IDEAS home Printed from https://ideas.repec.org/a/inm/oropre/v64y2016i2p362-370.html
   My bibliography  Save this article

Technical Note—Nonparametric Data-Driven Algorithms for Multiproduct Inventory Systems with Censored Demand

Author

Listed:
  • Cong Shi

    (Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109)

  • Weidong Chen

    (Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109)

  • Izak Duenyas

    (Technology and Operations, Ross School of Business, University of Michigan, Ann Arbor, Michigan 48109)

Abstract

We propose a nonparametric data-driven algorithm called DDM for the management of stochastic periodic-review multiproduct inventory systems with a warehouse-capacity constraint. The demand distribution is not known a priori and the firm only has access to past sales data (often referred to as censored demand data). We measure performance of DDM through regret, the difference between the total expected cost of DDM and that of an oracle with access to the true demand distribution acting optimally. We characterize the rate of convergence guarantee of DDM. More specifically, we show that the average expected T -period cost incurred under DDM converges to the optimal cost at the rate of O ( T −1/2 ). Our asymptotic analysis significantly generalizes approaches used in Huh and Rusmevichientong (2009) for the uncapacitated single-product inventory systems. We also discuss several extensions and conduct numerical experiments to demonstrate the effectiveness of our proposed algorithm.

Suggested Citation

  • Cong Shi & Weidong Chen & Izak Duenyas, 2016. "Technical Note—Nonparametric Data-Driven Algorithms for Multiproduct Inventory Systems with Censored Demand," Operations Research, INFORMS, vol. 64(2), pages 362-370, April.
    • Handle: RePEc:inm:oropre:v:64:y:2016:i:2:p:362-370
    DOI: 10.1287/opre.2015.1474
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/opre.2015.1474
    Download Restriction: no
    File URL: https://libkey.io/10.1287/opre.2015.1474?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
    ---><---

