IDEAS home Printed from https://ideas.repec.org/a/spr/coopap/v89y2024i2d10.1007_s10589-024-00593-5.html
   My bibliography  Save this article

Value of risk aversion in perishable products supply chain management

Author

Listed:
  • Soumya Ranjan Pathy

    (Clemson University)

  • Hamed Rahimian

    (Clemson University)

Abstract

In this paper, we study optimal procurement and inventory decisions for a supply chain with a single perishable product under demand uncertainty. To control risk, on the one hand, we use a risk-averse objective, and on the other hand, we utilize a chance constraint to satisfy demand with a high probability. We formulate the problem as a two-stage stochastic program with a chance constraint and risk-averse objective, where long-term decisions on pre-positioning products are made in the first stage, while recourse decisions on reallocation and emergency procurement are made in the second stage. To allow for different risk preferences, we incorporate conditional value-at-risk into the objective function and study its combination with the expectation or worst-case of the second-stage costs. To solve the resulting models, we develop various variants of the L-shaped method, based on dual and primal decomposition, and by leveraging the connection between the optimization of coherent risk measures and distributionally robust optimization. Through extensive numerical experiments, we demonstrate the value of risk aversion and present a comparative computational study on the performance of different algorithms.

Suggested Citation

  • Soumya Ranjan Pathy & Hamed Rahimian, 2024. "Value of risk aversion in perishable products supply chain management," Computational Optimization and Applications, Springer, vol. 89(2), pages 517-552, November.
    • Handle: RePEc:spr:coopap:v:89:y:2024:i:2:d:10.1007_s10589-024-00593-5
    DOI: 10.1007/s10589-024-00593-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10589-024-00593-5
    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/s10589-024-00593-5?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. Abe, Naohito & Moriguchi, Chiaki & Inakura, Noriko, 2014. "The Effects of Natural Disasters on Prices and Purchasing Behaviors: The Case of the Great East Japan Earthquake," RCESR Discussion Paper Series DP14-1, Research Center for Economic and Social Risks, Institute of Economic Research, Hitotsubashi University.
    2. GÜNLÜK, Oktay & POCHET, Yves, 2001. "Mixing mixed-integer inequalities," LIDAM Reprints CORE 1504, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Andrzej Ruszczyński & Alexander Shapiro, 2006. "Conditional Risk Mappings," Mathematics of Operations Research, INFORMS, vol. 31(3), pages 544-561, August.
    4. Xin Chen & Zhan Pang & Limeng Pan, 2014. "Coordinating Inventory Control and Pricing Strategies for Perishable Products," Operations Research, INFORMS, vol. 62(2), pages 284-300, April.
    5. Zhu, Xiaoyan & Cao, Yunzhi, 2021. "The optimal recovery-fund based strategy for uncertain supply chain disruptions: A risk-averse two-stage stochastic programming approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    6. Rahimian, Hamed & Bayraksan, Güzin & Homem-de-Mello, Tito, 2019. "Controlling risk and demand ambiguity in newsvendor models," European Journal of Operational Research, Elsevier, vol. 279(3), pages 854-868.
    7. Emily L. Tucker & Mark S. Daskin & Burgunda V. Sweet & Wallace J. Hopp, 2020. "Incentivizing resilient supply chain design to prevent drug shortages: policy analysis using two- and multi-stage stochastic programs," IISE Transactions, Taylor & Francis Journals, vol. 52(4), pages 394-412, April.
    8. Yi Yang & Youhua (Frank) Chen & Yun Zhou, 2014. "Coordinating Inventory Control and Pricing Strategies Under Batch Ordering," Operations Research, INFORMS, vol. 62(1), pages 25-37, February.
    9. Yuan Xu & Joseph Szmerekovsky, 2022. "A multi-product multi-period stochastic model for a blood supply chain considering blood substitution and demand uncertainty," Health Care Management Science, Springer, vol. 25(3), pages 441-459, September.
    10. Pauls-Worm, Karin G.J. & Hendrix, Eligius M.T. & Haijema, René & van der Vorst, Jack G.A.J., 2014. "An MILP approximation for ordering perishable products with non-stationary demand and service level constraints," International Journal of Production Economics, Elsevier, vol. 157(C), pages 133-146.
    11. Devapriya, Priyantha & Ferrell, William & Geismar, Neil, 2017. "Integrated production and distribution scheduling with a perishable product," European Journal of Operational Research, Elsevier, vol. 259(3), pages 906-916.
    12. John B. Jennings, 1973. "Blood Bank Inventory Control," Management Science, INFORMS, vol. 19(6), pages 637-645, February.
    13. Soysal, Mehmet & Bloemhof-Ruwaard, Jacqueline M. & Haijema, Rene & van der Vorst, Jack G.A.J., 2015. "Modeling an Inventory Routing Problem for perishable products with environmental considerations and demand uncertainty," International Journal of Production Economics, Elsevier, vol. 164(C), pages 118-133.
    14. H. Neil Geismar & Gilbert Laporte & Lei Lei & Chelliah Sriskandarajah, 2008. "The Integrated Production and Transportation Scheduling Problem for a Product with a Short Lifespan," INFORMS Journal on Computing, INFORMS, vol. 20(1), pages 21-33, February.
    15. Wang, Weiqiao & Yang, Kai & Yang, Lixing & Gao, Ziyou, 2021. "Two-stage distributionally robust programming based on worst-case mean-CVaR criterion and application to disaster relief management," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    16. Daniela Guericke & Ignacio Blanco & Juan M. Morales & Henrik Madsen, 2020. "A two-phase stochastic programming approach to biomass supply planning for combined heat and power plants," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 42(4), pages 863-900, December.
