IDEAS home Printed from https://ideas.repec.org/a/spr/joptap/v152y2012i2d10.1007_s10957-011-9902-7.html
   My bibliography  Save this article

Minimum and Worst-Case Performance Ratios of Rollout Algorithms

Author

Listed:
  • Luca Bertazzi

    (University of Brescia)

Abstract

Rollout algorithms are heuristic algorithms that can be applied to solve deterministic and stochastic dynamic programming problems. The basic idea is to use the cost obtained by applying a well known heuristic, called the base policy, to approximate the value of the optimal cost-to-go. We develop a theoretical approach to prove, for the 0-1 knapsack problem, that the minimum performance ratio of the rollout algorithms tends to be significantly greater when the performance ratio of the corresponding base policy is poor and that the worst-case performance ratio is significantly better than the one of the corresponding base policies.

Suggested Citation

  • Luca Bertazzi, 2012. "Minimum and Worst-Case Performance Ratios of Rollout Algorithms," Journal of Optimization Theory and Applications, Springer, vol. 152(2), pages 378-393, February.
    • Handle: RePEc:spr:joptap:v:152:y:2012:i:2:d:10.1007_s10957-011-9902-7
    DOI: 10.1007/s10957-011-9902-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10957-011-9902-7
    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/s10957-011-9902-7?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. Dimitris Bertsimas & Ramazan Demir, 2002. "An Approximate Dynamic Programming Approach to Multidimensional Knapsack Problems," Management Science, INFORMS, vol. 48(4), pages 550-565, April.
    2. Novoa, Clara & Storer, Robert, 2009. "An approximate dynamic programming approach for the vehicle routing problem with stochastic demands," European Journal of Operational Research, Elsevier, vol. 196(2), pages 509-515, July.
    3. F. Guerriero, 2008. "Hybrid Rollout Approaches for the Job Shop Scheduling Problem," Journal of Optimization Theory and Applications, Springer, vol. 139(2), pages 419-438, November.
    4. Hans Kellerer & Ulrich Pferschy, 1999. "A New Fully Polynomial Time Approximation Scheme for the Knapsack Problem," Journal of Combinatorial Optimization, Springer, vol. 3(1), pages 59-71, July.
    5. Eugene L. Lawler, 1979. "Fast Approximation Algorithms for Knapsack Problems," Mathematics of Operations Research, INFORMS, vol. 4(4), pages 339-356, November.
    6. Caprara, Alberto & Kellerer, Hans & Pferschy, Ulrich & Pisinger, David, 2000. "Approximation algorithms for knapsack problems with cardinality constraints," European Journal of Operational Research, Elsevier, vol. 123(2), pages 333-345, June.
    7. Hans Kellerer & Ulrich Pferschy, 2004. "Improved Dynamic Programming in Connection with an FPTAS for the Knapsack Problem," Journal of Combinatorial Optimization, Springer, vol. 8(1), pages 5-11, March.
    8. Nicola Secomandi, 2001. "A Rollout Policy for the Vehicle Routing Problem with Stochastic Demands," Operations Research, INFORMS, vol. 49(5), pages 796-802, October.
    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. Bertazzi, Luca & Secomandi, Nicola, 2018. "Faster rollout search for the vehicle routing problem with stochastic demands and restocking," European Journal of Operational Research, Elsevier, vol. 270(2), pages 487-497.
    2. Bertazzi, Luca & Bosco, Adamo & Laganà, Demetrio, 2015. "Managing stochastic demand in an Inventory Routing Problem with transportation procurement," Omega, Elsevier, vol. 56(C), pages 112-121.
    3. Andrew Mastin & Patrick Jaillet, 2015. "Average-Case Performance of Rollout Algorithms for Knapsack Problems," Journal of Optimization Theory and Applications, Springer, vol. 165(3), pages 964-984, June.
    4. Goodson, Justin C. & Thomas, Barrett W. & Ohlmann, Jeffrey W., 2017. "A rollout algorithm framework for heuristic solutions to finite-horizon stochastic dynamic programs," European Journal of Operational Research, Elsevier, vol. 258(1), pages 216-229.
    5. Zhenyuan Liu & Jiongbing Lu & Zaisheng Liu & Guangrui Liao & Hao Howard Zhang & Junwu Dong, 2019. "Patient scheduling in hemodialysis service," Journal of Combinatorial Optimization, Springer, vol. 37(1), pages 337-362, January.

