IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v286y2020i1p101-114.html
   My bibliography  Save this article

A cutoff time strategy based on the coupon collector’s problem

Author

Listed:
  • Lobo, Fernando G.
  • Bazargani, Mosab
  • Burke, Edmund K.

Abstract

Throughout the course of an optimization run, the probability of yielding further improvement becomes smaller as the search proceeds, and eventually the search stagnates. Under such a state, letting the algorithm continue to run is a waste of time as there is little hope that subsequent improvement can be made. The ability to detect the stagnation point is therefore of prime importance. If such a point can be detected reliably, then it is possible to make better use of the computing resources, perhaps restarting the algorithm at the stagnation point, either with the same or with a different parameter configuration.

Suggested Citation

  • Lobo, Fernando G. & Bazargani, Mosab & Burke, Edmund K., 2020. "A cutoff time strategy based on the coupon collector’s problem," European Journal of Operational Research, Elsevier, vol. 286(1), pages 101-114.
    • Handle: RePEc:eee:ejores:v:286:y:2020:i:1:p:101-114
    DOI: 10.1016/j.ejor.2020.03.027
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221720302411
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2020.03.027?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. Fernandez-Viagas, Victor & Ruiz, Rubén & Framinan, Jose M., 2017. "A new vision of approximate methods for the permutation flowshop to minimise makespan: State-of-the-art and computational evaluation," European Journal of Operational Research, Elsevier, vol. 257(3), pages 707-721.
    2. Pagnozzi, Federico & Stützle, Thomas, 2019. "Automatic design of hybrid stochastic local search algorithms for permutation flowshop problems," European Journal of Operational Research, Elsevier, vol. 276(2), pages 409-421.
    3. Fred Glover, 1989. "Tabu Search---Part I," INFORMS Journal on Computing, INFORMS, vol. 1(3), pages 190-206, August.
    4. Laporte, Gilbert, 1992. "The traveling salesman problem: An overview of exact and approximate algorithms," European Journal of Operational Research, Elsevier, vol. 59(2), pages 231-247, June.
    5. Ruiz, Ruben & Maroto, Concepcion, 2005. "A comprehensive review and evaluation of permutation flowshop heuristics," European Journal of Operational Research, Elsevier, vol. 165(2), pages 479-494, September.
    6. López-Ibáñez, Manuel & Dubois-Lacoste, Jérémie & Pérez Cáceres, Leslie & Birattari, Mauro & Stützle, Thomas, 2016. "The irace package: Iterated racing for automatic algorithm configuration," Operations Research Perspectives, Elsevier, vol. 3(C), pages 43-58.
    7. Vallada, Eva & Ruiz, Rubén & Framinan, Jose M., 2015. "New hard benchmark for flowshop scheduling problems minimising makespan," European Journal of Operational Research, Elsevier, vol. 240(3), pages 666-677.
    8. Edmund K. Burke & Yuri Bykov, 2016. "An Adaptive Flex-Deluge Approach to University Exam Timetabling," INFORMS Journal on Computing, INFORMS, vol. 28(4), pages 781-794, November.
    9. Taillard, E., 1990. "Some efficient heuristic methods for the flow shop sequencing problem," European Journal of Operational Research, Elsevier, vol. 47(1), pages 65-74, July.
    10. Taillard, E., 1993. "Benchmarks for basic scheduling problems," European Journal of Operational Research, Elsevier, vol. 64(2), pages 278-285, January.
    11. Ruiz, Ruben & Stutzle, Thomas, 2007. "A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem," European Journal of Operational Research, Elsevier, vol. 177(3), pages 2033-2049, March.
    12. Burke, Edmund K. & Bykov, Yuri, 2017. "The late acceptance Hill-Climbing heuristic," European Journal of Operational Research, Elsevier, vol. 258(1), pages 70-78.
    13. Nawaz, Muhammad & Enscore Jr, E Emory & Ham, Inyong, 1983. "A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem," Omega, Elsevier, vol. 11(1), pages 91-95.
    14. A. M. Geoffrion & G. W. Graves, 1976. "Scheduling Parallel Production Lines with Changeover Costs: Practical Application of a Quadratic Assignment/ LP Approach," Operations Research, INFORMS, vol. 24(4), pages 595-610, August.
    15. Connolly, David T., 1990. "An improved annealing scheme for the QAP," European Journal of Operational Research, Elsevier, vol. 46(1), pages 93-100, May.
    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. Hongjun Lv, 2021. "Who benefits when coupons are issued by a duopoly from an e‐market?," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(7), pages 1656-1664, October.

