol. 283(1), pages 1045-1078, December.
    18. Donya Rahmani & Arash Zandi & Sara Behdad & Arezou Entezaminia, 2021. "A light robust model for aggregate production planning with consideration of environmental impacts of machines," Operational Research, Springer, vol. 21(1), pages 273-297, March.
    19. Cleber D. Rocco & Reinaldo Morabito, 2016. "Robust optimisation approach applied to the analysis of production / logistics and crop planning in the tomato processing industry," International Journal of Production Research, Taylor & Francis Journals, vol. 54(19), pages 5842-5861, October.
    20. Jiankun Sun & Jan A. Van Mieghem, 2019. "Robust Dual Sourcing Inventory Management: Optimality of Capped Dual Index Policies and Smoothing," Manufacturing & Service Operations Management, INFORMS, vol. 21(4), pages 912-931, October.

    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:218:y:2019:i:c:p:212-227. 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.