    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. Bertazzi, Luca & Bosco, Adamo & Laganà, Demetrio, 2015. "Managing stochastic demand in an Inventory Routing Problem with transportation procurement," Omega, Elsevier, vol. 56(C), pages 112-121.
    2. Goodson, Justin C. & Thomas, Barrett W. & Ohlmann, Jeffrey W., 2017. "A rollout algorithm framework for heuristic solutions to finite-horizon stochastic dynamic programs," European Journal of Operational Research, Elsevier, vol. 258(1), pages 216-229.
    3. Jooken, Jorik & Leyman, Pieter & De Causmaecker, Patrick, 2022. "A new class of hard problem instances for the 0–1 knapsack problem," European Journal of Operational Research, Elsevier, vol. 301(3), pages 841-854.
    4. Zhou Xu, 2013. "The knapsack problem with a minimum filling constraint," Naval Research Logistics (NRL), John Wiley & Sons, vol. 60(1), pages 56-63, February.
    5. Aardal, Karen & van den Berg, Pieter L. & Gijswijt, Dion & Li, Shanfei, 2015. "Approximation algorithms for hard capacitated k-facility location problems," European Journal of Operational Research, Elsevier, vol. 242(2), pages 358-368.
    6. Zhong, Xueling & Ou, Jinwen & Wang, Guoqing, 2014. "Order acceptance and scheduling with machine availability constraints," European Journal of Operational Research, Elsevier, vol. 232(3), pages 435-441.
    7. Shu Zhang & Jeffrey W. Ohlmann & Barrett W. Thomas, 2018. "Dynamic Orienteering on a Network of Queues," Transportation Science, INFORMS, vol. 52(3), pages 691-706, June.
    8. Wadi Khalid Anuar & Lai Soon Lee & Hsin-Vonn Seow & Stefan Pickl, 2021. "A Multi-Depot Vehicle Routing Problem with Stochastic Road Capacity and Reduced Two-Stage Stochastic Integer Linear Programming Models for Rollout Algorithm," Mathematics, MDPI, vol. 9(13), pages 1-44, July.
    9. Jorge Oyola & Halvard Arntzen & David L. Woodruff, 2017. "The stochastic vehicle routing problem, a literature review, Part II: solution methods," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 6(4), pages 349-388, December.
    10. Stephan Helfrich & Arne Herzel & Stefan Ruzika & Clemens Thielen, 2022. "An approximation algorithm for a general class of multi-parametric optimization problems," Journal of Combinatorial Optimization, Springer, vol. 44(3), pages 1459-1494, October.
    11. Justin C. Goodson & Barrett W. Thomas & Jeffrey W. Ohlmann, 2016. "Restocking-Based Rollout Policies for the Vehicle Routing Problem with Stochastic Demand and Duration Limits," Transportation Science, INFORMS, vol. 50(2), pages 591-607, May.
    12. Rebi Daldal & Iftah Gamzu & Danny Segev & Tonguç Ünlüyurt, 2016. "Approximation algorithms for sequential batch‐testing of series systems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 63(4), pages 275-286, June.
    13. Majid Salavati-Khoshghalb & Michel Gendreau & Ola Jabali & Walter Rei, 2019. "A Rule-Based Recourse for the Vehicle Routing Problem with Stochastic Demands," Transportation Science, INFORMS, vol. 53(5), pages 1334-1353, September.
    14. Wadi Khalid Anuar & Lai Soon Lee & Hsin-Vonn Seow & Stefan Pickl, 2022. "A Multi-Depot Dynamic Vehicle Routing Problem with Stochastic Road Capacity: An MDP Model and Dynamic Policy for Post-Decision State Rollout Algorithm in Reinforcement Learning," Mathematics, MDPI, vol. 10(15), pages 1-70, July.
    15. Basso, Rafael & Kulcsár, Balázs & Sanchez-Diaz, Ivan & Qu, Xiaobo, 2022. "Dynamic stochastic electric vehicle routing with safe reinforcement learning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    16. Sayarshad, Hamid R. & Gao, H. Oliver, 2018. "A non-myopic dynamic inventory routing and pricing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 109(C), pages 83-98.
    17. Sayarshad, Hamid R. & Chow, Joseph Y.J., 2015. "A scalable non-myopic dynamic dial-a-ride and pricing problem," Transportation Research Part B: Methodological, Elsevier, vol. 81(P2), pages 539-554.
    18. F. Hooshmand Khaligh & S.A. MirHassani, 2016. "A mathematical model for vehicle routing problem under endogenous uncertainty," International Journal of Production Research, Taylor & Francis Journals, vol. 54(2), pages 579-590, January.
    19. Zhang, Junlong & Lam, William H.K. & Chen, Bi Yu, 2016. "On-time delivery probabilistic models for the vehicle routing problem with stochastic demands and time windows," European Journal of Operational Research, Elsevier, vol. 249(1), pages 144-154.
    20. Halman, Nir & Kellerer, Hans & Strusevich, Vitaly A., 2018. "Approximation schemes for non-separable non-linear boolean programming problems under nested knapsack constraints," European Journal of Operational Research, Elsevier, vol. 270(2), pages 435-447.

    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:joptap:v:152:y:2012:i:2:d:10.1007_s10957-011-9902-7. 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.