IDEAS home Printed from https://ideas.repec.org/a/spr/snopef/v2y2021i4d10.1007_s43069-021-00094-9.html
   My bibliography  Save this article

A Feasibility-Ensured Lagrangian Heuristic for General Decomposable Problems

Author

Listed:
  • Kouhei Harada

    (NTTDATA Mathematical Systems Inc.)

Abstract

The Lagrangian relaxation method is a popular and useful tool to solve large-scale optimization problems with decomposable structures. A drawback of this approach is that a primal solution obtained by solving the Lagrangian relaxation problem is often primal infeasible even if an optimal dual solution is given. To recover primal feasibility, we rely on specialized heuristic-based approaches, each of which is tailored for a particular optimization problem. To overcome the difficulty, a general heuristic method has been proposed recently. The method ensures primal feasibility, but it is not tailored for some particular optimization problems. Furthermore, the method can estimate the gap of the objective between an optimal solution and the feasible solution obtained using the heuristic. Although the method provides important results, it relies on the assumption that both objective and constraint functions are concave. We generalize the results: the method ensures primal feasibility for a rather general class of problems. We have demonstrated that the fundamentally important requirement here is the extremity of the solution set of the Lagrangian relaxation problem. Our numerical experiments support the efficiency and validity of our generalized results.