    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. Pagnozzi, Federico & Stützle, Thomas, 2019. "Automatic design of hybrid stochastic local search algorithms for permutation flowshop problems," European Journal of Operational Research, Elsevier, vol. 276(2), pages 409-421.
    2. Benavides, Alexander J. & Vera, Antony, 2022. "The reversibility property in a job-insertion tiebreaker for the permutational flow shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 297(2), pages 407-421.
    3. Karimi-Mamaghan, Maryam & Mohammadi, Mehrdad & Pasdeloup, Bastien & Meyer, Patrick, 2023. "Learning to select operators in meta-heuristics: An integration of Q-learning into the iterated greedy algorithm for the permutation flowshop scheduling problem," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1296-1330.
    4. Perez-Gonzalez, Paz & Framinan, Jose M., 2024. "A review and classification on distributed permutation flowshop scheduling problems," European Journal of Operational Research, Elsevier, vol. 312(1), pages 1-21.
    5. Libralesso, Luc & Focke, Pablo Andres & Secardin, Aurélien & Jost, Vincent, 2022. "Iterative beam search algorithms for the permutation flowshop," European Journal of Operational Research, Elsevier, vol. 301(1), pages 217-234.
    6. Fernandez-Viagas, Victor & Ruiz, Rubén & Framinan, Jose M., 2017. "A new vision of approximate methods for the permutation flowshop to minimise makespan: State-of-the-art and computational evaluation," European Journal of Operational Research, Elsevier, vol. 257(3), pages 707-721.
    7. Franzin, Alberto & Stützle, Thomas, 2023. "A landscape-based analysis of fixed temperature and simulated annealing," European Journal of Operational Research, Elsevier, vol. 304(2), pages 395-410.
    8. Brammer, Janis & Lutz, Bernhard & Neumann, Dirk, 2022. "Permutation flow shop scheduling with multiple lines and demand plans using reinforcement learning," European Journal of Operational Research, Elsevier, vol. 299(1), pages 75-86.
    9. Pagnozzi, Federico & Stützle, Thomas, 2021. "Automatic design of hybrid stochastic local search algorithms for permutation flowshop problems with additional constraints," Operations Research Perspectives, Elsevier, vol. 8(C).
    10. Ruiz, Rubén & Pan, Quan-Ke & Naderi, Bahman, 2019. "Iterated Greedy methods for the distributed permutation flowshop scheduling problem," Omega, Elsevier, vol. 83(C), pages 213-222.
    11. Smutnicki, Czeslaw & Pempera, Jaroslaw & Bocewicz, Grzegorz & Banaszak, Zbigniew, 2022. "Cyclic flow-shop scheduling with no-wait constraints and missing operations," European Journal of Operational Research, Elsevier, vol. 302(1), pages 39-49.
    12. Kalczynski, Pawel J. & Kamburowski, Jerzy, 2009. "An empirical analysis of the optimality rate of flow shop heuristics," European Journal of Operational Research, Elsevier, vol. 198(1), pages 93-101, October.
    13. Pan, Quan-Ke & Ruiz, Rubén, 2014. "An effective iterated greedy algorithm for the mixed no-idle permutation flowshop scheduling problem," Omega, Elsevier, vol. 44(C), pages 41-50.
    14. Fernandez-Viagas, Victor & Talens, Carla & Framinan, Jose M., 2022. "Assembly flowshop scheduling problem: Speed-up procedure and computational evaluation," European Journal of Operational Research, Elsevier, vol. 299(3), pages 869-882.
    15. Naderi, Bahman & Ruiz, Rubén, 2014. "A scatter search algorithm for the distributed permutation flowshop scheduling problem," European Journal of Operational Research, Elsevier, vol. 239(2), pages 323-334.
    16. Liu, Weibo & Jin, Yan & Price, Mark, 2017. "A new improved NEH heuristic for permutation flowshop scheduling problems," International Journal of Production Economics, Elsevier, vol. 193(C), pages 21-30.
    17. Pessoa, Luciana S. & Andrade, Carlos E., 2018. "Heuristics for a flowshop scheduling problem with stepwise job objective function," European Journal of Operational Research, Elsevier, vol. 266(3), pages 950-962.
    18. Ruiz, Ruben & Stutzle, Thomas, 2008. "An Iterated Greedy heuristic for the sequence dependent setup times flowshop problem with makespan and weighted tardiness objectives," European Journal of Operational Research, Elsevier, vol. 187(3), pages 1143-1159, June.
    19. Rad, Shahriar Farahmand & Ruiz, Rubén & Boroojerdian, Naser, 2009. "New high performing heuristics for minimizing makespan in permutation flowshops," Omega, Elsevier, vol. 37(2), pages 331-345, April.
    20. J A Vázquez-Rodríguez & G Ochoa, 2011. "On the automatic discovery of variants of the NEH procedure for flow shop scheduling using genetic programming," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(2), pages 381-396, February.

    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:eee:ejores:v:286:y:2020:i:1:p:101-114. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.