IDEAS home Printed from https://ideas.repec.org/a/spr/jcomop/v35y2018i3d10.1007_s10878-017-0234-x.html
   My bibliography  Save this article

Lower bounds on the adaptivity gaps in variants of the stochastic knapsack problem

Author

Listed:
  • Asaf Levin

    (The Technion)

  • Aleksander Vainer

    (The Technion)

Abstract

We consider stochastic variants of the NP-hard 0/1 knapsack problem in which item values are deterministic and item sizes are independent random variables with known, arbitrary distributions. Items are placed in the knapsack sequentially, and the act of placing an item in the knapsack instantiates its size. The goal is to compute a policy for insertion of the items, that maximizes the expected value of the set of items placed in the knapsack. These variants that we study differ only in the formula for computing the value of the final solution obtained by the policy. We consider both nonadaptive policies (that designate a priori a fixed subset or permutation of items to insert) and adaptive policies (that can make dynamic decisions based on the instantiated sizes of the items placed in the knapsack thus far). Our work characterizes the benefit of adaptivity. For this purpose we use a measure called the adaptivity gap: the supremum over instances of the ratio between the expected value obtained by an optimal adaptive policy and the expected value obtained by an optimal non-adaptive policy. We show that while for the variants considered in the literature this quantity is bounded by a constant there are other variants where it is unbounded.

Suggested Citation

  • Asaf Levin & Aleksander Vainer, 2018. "Lower bounds on the adaptivity gaps in variants of the stochastic knapsack problem," Journal of Combinatorial Optimization, Springer, vol. 35(3), pages 794-813, April.
    • Handle: RePEc:spr:jcomop:v:35:y:2018:i:3:d:10.1007_s10878-017-0234-x
    DOI: 10.1007/s10878-017-0234-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10878-017-0234-x
    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/s10878-017-0234-x?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. Jason D. Papastavrou & Srikanth Rajagopalan & Anton J. Kleywegt, 1996. "The Dynamic and Stochastic Knapsack Problem with Deadlines," Management Science, INFORMS, vol. 42(12), pages 1706-1718, December.
    2. Mordechai I. Henig, 1990. "Risk Criteria in a Stochastic Knapsack Problem," Operations Research, INFORMS, vol. 38(5), pages 820-825, October.
    3. Chen, Kai & Ross, Sheldon M., 2014. "An adaptive stochastic knapsack problem," European Journal of Operational Research, Elsevier, vol. 239(3), pages 625-635.
    4. Brian C. Dean & Michel X. Goemans & Jan Vondrák, 2008. "Approximating the Stochastic Knapsack Problem: The Benefit of Adaptivity," Mathematics of Operations Research, INFORMS, vol. 33(4), pages 945-964, 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. Yasemin Merzifonluoglu & Joseph Geunes, 2021. "The Risk-Averse Static Stochastic Knapsack Problem," INFORMS Journal on Computing, INFORMS, vol. 33(3), pages 931-948, July.
    2. Bayliss, Christopher & Currie, Christine S.M. & Bennell, Julia A. & Martinez-Sykora, Antonio, 2021. "Queue-constrained packing: A vehicle ferry case study," European Journal of Operational Research, Elsevier, vol. 289(2), pages 727-741.
    3. Brian C. Dean & Michel X. Goemans & Jan Vondrák, 2008. "Approximating the Stochastic Knapsack Problem: The Benefit of Adaptivity," Mathematics of Operations Research, INFORMS, vol. 33(4), pages 945-964, November.
    4. Clifford Stein & Van-Anh Truong & Xinshang Wang, 2020. "Advance Service Reservations with Heterogeneous Customers," Management Science, INFORMS, vol. 66(7), pages 2929-2950, July.
    5. Jian Li & Amol Deshpande, 2019. "Maximizing Expected Utility for Stochastic Combinatorial Optimization Problems," Mathematics of Operations Research, INFORMS, vol. 44(1), pages 354-375, February.
    6. Range, Troels Martin & Kozlowski, Dawid & Petersen, Niels Chr., 2017. "A shortest-path-based approach for the stochastic knapsack problem with non-decreasing expected overfilling costs," Discussion Papers on Economics 9/2017, University of Southern Denmark, Department of Economics.
    7. Santiago R. Balseiro & David B. Brown, 2019. "Approximations to Stochastic Dynamic Programs via Information Relaxation Duality," Operations Research, INFORMS, vol. 67(2), pages 577-597, March.
    8. Diaz, Juan Esteban & Handl, Julia & Xu, Dong-Ling, 2018. "Integrating meta-heuristics, simulation and exact techniques for production planning of a failure-prone manufacturing system," European Journal of Operational Research, Elsevier, vol. 266(3), pages 976-989.
    9. Tianke Feng & Joseph C. Hartman, 2015. "The dynamic and stochastic knapsack Problem with homogeneous‐sized items and postponement options," Naval Research Logistics (NRL), John Wiley & Sons, vol. 62(4), pages 267-292, June.
    10. Huanan Zhang & Cong Shi & Chao Qin & Cheng Hua, 2016. "Stochastic regret minimization for revenue management problems with nonstationary demands," Naval Research Logistics (NRL), John Wiley & Sons, vol. 63(6), pages 433-448, September.
    11. Martin Skutella & Maxim Sviridenko & Marc Uetz, 2016. "Unrelated Machine Scheduling with Stochastic Processing Times," Mathematics of Operations Research, INFORMS, vol. 41(3), pages 851-864, August.
    12. Murthy, Ishwar & Sarkar, Sumit, 1997. "Exact algorithms for the stochastic shortest path problem with a decreasing deadline utility function," European Journal of Operational Research, Elsevier, vol. 103(1), pages 209-229, November.
    13. Jacko, Peter & Niño Mora, José, 2009. "An index for dynamic product promotion and the knapsack problem for perishable items," DES - Working Papers. Statistics and Econometrics. WS ws093111, Universidad Carlos III de Madrid. Departamento de Estadística.
    14. Wang, Xiubin & Regan, Amelia, 2006. "Dynamic yield management when aircraft assignments are subject to swap," Transportation Research Part B: Methodological, Elsevier, vol. 40(7), pages 563-576, August.
    15. Zettelmeyer, Florian & Scott Morton, Fiona & Silva-Risso, Jorge, 2005. "Inventory Fluctuations and Price Discrimination: The Determinants of Price Variation in Car Retailing," Department of Economics, Working Paper Series qt6pf5s593, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    16. Soumia Ichoua & Michel Gendreau & Jean-Yves Potvin, 2006. "Exploiting Knowledge About Future Demands for Real-Time Vehicle Dispatching," Transportation Science, INFORMS, vol. 40(2), pages 211-225, May.
    17. Yigao Liang, 1999. "Solution to the Continuous Time Dynamic Yield Management Model," Transportation Science, INFORMS, vol. 33(1), pages 117-123, February.
    18. Alessandro Arlotto & Noah Gans & J. Michael Steele, 2014. "Markov Decision Problems Where Means Bound Variances," Operations Research, INFORMS, vol. 62(4), pages 864-875, August.
    19. Chernonog, Tatyana & Avinadav, Tal, 2014. "Profit criteria involving risk in price setting of virtual products," European Journal of Operational Research, Elsevier, vol. 236(1), pages 351-360.
    20. Mallesh M. Pai & Rakesh Vohra, 2013. "Optimal Dynamic Auctions and Simple Index Rules," Mathematics of Operations Research, INFORMS, vol. 38(4), pages 682-697, November.

    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:jcomop:v:35:y:2018:i:3:d:10.1007_s10878-017-0234-x. 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.