IDEAS home Printed from https://ideas.repec.org/a/inm/orinte/v54y2024i6p455-470.html
   My bibliography  Save this article

Amazon Locker Capacity Management

Author

Listed:
  • Samyukta Sethuraman

    (Sponsored Products Advertising, Amazon Ads, Palo Alto, California 94301)

  • Ankur Bansal

    (Kotak Mahindra Bank, Gurugram, Haryana 122002, India)

  • Setareh Mardan

    (Retail Pricing, Pricing Research, Amazon, Seattle, Washington 98109)

  • Mauricio G. C. Resende

    (Industrial & Systems Engineering, University of Washington, Seattle, Washington 98195)

  • Timothy L. Jacobs

    (ATS Science & Engineering, Amazon Transportation Services, Amazon, Bellevue, Washington 98004)

Abstract

Amazon Locker is a self-service delivery or pickup location where customers can pick up packages and drop off returns. A basic first-come-first-served policy for accepting package delivery requests to lockers results in lockers becoming full with standard shipping speed (3- to 5-day shipping) packages, leaving no space for expedited packages, which are mostly next-day or two-day shipping. This paper proposes a solution to the problem of determining how much locker capacity to reserve for different ship-option packages. Yield management is a much-researched field with popular applications in the airline, car rental, and hotel industries. However, Amazon Locker poses a unique challenge in this field because the number of days a package will wait in a locker (package dwell time) is, in general, unknown. The proposed solution combines machine learning techniques to predict locker demand and package dwell time with linear programming to maximize throughput in lockers. The decision variables from this optimization provide optimal capacity reservation values for different ship options. This resulted in a year-over-year increase of 9% in Locker throughput worldwide during the holiday season of 2018, impacting millions of customers.