    References listed on IDEAS

    as
    1. Li Chen & Erica L. Plambeck, 2008. "Dynamic Inventory Management with Learning About the Demand Distribution and Substitution Probability," Manufacturing & Service Operations Management, INFORMS, vol. 10(2), pages 236-256, May.
    2. Costis Maglaras & Serkan Eren, 2015. "A Maximum Entropy Joint Demand Estimation and Capacity Control Policy," Production and Operations Management, Production and Operations Management Society, vol. 24(3), pages 438-450, March.
    3. Retsef Levi & Georgia Perakis & Joline Uichanco, 2015. "The Data-Driven Newsvendor Problem: New Bounds and Insights," Operations Research, INFORMS, vol. 63(6), pages 1294-1306, December.
    4. Edward Ignall & Arthur F. Veinott, Jr., 1969. "Optimality of Myopic Inventory Policies for Several Substitute Products," Management Science, INFORMS, vol. 15(5), pages 284-304, January.
    5. Omar Besbes & Alp Muharremoglu, 2013. "On Implications of Demand Censoring in the Newsvendor Problem," Management Science, INFORMS, vol. 59(6), pages 1407-1424, June.
    6. Gregory A. Godfrey & Warren B. Powell, 2001. "An Adaptive, Distribution-Free Algorithm for the Newsvendor Problem with Censored Demands, with Applications to Inventory and Distribution," Management Science, INFORMS, vol. 47(8), pages 1101-1112, August.
    7. Woonghee Tim Huh & Paat Rusmevichientong, 2009. "A Nonparametric Asymptotic Analysis of Inventory Planning with Censored Demand," Mathematics of Operations Research, INFORMS, vol. 34(1), pages 103-123, February.
    8. Retsef Levi & Robin O. Roundy & David B. Shmoys, 2007. "Provably Near-Optimal Sampling-Based Policies for Stochastic Inventory Control Models," Mathematics of Operations Research, INFORMS, vol. 32(4), pages 821-839, November.
    9. Woonghee Tim Huh & Ganesh Janakiraman & John A. Muckstadt & Paat Rusmevichientong, 2009. "An Adaptive Algorithm for Finding the Optimal Base-Stock Policy in Lost Sales Inventory Systems with Censored Demand," Mathematics of Operations Research, INFORMS, vol. 34(2), pages 397-416, May.
    10. Sumit Kunnumkal & Huseyin Topaloglu, 2008. "Using Stochastic Approximation Methods to Compute Optimal Base-Stock Levels in Inventory Control Problems," Operations Research, INFORMS, vol. 56(3), pages 646-664, June.
    11. Warren Powell & Andrzej Ruszczyński & Huseyin Topaloglu, 2004. "Learning Algorithms for Separable Approximations of Discrete Stochastic Optimization Problems," Mathematics of Operations Research, INFORMS, vol. 29(4), pages 814-836, November.
    12. Martin A. Lariviere & Evan L. Porteus, 1999. "Stalking Information: Bayesian Inventory Management with Unobserved Lost Sales," Management Science, INFORMS, vol. 45(3), pages 346-363, March.
    13. Woonghee Tim Huh & Retsef Levi & Paat Rusmevichientong & James B. Orlin, 2011. "Adaptive Data-Driven Inventory Control with Censored Demand Based on Kaplan-Meier Estimator," Operations Research, INFORMS, vol. 59(4), pages 929-941, August.
    14. D. Beyer & S. P. Sethi & R. Sridhar, 2001. "Stochastic Multiproduct Inventory Models with Limited Storage," Journal of Optimization Theory and Applications, Springer, vol. 111(3), pages 553-588, December.
    15. Apostolos N. Burnetas & Craig E. Smith, 2000. "Adaptive Ordering and Pricing for Perishable Products," Operations Research, INFORMS, vol. 48(3), pages 436-443, June.
    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. Gah-Yi Ban, 2020. "Confidence Intervals for Data-Driven Inventory Policies with Demand Censoring," Operations Research, INFORMS, vol. 68(2), pages 309-326, March.
    2. Albiński, Szymon & Fontaine, Pirmin & Minner, Stefan, 2018. "Performance analysis of a hybrid bike sharing system: A service-level-based approach under censored demand observations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 116(C), pages 59-69.
    3. Chao Fu & Weiyong Liu & Wenjun Chang, 2020. "Data-driven multiple criteria decision making for diagnosis of thyroid cancer," Annals of Operations Research, Springer, vol. 293(2), pages 833-862, October.
    4. Georgia Perakis & Melvyn Sim & Qinshen Tang & Peng Xiong, 2023. "Robust Pricing and Production with Information Partitioning and Adaptation," Management Science, INFORMS, vol. 69(3), pages 1398-1419, March.
    5. Huanan Zhang & Xiuli Chao & Cong Shi, 2020. "Closing the Gap: A Learning Algorithm for Lost-Sales Inventory Systems with Lead Times," Management Science, INFORMS, vol. 66(5), pages 1962-1980, May.
