IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v242y2016i1d10.1007_s10479-014-1780-6.html
   My bibliography  Save this article

A hybrid MIP-based large neighborhood search heuristic for solving the machine reassignment problem

Author

Listed:
  • W. Jaśkowski

    (Poznan University of Technology)

  • M. Szubert

    (Poznan University of Technology)

  • P. Gawron

    (Poznan University of Technology
    University of Luxembourg)

Abstract

We present a hybrid metaheuristic approach for the machine reassignment problem, which was proposed for ROADEF/EURO Challenge 2012. The problem is a combinatorial optimization problem, which can be viewed as a highly constrained version of the multidimensional bin packing problem. Our algorithm, which took the third place in the challenge, consists of two components: a fast greedy hill climber and a large neighborhood search, which uses mixed integer programming to solve subproblems. We show that the hill climber, although simple, is an indispensable component that allows us to achieve high quality results especially for large instances of the problem. In the experimental part we analyze two subproblem selection methods used by the large neighborhood search algorithm and compare our approach with the two best entries in the competition, observing that none of the three algorithms dominates others on all available instances.

Suggested Citation

  • W. Jaśkowski & M. Szubert & P. Gawron, 2016. "A hybrid MIP-based large neighborhood search heuristic for solving the machine reassignment problem," Annals of Operations Research, Springer, vol. 242(1), pages 33-62, July.
    • Handle: RePEc:spr:annopr:v:242:y:2016:i:1:d:10.1007_s10479-014-1780-6
    DOI: 10.1007/s10479-014-1780-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-014-1780-6
    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/s10479-014-1780-6?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. Mireille Palpant & Christian Artigues & Philippe Michelon, 2004. "LSSPER: Solving the Resource-Constrained Project Scheduling Problem with Large Neighbourhood Search," Annals of Operations Research, Springer, vol. 131(1), pages 237-257, October.
    2. Burke, Edmund K. & Li, Jingpeng & Qu, Rong, 2010. "A hybrid model of integer programming and variable neighbourhood search for highly-constrained nurse rostering problems," European Journal of Operational Research, Elsevier, vol. 203(2), pages 484-493, June.
    3. Russell Bent & Pascal Van Hentenryck, 2004. "A Two-Stage Hybrid Local Search for the Vehicle Routing Problem with Time Windows," Transportation Science, INFORMS, vol. 38(4), pages 515-530, November.
    4. Prandtstetter, Matthias & Raidl, Günther R., 2008. "An integer linear programming approach and a hybrid variable neighborhood search for the car sequencing problem," European Journal of Operational Research, Elsevier, vol. 191(3), pages 1004-1022, December.
    5. Hadrien Cambazard & Emmanuel Hebrard & Barry O’Sullivan & Alexandre Papadopoulos, 2012. "Local search and constraint programming for the post enrolment-based course timetabling problem," Annals of Operations Research, Springer, vol. 194(1), pages 111-135, April.
    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. Goodson, Justin C. & Ohlmann, Jeffrey W. & Thomas, Barrett W., 2012. "Cyclic-order neighborhoods with application to the vehicle routing problem with stochastic demand," European Journal of Operational Research, Elsevier, vol. 217(2), pages 312-323.
    2. Sébastien Mouthuy & Florence Massen & Yves Deville & Pascal Van Hentenryck, 2015. "A Multistage Very Large-Scale Neighborhood Search for the Vehicle Routing Problem with Soft Time Windows," Transportation Science, INFORMS, vol. 49(2), pages 223-238, May.
    3. Dieter Debels & Mario Vanhoucke, 2007. "A Decomposition-Based Genetic Algorithm for the Resource-Constrained Project-Scheduling Problem," Operations Research, INFORMS, vol. 55(3), pages 457-469, June.