Suggested Citation

  • Samyukta Sethuraman & Ankur Bansal & Setareh Mardan & Mauricio G. C. Resende & Timothy L. Jacobs, 2024. "Amazon Locker Capacity Management," Interfaces, INFORMS, vol. 54(6), pages 455-470, November.
    • Handle: RePEc:inm:orinte:v:54:y:2024:i:6:p:455-470
    DOI: 10.1287/inte.2023.0005
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/inte.2023.0005
    Download Restriction: no
    File URL: https://libkey.io/10.1287/inte.2023.0005?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. Barry C. Smith & John F. Leimkuhler & Ross M. Darrow, 1992. "Yield Management at American Airlines," Interfaces, INFORMS, vol. 22(1), pages 8-31, February.
    2. Julien Guillen & Pierre Ruiz & Umberto Dellepiane & Ludovica Maccarrone & Raffaele Maccioni & Alessandro Pinzuti & Enrico Procacci, 2019. "Europcar Integrates Forecasting, Simulation, and Optimization Techniques in a Capacity and Revenue Management System," Interfaces, INFORMS, vol. 49(1), pages 1-40, January.
    3. Shinjini Pandey & Goutam Dutta & Harit Joshi, 2017. "Survey on Revenue Management in Media and Broadcasting," Interfaces, INFORMS, vol. 47(3), pages 195-213, June.
    4. Jeffrey I. McGill & Garrett J. van Ryzin, 1999. "Revenue Management: Research Overview and Prospects," Transportation Science, INFORMS, vol. 33(2), pages 233-256, May.
    5. Weatherford, Larry R. & Kimes, Sheryl E., 2003. "A comparison of forecasting methods for hotel revenue management," International Journal of Forecasting, Elsevier, vol. 19(3), pages 401-415.
    6. Gabriel Bitran & René Caldentey, 2003. "An Overview of Pricing Models for Revenue Management," Manufacturing & Service Operations Management, INFORMS, vol. 5(3), pages 203-229, August.
    7. Ido Orenstein & Tal Raviv & Elad Sadan, 2019. "Flexible parcel delivery to automated parcel lockers: models, solution methods and analysis," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 8(5), pages 683-711, December.
    8. Michael F. Gorman, 2010. "Hub Group Implements a Suite of OR Tools to Improve Its Operations," Interfaces, INFORMS, vol. 40(5), pages 368-384, October.
    9. Sumit Kunnumkal & Kalyan Talluri, 2019. "A strong Lagrangian relaxation for general discrete-choice network revenue management," Computational Optimization and Applications, Springer, vol. 73(1), pages 275-310, May.
    10. Raja Gopalakrishnan & Narayan Rangaraj, 2010. "Capacity Management on Long-Distance Passenger Trains of Indian Railways," Interfaces, INFORMS, vol. 40(4), pages 291-302, August.
    11. Jacob B. Feldman & Huseyin Topaloglu, 2017. "Revenue Management Under the Markov Chain Choice Model," Operations Research, INFORMS, vol. 65(5), pages 1322-1342, October.
    12. Justin Beck & John Harvey & Kristina Kaylen & Corrado Sala & Melinda Urban & Peter Vermeulen & Norman Wilken & Wei Xie & Dan Iliescu & Pratik Mital, 2021. "Carnival Optimizes Revenue and Inventory Across Heterogenous Cruise Line Brands," Interfaces, INFORMS, vol. 51(1), pages 26-41, February.
    13. Timothy L. Jacobs & Barry C. Smith & Ellis L. Johnson, 2008. "Incorporating Network Flow Effects into the Airline Fleet Assignment Process," Transportation Science, INFORMS, vol. 42(4), pages 514-529, November.
    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. Laumer, Simon & Barz, Christiane, 2023. "Reductions of non-separable approximate linear programs for network revenue management," European Journal of Operational Research, Elsevier, vol. 309(1), pages 252-270.
    2. Larry Weatherford, 2016. "The history of forecasting models in revenue management," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 15(3), pages 212-221, July.
    3. Syed Asif Raza & Rafi Ashrafi & Ali Akgunduz, 2020. "A bibliometric analysis of revenue management in airline industry," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 19(6), pages 436-465, December.
    4. Strauss, Arne K. & Klein, Robert & Steinhardt, Claudius, 2018. "A review of choice-based revenue management: Theory and methods," European Journal of Operational Research, Elsevier, vol. 271(2), pages 375-387.
    5. Ali Hortaçsu & Olivia R. Natan & Hayden Parsley & Timothy Schwieg & Kevin R. Williams, 2021. "Organizational Structure and Pricing: Evidence from a Large U.S. Airline," NBER Working Papers 29508, National Bureau of Economic Research, Inc.
    6. Dasci, A. & Karakul, M., 2009. "Two-period dynamic versus fixed-ratio pricing in a capacity constrained duopoly," European Journal of Operational Research, Elsevier, vol. 197(3), pages 945-968, September.
    7. Yiwei Chen & Vivek F. Farias, 2013. "Simple Policies for Dynamic Pricing with Imperfect Forecasts," Operations Research, INFORMS, vol. 61(3), pages 612-624, June.
    8. Ma, Jie & Tse, Ying Kei & Wang, Xiaojun & Zhang, Minhao, 2019. "Examining customer perception and behaviour through social media research – An empirical study of the United Airlines overbooking crisis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 127(C), pages 192-205.
    9. Yuri Levin & Jeff McGill & Mikhail Nediak, 2008. "Risk in Revenue Management and Dynamic Pricing," Operations Research, INFORMS, vol. 56(2), pages 326-343, April.
    10. Feng, Youyi & Xiao, Baichun, 2006. "A continuous-time seat control model for single-leg flights with no-shows and optimal overbooking upper bound," European Journal of Operational Research, Elsevier, vol. 174(2), pages 1298-1316, October.
    11. Naragain Phumchusri & Phatsakorn Sangsukiam & Nannapat Chariyasethapong, 2020. "Optimal overbooking model for car rental business with two levels of prices having stochastic joint booking and show-up levels," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 19(3), pages 190-209, June.
    12. Sabri Çelik & Alp Muharremoglu & Sergei Savin, 2009. "Revenue Management with Costly Price Adjustments," Operations Research, INFORMS, vol. 57(5), pages 1206-1219, October.
    13. Chen, Bo & Zhang, Xiandong, 2019. "Scheduling with time-of-use costs," European Journal of Operational Research, Elsevier, vol. 274(3), pages 900-908.
    14. Irene Ng & Nick K.T. Yip, 2009. "Mechanism design in an integrated approach towards revenue management: the case of Empress Cruise Lines," The Service Industries Journal, Taylor & Francis Journals, vol. 31(3), pages 469-482, February.
    15. Lingxiu Dong & Panos Kouvelis & Zhongjun Tian, 2009. "Dynamic Pricing and Inventory Control of Substitute Products," Manufacturing & Service Operations Management, INFORMS, vol. 11(2), pages 317-339, December.
    16. Wei Shi Lim, 2009. "Overselling in a Competitive Environment: Boon or Bane?," Marketing Science, INFORMS, vol. 28(6), pages 1129-1143, 11-12.
    17. Longyuan Du & Ming Hu & Jiahua Wu, 2022. "Contingent stimulus in crowdfunding," Production and Operations Management, Production and Operations Management Society, vol. 31(9), pages 3543-3558, September.
    18. John Kros & Scott Dellana & David West, 2009. "Overbooking Increases Patient Access at East Carolina University's Student Health Services Clinic," Interfaces, INFORMS, vol. 39(3), pages 271-287, June.
    19. E. Andrew Boyd & Ioana C. Bilegan, 2003. "Revenue Management and E-Commerce," Management Science, INFORMS, vol. 49(10), pages 1363-1386, October.
    20. Valerio Lacagnina & Davide Provenzano, 2016. "An integrated fuzzy-stochastic model for revenue management," Tourism Economics, , vol. 22(4), pages 779-792, August.

    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:orinte:v:54:y:2024:i:6:p:455-470. 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.