Solving mixed integer programming problems using automatic reformulation
Operations Research
Efficient reformulation for 0-1 programs: methods and computational results
Discrete Applied Mathematics - Special issue: combinatorial structures and algorithms
An efficient preprocessing procedure for the multidimensional 0–1 knapsack problem
Discrete Applied Mathematics - Special volume: viewpoints on optimization
Solving Multiple Knapsack Problems by Cutting Planes
SIAM Journal on Optimization
A Genetic Algorithm for the Multidimensional Knapsack Problem
Journal of Heuristics
Software section: MINTO, a mixed INTeger optimizer
Operations Research Letters
Exact solution of multicommodity network optimization problems with general step cost functions
Operations Research Letters
Very Large-Scale Neighborhood Search for the K-Constraint Multiple Knapsack Problem
Journal of Heuristics
An efficient algorithm for the collapsing knapsack problem
Information Sciences: an International Journal
Multicriteria 0-1 knapsack problems with k-min objectives
Computers and Operations Research
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We present an exact separation scheme for identifying most violated extended cover inequalities for application to multidimensional knapsack problems (MKP). The minimality of the resulting covers is shown to be a basic property of the criterion used for separation, namely the ratio between left- and right-hand sides of the extended cover inequality looked for. Computational results obtained on a set of randomly generated (MKP) instances together with instances from the OR-library with up to 180 variables and 60 constraints show significant reduction in overall computing times as compared with the standard version of Cplex 6.5 in MIP mode using automatic cover inequality generation.