    4. Raeesi, Ramin & Zografos, Konstantinos G., 2020. "The electric vehicle routing problem with time windows and synchronised mobile battery swapping," Transportation Research Part B: Methodological, Elsevier, vol. 140(C), pages 101-129.
    5. Debels, Dieter & De Reyck, Bert & Leus, Roel & Vanhoucke, Mario, 2006. "A hybrid scatter search/electromagnetism meta-heuristic for project scheduling," European Journal of Operational Research, Elsevier, vol. 169(2), pages 638-653, March.
    6. Paul Czioska & Ronny Kutadinata & Aleksandar Trifunović & Stephan Winter & Monika Sester & Bernhard Friedrich, 2019. "Real-world meeting points for shared demand-responsive transportation systems," Public Transport, Springer, vol. 11(2), pages 341-377, August.
    7. Jürgen Kuster & Dietmar Jannach & Gerhard Friedrich, 2010. "Applying Local Rescheduling in response to schedule disruptions," Annals of Operations Research, Springer, vol. 180(1), pages 265-282, November.
    8. Schneider, M., 2016. "The vehicle-routing problem with time windows and driver-specific times," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 65941, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    9. Olli Bräysy & Michel Gendreau, 2005. "Vehicle Routing Problem with Time Windows, Part II: Metaheuristics," Transportation Science, INFORMS, vol. 39(1), pages 119-139, February.
    10. Rajeswari Muniyan & Rajakumar Ramalingam & Sultan S. Alshamrani & Durgaprasad Gangodkar & Ankur Dumka & Rajesh Singh & Anita Gehlot & Mamoon Rashid, 2022. "Artificial Bee Colony Algorithm with Nelder–Mead Method to Solve Nurse Scheduling Problem," Mathematics, MDPI, vol. 10(15), pages 1-24, July.
    11. Du, Mingyang & Cheng, Lin & Li, Xuefeng & Tang, Fang, 2020. "Static rebalancing optimization with considering the collection of malfunctioning bikes in free-floating bike sharing system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    12. Sioud, A. & Gagné, C., 2018. "Enhanced migrating birds optimization algorithm for the permutation flow shop problem with sequence dependent setup times," European Journal of Operational Research, Elsevier, vol. 264(1), pages 66-73.
    13. Say Leng Goh & Graham Kendall & Nasser R. Sabar & Salwani Abdullah, 2020. "An effective hybrid local search approach for the post enrolment course timetabling problem," OPSEARCH, Springer;Operational Research Society of India, vol. 57(4), pages 1131-1163, December.
    14. Hanne L. Petersen & Allan Larsen & Oli B. G. Madsen & Bjørn Petersen & Stefan Ropke, 2013. "The Simultaneous Vehicle Scheduling and Passenger Service Problem," Transportation Science, INFORMS, vol. 47(4), pages 603-616, November.
    15. Marco Antonio Boschetti & Vittorio Maniezzo, 2022. "Matheuristics: using mathematics for heuristic design," 4OR, Springer, vol. 20(2), pages 173-208, June.
    16. Andrew Lim & Xingwen Zhang, 2007. "A Two-Stage Heuristic with Ejection Pools and Generalized Ejection Chains for the Vehicle Routing Problem with Time Windows," INFORMS Journal on Computing, INFORMS, vol. 19(3), pages 443-457, August.
    17. Boysen, Nils & Scholl, Armin & Wopperer, Nico, 2012. "Resequencing of mixed-model assembly lines: Survey and research agenda," European Journal of Operational Research, Elsevier, vol. 216(3), pages 594-604.
    18. Kadri Sylejmani & Edon Gashi & Adrian Ymeri, 2023. "Simulated annealing with penalization for university course timetabling," Journal of Scheduling, Springer, vol. 26(5), pages 497-517, October.
    19. Schneider, Michael & Schwahn, Fabian & Vigo, Daniele, 2017. "Designing granular solution methods for routing problems with time windows," European Journal of Operational Research, Elsevier, vol. 263(2), pages 493-509.
    20. Ran Liu & Xiaolan Xie, 2018. "Physician Staffing for Emergency Departments with Time-Varying Demand," INFORMS Journal on Computing, INFORMS, vol. 30(3), pages 588-607, 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:spr:annopr:v:242:y:2016:i:1:d:10.1007_s10479-014-1780-6. 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.