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General Purpose Heuristics for Integer Programming—Part II
Journal of Heuristics
Octane: A New Heuristic for Pure 0-1 Programs
Operations Research
Exploring relaxation induced neighborhoods to improve MIP solutions
Mathematical Programming: Series A and B
Mathematical Programming: Series A and B
Pivot and shift-a mixed integer programming heuristic
Discrete Optimization
Mixed-integer nonlinear programming: some modeling and solution issues
IBM Journal of Research and Development - Business optimization
A Generalized Wedelin Heuristic for Integer Programming
INFORMS Journal on Computing
A linear relaxation-based heuristic approach for logistics network design
Computers and Industrial Engineering
An interior point cutting plane heuristic for mixed integer programming
Computers and Operations Research
Experiments with a feasibility pump approach for nonconvex MINLPs
SEA'10 Proceedings of the 9th international conference on Experimental Algorithms
Heuristic and exact methods for the discrete (r|p)-centroid problem
EvoCOP'10 Proceedings of the 10th European conference on Evolutionary Computation in Combinatorial Optimization
Feasibility pump heuristics for column generation approaches
SEA'12 Proceedings of the 11th international conference on Experimental Algorithms
An empirical evaluation of walk-and-round heuristics for mixed integer linear programs
Computational Optimization and Applications
Recursive central rounding for mixed integer programs
Computers and Operations Research
Feasibility Pump-like heuristics for mixed integer problems
Discrete Applied Mathematics
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Finding a feasible solution of a given Mixed-Integer Programming (MIP) model is a very important (NP-complete) problem that can be extremely hard in practice. Very recently, Fischetti, Glover and Lodi proposed a heuristic scheme for finding a feasible solution to general MIPs, called a Feasibility Pump (FP). According to the computational analysis reported by these authors, FP is indeed quite effective in finding feasible solutions of hard 0-1 MIPs. However, MIPs with general-integer variables seem much more difficult to solve by using the FP approach. In this paper we elaborate on the Fischetti-Glover-Lodi approach and extend it in two main directions, namely (i) handling as effectively as possible MIP problems with both binary and general-integer variables, and (ii) exploiting the FP information to drive a subsequent enumeration phase. Extensive computational results on large sets of test instances from the literature are reported, showing the effectiveness of our improved FP scheme for finding feasible solutions to hard MIPs with general-integer variables.