Generating Cuts from Surrogate Constraint Analysis for Zero-One and Multiple Choice Programming

This paper presents a new surrogate constraint analysis that givesrise to a family of strong valid inequalities calledsurrogate-knapsack (S-K) cuts. The analytical procedure presentedprovides a strong S-K cut subject to constraining the values ofselected cut coefficients, including the right-hand side. Ourapproach is applicable to both zero-one integer problems and problemshaving multiple choice (generalized upper bound) constraints. We alsodevelop a strengthening process that further tightens the S-K cutobtained via the surrogate analysis. Building on this, we develop apolynomial-time separation procedure that successfully generates anS-K cut that renders a given non-integer extreme point infeasible. Weshow how sequential lifting processes can be viewed in our framework,and demonstrate that our approach can obtain facets that are notavailable to standard lifting methods. We also provide a relatedanalysis for generating fast cuts. Finally, we presentcomputational results of the new S-K cuts for solving 0-1 integerprogramming problems. Our outcomes disclose that the new cuts arecapable of reducing the duality gap between optimal continuous andinteger feasible solutions more effectively than standard liftedcover inequalities, as used in modern codes such as the CPLEX mixed0-1 integer programming solver.