Improving benders decomposition using a genetic algorithm

C.A. Poojari and John Beasley

European Journal of Operational Research, 2009, vol. 199, issue 1, 89-97

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.

Keywords: Genetic; algorithm; Benders; decomposition; Mixed-integer; linear; programs (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (23)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:199:y:2009:i:1:p:89-97

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