    17. Weini Zhang & Hamed Rahimian & Güzin Bayraksan, 2016. "Decomposition Algorithms for Risk-Averse Multistage Stochastic Programs with Application to Water Allocation under Uncertainty," INFORMS Journal on Computing, INFORMS, vol. 28(3), pages 385-404, August.
    18. Elçi, Özgün & Noyan, Nilay, 2018. "A chance-constrained two-stage stochastic programming model for humanitarian relief network design," Transportation Research Part B: Methodological, Elsevier, vol. 108(C), pages 55-83.
    19. A. Ben-Tal & A. Nemirovski, 1998. "Robust Convex Optimization," Mathematics of Operations Research, INFORMS, vol. 23(4), pages 769-805, November.
    20. Volha Yakavenka & Ioannis Mallidis & Dimitrios Vlachos & Eleftherios Iakovou & Zafeiriou Eleni, 2020. "Development of a multi-objective model for the design of sustainable supply chains: the case of perishable food products," Annals of Operations Research, Springer, vol. 294(1), pages 593-621, November.
    21. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
    22. Alem, Douglas & Clark, Alistair & Moreno, Alfredo, 2016. "Stochastic network models for logistics planning in disaster relief," European Journal of Operational Research, Elsevier, vol. 255(1), pages 187-206.
    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. Liu, Kanglin & Yang, Liu & Zhao, Yejia & Zhang, Zhi-Hai, 2023. "Multi-period stochastic programming for relief delivery considering evolving transportation network and temporary facility relocation/closure," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 180(C).
    2. Babaee, Sara & Araghi, Mojtaba & Rostami, Borzou, 2022. "Coordinating transportation and pricing policies for perishable products," Transportation Research Part B: Methodological, Elsevier, vol. 164(C), pages 105-125.
    3. Bian Liang & Dapeng Yang & Xinghong Qin & Teresa Tinta, 2019. "A Risk-Averse Shelter Location and Evacuation Routing Assignment Problem in an Uncertain Environment," IJERPH, MDPI, vol. 16(20), pages 1-28, October.
    4. Jin, Zhongyi & Ng, Kam K.H. & Zhang, Chenliang & Liu, Wei & Zhang, Fangni & Xu, Gangyan, 2024. "A risk-averse distributionally robust optimisation approach for drone-supported relief facility location problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).
    5. Tian, Xin & Jiang, Hailiang & Zhao, Xuan, 2024. "Product assortment and online sales in community group-buying channel: Evidence from a convenience store chain," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    6. Wang, Duo & Yang, Kai & Yang, Lixing & Dong, Jianjun, 2023. "Two-stage distributionally robust optimization for disaster relief logistics under option contract and demand ambiguity," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 170(C).
    7. Zhi Chen & Melvyn Sim & Huan Xu, 2019. "Distributionally Robust Optimization with Infinitely Constrained Ambiguity Sets," Operations Research, INFORMS, vol. 67(5), pages 1328-1344, September.
    8. Yuqian Xu & Lingjiong Zhu & Michael Pinedo, 2020. "Operational Risk Management: A Stochastic Control Framework with Preventive and Corrective Controls," Operations Research, INFORMS, vol. 68(6), pages 1804-1825, November.
    9. 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.
    10. Nan Yang & Renyu Zhang, 2022. "Dynamic pricing and inventory management in the presence of online reviews," Production and Operations Management, Production and Operations Management Society, vol. 31(8), pages 3180-3197, August.
    11. Sasanuma, Katsunobu & Delasay, Mohammad & Pitocco, Christine & Scheller-Wolf, Alan & Sexton, Thomas, 2022. "A marginal analysis framework to incorporate the externality effect of ordering perishables," Operations Research Perspectives, Elsevier, vol. 9(C).
    12. Gurkan, M. Edib & Tunc, Huseyin & Tarim, S. Armagan, 2022. "The joint stochastic lot sizing and pricing problem," Omega, Elsevier, vol. 108(C).
    13. Hu, Shaolong & Han, Chuanfeng & Dong, Zhijie Sasha & Meng, Lingpeng, 2019. "A multi-stage stochastic programming model for relief distribution considering the state of road network," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 64-87.
    14. Bachtenkirch, David & Bock, Stefan, 2022. "Finding efficient make-to-order production and batch delivery schedules," European Journal of Operational Research, Elsevier, vol. 297(1), pages 133-152.
    15. Arda, Yasemin & Cattaruzza, Diego & François, Véronique & Ogier, Maxime, 2024. "Home chemotherapy delivery: An integrated production scheduling and multi-trip vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 317(2), pages 468-486.
    16. Boxiao Chen & Xiuli Chao & Cong Shi, 2021. "Nonparametric Learning Algorithms for Joint Pricing and Inventory Control with Lost Sales and Censored Demand," Mathematics of Operations Research, INFORMS, vol. 46(2), pages 726-756, May.
    17. Zhen Xu & Jiheng Zhang & Rachel Q. Zhang, 2019. "Instantaneous Control of Brownian Motion with a Positive Lead Time," Mathematics of Operations Research, INFORMS, vol. 44(3), pages 943-965, August.
    18. Takeda, Akiko & Kanamori, Takafumi, 2009. "A robust approach based on conditional value-at-risk measure to statistical learning problems," European Journal of Operational Research, Elsevier, vol. 198(1), pages 287-296, October.
    19. Xiuyan Ma, 2019. "Pricing to the Scenario: Price-Setting Newsvendor Models for Innovative Products," Mathematics, MDPI, vol. 7(9), pages 1-15, September.
    20. He, Xiuli & Sethi, Suresh & Xu, Xun & Yan, Nina, 2024. "Financing a capital-constrained supply chain: Equity or debt," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 188(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:coopap:v:89:y:2024:i:2:d:10.1007_s10589-024-00593-5. 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.