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On generating maximal nondominated Benders cuts

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  • Hanif Sherali
  • Brian Lunday

Abstract

In this paper, we explore certain algorithmic strategies for accelerating the convergence of Benders decomposition method via the generation of maximal nondominated cuts. Based on interpreting the seminal work of Magnanti and Wong (Operations Research, 29(3), 464–484, 1981 ) for generating nondominated cuts within a multiobjective framework, we propose an algorithmic strategy that utilizes a preemptively small perturbation of the right-hand-side of the Benders subproblem to generate maximal nondominated Benders cuts, as well as a complimentary strategy that generates an additional cut in each iteration via an alternative emphasis on decision variable weights. We also examine the computational effectiveness of solving a secondary subproblem using an objective cut as proposed by Magnanti and Wong versus identifying the Pareto-optimality region for cut generation by utilizing complementary slackness conditions. In addition, we exhibit how a standard feasibility cut can be extracted from the solution of subproblems that generate only optimality cuts through the use of artificial variables. With Magnanti and Wong’s baseline procedure approximated during implementation via the use of a core point estimation technique (Papadakos in Computers and Operations Research, 36(1), 176–195, 2009 ), these algorithmic strategies are tested on instances from the literature concerning the fixed charge network flow program. Copyright Springer Science+Business Media, LLC 2013

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  • Hanif Sherali & Brian Lunday, 2013. "On generating maximal nondominated Benders cuts," Annals of Operations Research, Springer, vol. 210(1), pages 57-72, November.
  • Handle: RePEc:spr:annopr:v:210:y:2013:i:1:p:57-72:10.1007/s10479-011-0883-6
    DOI: 10.1007/s10479-011-0883-6
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    References listed on IDEAS

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    Cited by:

    1. M. Jenabi & S. M. T. Fatemi Ghomi & S. A. Torabi & Moeen Sammak Jalali, 2022. "An accelerated Benders decomposition algorithm for stochastic power system expansion planning using sample average approximation," OPSEARCH, Springer;Operational Research Society of India, vol. 59(4), pages 1304-1336, December.
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    8. Rahmaniani, Ragheb & Crainic, Teodor Gabriel & Gendreau, Michel & Rei, Walter, 2017. "The Benders decomposition algorithm: A literature review," European Journal of Operational Research, Elsevier, vol. 259(3), pages 801-817.
    9. Jeihoonian, Mohammad & Kazemi Zanjani, Masoumeh & Gendreau, Michel, 2016. "Accelerating Benders decomposition for closed-loop supply chain network design: Case of used durable products with different quality levels," European Journal of Operational Research, Elsevier, vol. 251(3), pages 830-845.
    10. Emilia Grass & Kathrin Fischer & Antonia Rams, 2020. "An accelerated L-shaped method for solving two-stage stochastic programs in disaster management," Annals of Operations Research, Springer, vol. 284(2), pages 557-582, January.
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    12. Blanchot, Xavier & Clautiaux, François & Detienne, Boris & Froger, Aurélien & Ruiz, Manuel, 2023. "The Benders by batch algorithm: Design and stabilization of an enhanced algorithm to solve multicut Benders reformulation of two-stage stochastic programs," European Journal of Operational Research, Elsevier, vol. 309(1), pages 202-216.
    13. Keyvanshokooh, Esmaeil & Ryan, Sarah M. & Kabir, Elnaz, 2016. "Hybrid robust and stochastic optimization for closed-loop supply chain network design using accelerated Benders decomposition," European Journal of Operational Research, Elsevier, vol. 249(1), pages 76-92.
    14. W. Ackooij & A. Frangioni & W. Oliveira, 2016. "Inexact stabilized Benders’ decomposition approaches with application to chance-constrained problems with finite support," Computational Optimization and Applications, Springer, vol. 65(3), pages 637-669, December.
    15. Placido dos Santos, Felipe Silva & Oliveira, Fabricio, 2019. "An enhanced L-Shaped method for optimizing periodic-review inventory control problems modeled via two-stage stochastic programming," European Journal of Operational Research, Elsevier, vol. 275(2), pages 677-693.
    16. Wei Zhang & Kai Wang & Alexandre Jacquillat & Shuaian Wang, 2023. "Optimized Scenario Reduction: Solving Large-Scale Stochastic Programs with Quality Guarantees," INFORMS Journal on Computing, INFORMS, vol. 35(4), pages 886-908, July.
    17. Aakil M. Caunhye & Xiaofeng Nie, 2018. "A Stochastic Programming Model for Casualty Response Planning During Catastrophic Health Events," Transportation Science, INFORMS, vol. 52(2), pages 437-453, March.
    18. Teodor Gabriel Crainic & Mike Hewitt & Francesca Maggioni & Walter Rei, 2021. "Partial Benders Decomposition: General Methodology and Application to Stochastic Network Design," Transportation Science, INFORMS, vol. 55(2), pages 414-435, March.
    19. Shengzhi Shao & Hanif D. Sherali & Mohamed Haouari, 2017. "A Novel Model and Decomposition Approach for the Integrated Airline Fleet Assignment, Aircraft Routing, and Crew Pairing Problem," Transportation Science, INFORMS, vol. 51(1), pages 233-249, February.
    20. Ragheb Rahmaniani & Shabbir Ahmed & Teodor Gabriel Crainic & Michel Gendreau & Walter Rei, 2020. "The Benders Dual Decomposition Method," Operations Research, INFORMS, vol. 68(3), pages 878-895, May.
    21. Nathan Sudermann‐Merx & Steffen Rebennack & Christian Timpe, 2021. "Crossing Minimal Edge‐Constrained Layout Planning using Benders Decomposition," Production and Operations Management, Production and Operations Management Society, vol. 30(10), pages 3429-3447, October.
    22. Vedat Bayram & Hande Yaman, 2018. "Shelter Location and Evacuation Route Assignment Under Uncertainty: A Benders Decomposition Approach," Transportation Science, INFORMS, vol. 52(2), pages 416-436, March.
    23. N. Beheshti Asl & S. A. MirHassani, 2019. "Accelerating benders decomposition: multiple cuts via multiple solutions," Journal of Combinatorial Optimization, Springer, vol. 37(3), pages 806-826, April.

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