A LP-based neighborhood search for general integer programs

Authors:
Qun Gu;Xinhui Zhang;S. Narayanan
Affiliations:
Department of Biomedical, Industrial and Human Factors Engineering, Wright State University, Dayton, OH;Department of Biomedical, Industrial and Human Factors Engineering, Wright State University, Dayton, OH;Department of Biomedical, Industrial and Human Factors Engineering, Wright State University, Dayton, OH
Venue:
ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
Year:
2008

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Abstract

This paper presents a methodology to generate diverse solutions for linear programming relaxation and its application to solve integer programs using a neighborhood search. The algorithm generates multiple linear programming solutions of maximal difference and uses them as targets for a neighborhood search to locate high-quality integer solutions. The multiple diverse solutions provide a good coverage of the solution landscape and the neighborhood search reduces the computational effort in searching for good integer solutions. The algorithm is seeded into a genetic algorithm on benchmark knapsack problem and the results have shown that the algorithm is computationally effective, providing high-quality solutions at faster convergence rates than state-of-the-art commercial integer program solvers.