    6. Rong Li & Jing‐Sheng Jeannette Song & Shuxiao Sun & Xiaona Zheng, 2022. "Fight inventory shrinkage: Simultaneous learning of inventory level and shrinkage rate," Production and Operations Management, Production and Operations Management Society, vol. 31(6), pages 2477-2491, 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. 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.
    2. Boxiao Chen & Xiuli Chao, 2020. "Dynamic Inventory Control with Stockout Substitution and Demand Learning," Management Science, INFORMS, vol. 66(11), pages 5108-5127, November.
    3. Jiri Chod & Mihalis G. Markakis & Nikolaos Trichakis, 2021. "On the Learning Benefits of Resource Flexibility," Management Science, INFORMS, vol. 67(10), pages 6513-6528, October.
    4. Gah-Yi Ban, 2020. "Confidence Intervals for Data-Driven Inventory Policies with Demand Censoring," Operations Research, INFORMS, vol. 68(2), pages 309-326, March.
    5. Hao Yuan & Qi Luo & Cong Shi, 2021. "Marrying Stochastic Gradient Descent with Bandits: Learning Algorithms for Inventory Systems with Fixed Costs," Management Science, INFORMS, vol. 67(10), pages 6089-6115, October.
    6. Omar Besbes & Alp Muharremoglu, 2013. "On Implications of Demand Censoring in the Newsvendor Problem," Management Science, INFORMS, vol. 59(6), pages 1407-1424, June.
    7. Woonghee Tim Huh & Paat Rusmevichientong, 2014. "Online Sequential Optimization with Biased Gradients: Theory and Applications to Censored Demand," INFORMS Journal on Computing, INFORMS, vol. 26(1), pages 150-159, February.
    8. Lin An & Andrew A. Li & Benjamin Moseley & R. Ravi, 2023. "The Nonstationary Newsvendor with (and without) Predictions," Papers 2305.07993, arXiv.org, revised Oct 2023.
    9. Weidong Chen & Cong Shi & Izak Duenyas, 2020. "Optimal Learning Algorithms for Stochastic Inventory Systems with Random Capacities," Production and Operations Management, Production and Operations Management Society, vol. 29(7), pages 1624-1649, July.
    10. Gah-Yi Ban & Cynthia Rudin, 2019. "The Big Data Newsvendor: Practical Insights from Machine Learning," Operations Research, INFORMS, vol. 67(1), pages 90-108, January.
    11. Boxiao Chen & David Simchi-Levi & Yining Wang & Yuan Zhou, 2022. "Dynamic Pricing and Inventory Control with Fixed Ordering Cost and Incomplete Demand Information," Management Science, INFORMS, vol. 68(8), pages 5684-5703, August.
    12. Satya S. Malladi & Alan L. Erera & Chelsea C. White, 2023. "Inventory control with modulated demand and a partially observed modulation process," Annals of Operations Research, Springer, vol. 321(1), pages 343-369, February.
    13. Soroush Saghafian & Brian Tomlin, 2016. "The Newsvendor under Demand Ambiguity: Combining Data with Moment and Tail Information," Operations Research, INFORMS, vol. 64(1), pages 167-185, February.
    14. Aditya Jain & Nils Rudi & Tong Wang, 2015. "Demand Estimation and Ordering Under Censoring: Stock-Out Timing Is (Almost) All You Need," Operations Research, INFORMS, vol. 63(1), pages 134-150, February.
    15. Woonghee Tim Huh & Retsef Levi & Paat Rusmevichientong & James B. Orlin, 2011. "Adaptive Data-Driven Inventory Control with Censored Demand Based on Kaplan-Meier Estimator," Operations Research, INFORMS, vol. 59(4), pages 929-941, August.
    16. Woonghee Tim Huh & Paat Rusmevichientong, 2009. "A Nonparametric Asymptotic Analysis of Inventory Planning with Censored Demand," Mathematics of Operations Research, INFORMS, vol. 34(1), pages 103-123, February.
    17. Anyan Qi & Hyun-Soo Ahn & Amitabh Sinha, 2017. "Capacity Investment with Demand Learning," Operations Research, INFORMS, vol. 65(1), pages 145-164, February.
    18. Boxiao Chen & Xiuli Chao & Hyun-Soo Ahn, 2019. "Coordinating Pricing and Inventory Replenishment with Nonparametric Demand Learning," Operations Research, INFORMS, vol. 67(4), pages 1035-1052, July.
    19. Adam J. Mersereau, 2015. "Demand Estimation from Censored Observations with Inventory Record Inaccuracy," Manufacturing & Service Operations Management, INFORMS, vol. 17(3), pages 335-349, July.
    20. Gah-Yi Ban & Jérémie Gallien & Adam J. Mersereau, 2019. "Dynamic Procurement of New Products with Covariate Information: The Residual Tree Method," Manufacturing & Service Operations Management, INFORMS, vol. 21(4), pages 798-815, 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:inm:oropre:v:64:y:2016:i:2:p:362-370. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.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.