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Improving benders decomposition using a genetic algorithm

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  • Poojari, C.A.
  • Beasley, J.E.

Abstract

We develop and investigate the performance of a hybrid solution framework for solving mixed-integer linear programming problems. Benders decomposition and a genetic algorithm are combined to develop a framework to compute feasible solutions. We decompose the problem into a master problem and a subproblem. A genetic algorithm along with a heuristic are used to obtain feasible solutions to the master problem, whereas the subproblem is solved to optimality using a linear programming solver. Over successive iterations the master problem is refined by adding cutting planes that are implied by the subproblem. We compare the performance of the approach against a standard Benders decomposition approach as well as against a stand-alone solver (Cplex) on MIPLIB test problems.

Suggested Citation

  • Poojari, C.A. & Beasley, J.E., 2009. "Improving benders decomposition using a genetic algorithm," European Journal of Operational Research, Elsevier, vol. 199(1), pages 89-97, November.
    • Handle: RePEc:eee:ejores:v:199:y:2009:i:1:p:89-97
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    References listed on IDEAS

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    1. Poojari, Chandra A. & Varghese, Boby, 2008. "Genetic Algorithm based technique for solving Chance Constrained Problems," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1128-1154, March.
    2. Cote, Gilles & Laughton, Michael A., 1984. "Large-scale mixed integer programming: Benders-type heuristics," European Journal of Operational Research, Elsevier, vol. 16(3), pages 327-333, June.
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    9. Raidl, Günther R., 2015. "Decomposition based hybrid metaheuristics," European Journal of Operational Research, Elsevier, vol. 244(1), pages 66-76.
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    14. 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.
    15. Jalali, Sajjad & Seifbarghy, Mehdi & Niaki, Seyed Taghi Akhavan, 2018. "A risk-averse location-protection problem under intentional facility disruptions: A modified hybrid decomposition algorithm," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 196-219.
    16. Gelareh, Shahin & Neamatian Monemi, Rahimeh & Nickel, Stefan, 2015. "Multi-period hub location problems in transportation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 75(C), pages 67-94.
    17. de Sá, Elisangela Martins & de Camargo, Ricardo Saraiva & de Miranda, Gilberto, 2013. "An improved Benders decomposition algorithm for the tree of hubs location problem," European Journal of Operational Research, Elsevier, vol. 226(2), pages 185-202.
    18. Soler, Edilaine Martins & de Sousa, Vanusa Alves & da Costa, Geraldo R.M., 2012. "A modified Primal–Dual Logarithmic-Barrier Method for solving the Optimal Power Flow problem with discrete and continuous control variables," European Journal of Operational Research, Elsevier, vol. 222(3), pages 616-622.
    19. Neamatian Monemi, Rahimeh & Gelareh, Shahin & Nagih, Anass & Maculan, Nelson & Danach, Kassem, 2021. "Multi-period hub location problem with serial demands: A case study of humanitarian aids distribution in Lebanon," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    20. Gelareh, Shahin & Pisinger, David, 2011. "Fleet deployment, network design and hub location of liner shipping companies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(6), pages 947-964.
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    22. 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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