MIP reformulations of the probabilistic set covering problem Authors Anureet Saxena Tepper School of Business Carnegie Mellon University Vineet Goyal Tepper School of Business Carnegie Mellon University Miguel A. Lejeune LeBow College of Business Drexel University FULL LENGTH PAPER Series A

First Online: 17 June 2008 Received: 06 February 2007 Accepted: 18 March 2008 DOI :
10.1007/s10107-008-0224-y

Cite this article as: Saxena, A., Goyal, V. & Lejeune, M.A. Math. Program. (2010) 121: 1. doi:10.1007/s10107-008-0224-y
Abstract In this paper, we address the following probabilistic version (PSC) of the set covering problem: \({\min\{cx\,|\,{\mathbb P}(Ax \ge \xi) \ge p, x \in \{0, 1\}^N\}}\) where A is a 0-1 matrix, \({\xi}\) is a random 0-1 vector and \({p \in (0,1]}\) is the threshold probability level. We introduce the concepts of p -inefficiency and polarity cuts. While the former is aimed at deriving an equivalent MIP reformulation of (PSC), the latter is used as a strengthening device to obtain a stronger formulation. Simplifications of the MIP model which result when one of the following conditions hold are briefly discussed: A is a balanced matrix, A has the circular ones property, the components of \({\xi}\) are pairwise independent, the distribution function of \({\xi}\) is a stationary distribution or has the disjunctive shattering property. We corroborate our theoretical findings by an extensive computational experiment on a test-bed consisting of almost 10,000 probabilistic instances. This test-bed was created using deterministic instances from the literature and consists of probabilistic variants of the set covering model and capacitated versions of facility location, warehouse location and k -median models. Our computational results show that our procedure is orders of magnitude faster than any of the existing approaches to solve (PSC), and in many cases can reduce hours of computing time to a fraction of a second.

Keywords Probabilistic programming Set covering Mixed integer programming Cutting planes Anureet Saxena’s research was supported by the National Science Foundation through grant
#DMI-0352885 and by the Office of Naval Research through contract N00014-03-1-0133. Vineet Goyal’s research was supported in part by NSF grant CCF-0430751 and ITR grant CCR-0122581.

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