Suggested Citation

  • Kouhei Harada, 2021. "A Feasibility-Ensured Lagrangian Heuristic for General Decomposable Problems," SN Operations Research Forum, Springer, vol. 2(4), pages 1-26, December.
    • Handle: RePEc:spr:snopef:v:2:y:2021:i:4:d:10.1007_s43069-021-00094-9
    DOI: 10.1007/s43069-021-00094-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s43069-021-00094-9
    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/s43069-021-00094-9?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. Duan Li & Xiaoling Sun, 2006. "Nonlinear Integer Programming," International Series in Operations Research and Management Science, Springer, number 978-0-387-32995-6, December.
    2. Valentina Cacchiani & Alberto Caprara & Matteo Fischetti, 2012. "A Lagrangian Heuristic for Robustness, with an Application to Train Timetabling," Transportation Science, INFORMS, vol. 46(1), pages 124-133, February.
    3. Michael Held & Richard M. Karp, 1970. "The Traveling-Salesman Problem and Minimum Spanning Trees," Operations Research, INFORMS, vol. 18(6), pages 1138-1162, December.
    4. Li, Jing-Quan & Mirchandani, Pitu B. & Borenstein, Denis, 2009. "A Lagrangian heuristic for the real-time vehicle rescheduling problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 45(3), pages 419-433, May.
    5. Diego Fiorotto & Silvio Araujo, 2014. "Reformulation and a Lagrangian heuristic for lot sizing problem on parallel machines," Annals of Operations Research, Springer, vol. 217(1), pages 213-231, June.
    6. Marshall L. Fisher, 1981. "The Lagrangian Relaxation Method for Solving Integer Programming Problems," Management Science, INFORMS, vol. 27(1), pages 1-18, January.
    7. Beasley, J. E., 1993. "Lagrangean heuristics for location problems," European Journal of Operational Research, Elsevier, vol. 65(3), pages 383-399, March.
    8. Patriksson, Michael & Strömberg, Christoffer, 2015. "Algorithms for the continuous nonlinear resource allocation problem—New implementations and numerical studies," European Journal of Operational Research, Elsevier, vol. 243(3), pages 703-722.
    9. Cynthia Barnhart & Ellis L. Johnson & George L. Nemhauser & Martin W. P. Savelsbergh & Pamela H. Vance, 1998. "Branch-and-Price: Column Generation for Solving Huge Integer Programs," Operations Research, INFORMS, vol. 46(3), pages 316-329, June.
    10. J. P. Aubin & I. Ekeland, 1976. "Estimates of the Duality Gap in Nonconvex Optimization," Mathematics of Operations Research, INFORMS, vol. 1(3), pages 225-245, August.
    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. Zhang, Yongxiang & Peng, Qiyuan & Yao, Yu & Zhang, Xin & Zhou, Xuesong, 2019. "Solving cyclic train timetabling problem through model reformulation: Extended time-space network construct and Alternating Direction Method of Multipliers methods," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 344-379.
    2. Degraeve, Z. & Jans, R.F., 2003. "A New Dantzig-Wolfe Reformulation And Branch-And-Price Algorithm For The Capacitated Lot Sizing Problem With Set Up Times," ERIM Report Series Research in Management ERS-2003-010-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    3. Torbjörn Larsson & Michael Patriksson, 2006. "Global Optimality Conditions for Discrete and Nonconvex Optimization---With Applications to Lagrangian Heuristics and Column Generation," Operations Research, INFORMS, vol. 54(3), pages 436-453, June.
    4. Balaji Gopalakrishnan & Ellis. Johnson, 2005. "Airline Crew Scheduling: State-of-the-Art," Annals of Operations Research, Springer, vol. 140(1), pages 305-337, November.
    5. Mohammad Nezhad, Ali & Manzour, Hasan & Salhi, Said, 2013. "Lagrangian relaxation heuristics for the uncapacitated single-source multi-product facility location problem," International Journal of Production Economics, Elsevier, vol. 145(2), pages 713-723.
    6. Ibrahim Muter & Tevfik Aytekin, 2017. "Incorporating Aggregate Diversity in Recommender Systems Using Scalable Optimization Approaches," INFORMS Journal on Computing, INFORMS, vol. 29(3), pages 405-421, August.
    7. Rostami, Borzou & Malucelli, Federico & Belotti, Pietro & Gualandi, Stefano, 2016. "Lower bounding procedure for the asymmetric quadratic traveling salesman problem," European Journal of Operational Research, Elsevier, vol. 253(3), pages 584-592.
    8. Peters, Emmanuel & de Matta, Renato & Boe, Warren, 2007. "Short-term work scheduling with job assignment flexibility for a multi-fleet transport system," European Journal of Operational Research, Elsevier, vol. 180(1), pages 82-98, July.
    9. Martinhon, Carlos & Lucena, Abilio & Maculan, Nelson, 2004. "Stronger K-tree relaxations for the vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 158(1), pages 56-71, October.
    10. Klose, Andreas & Gortz, Simon, 2007. "A branch-and-price algorithm for the capacitated facility location problem," European Journal of Operational Research, Elsevier, vol. 179(3), pages 1109-1125, June.
    11. Samir Elhedhli & Jean-Louis Goffin, 2005. "Efficient Production-Distribution System Design," Management Science, INFORMS, vol. 51(7), pages 1151-1164, July.
    12. Yao, Yu & Zhu, Xiaoning & Dong, Hongyu & Wu, Shengnan & Wu, Hailong & Carol Tong, Lu & Zhou, Xuesong, 2019. "ADMM-based problem decomposition scheme for vehicle routing problem with time windows," Transportation Research Part B: Methodological, Elsevier, vol. 129(C), pages 156-174.
    13. Joshua Q. Hale & Enlu Zhou & Jiming Peng, 2017. "A Lagrangian search method for the P-median problem," Journal of Global Optimization, Springer, vol. 69(1), pages 137-156, September.
    14. Huisman, D. & Jans, R.F. & Peeters, M. & Wagelmans, A.P.M., 2003. "Combining Column Generation and Lagrangian Relaxation," ERIM Report Series Research in Management ERS-2003-092-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    15. Arianna Alfieri & Shuyu Zhou & Rosario Scatamacchia & Steef L. van de Velde, 2021. "Dynamic programming algorithms and Lagrangian lower bounds for a discrete lot streaming problem in a two-machine flow shop," 4OR, Springer, vol. 19(2), pages 265-288, June.
    16. Daniel Adelman & Adam J. Mersereau, 2008. "Relaxations of Weakly Coupled Stochastic Dynamic Programs," Operations Research, INFORMS, vol. 56(3), pages 712-727, June.
    17. Marcel Turkensteen & Dmitry Malyshev & Boris Goldengorin & Panos M. Pardalos, 2017. "The reduction of computation times of upper and lower tolerances for selected combinatorial optimization problems," Journal of Global Optimization, Springer, vol. 68(3), pages 601-622, July.
    18. Yong Liang & Mengshi Lu & Zuo‐Jun Max Shen & Runyu Tang, 2021. "Data Center Network Design for Internet‐Related Services and Cloud Computing," Production and Operations Management, Production and Operations Management Society, vol. 30(7), pages 2077-2101, July.
    19. Samadi, Mohammadreza & Nikolaev, Alexander & Nagi, Rakesh, 2016. "A subjective evidence model for influence maximization in social networks," Omega, Elsevier, vol. 59(PB), pages 263-278.
    20. Emmanuel Ogbe & Xiang Li, 2019. "A joint decomposition method for global optimization of multiscenario nonconvex mixed-integer nonlinear programs," Journal of Global Optimization, Springer, vol. 75(3), pages 595-629, 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:snopef:v:2:y:2021:i:4:d:10.1007_s43069-021-00094-9. 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.