Given a set [Formula: see text] of size n, a nonnegative, integer-valued distance matrix D of dimensions [Formula: see text], an integer [Formula: see text] and an integer-valued weight vector [Formula: see text], the discrete ordered median problem (DOMP) consists of selecting a subset [Formula: see text] of exactly p points from [Formula: see text] (also referred to as the centers) so as to: 1) assign each point in [Formula: see text] to its closest center in [Formula: see text]; 2) rank the resulting distances (between every point and its center) from smallest to largest in a sorted vector that we denote [Formula: see text]; 3) minimize the scalar product [Formula: see text]. The DOMP generalizes several classical location problems such as the p-center, the p-median and the obnoxious median problem. We introduce an exact branch-and-bound algorithm to solve the DOMP. This branch-and-bound decouples the ranking attribute of the problem to form a series of simpler subproblems which are solved using innovative binary search methods. We consider several acceleration techniques such as warm-starts, primal heuristics, variable fixing, and symmetry breaking. We perform a thorough computational analysis and show that the proposed method is competitive against several MIP models from the scientific literature. We also comment on the limitations of our method and propose avenues of future research. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms—Discrete. Funding: This work was supported by the Natural Sciences and Engineering Research Council of Canada [Grants 2017-06106, 2020-06311, and 2021-03327]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0059 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0059 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Given a ground-set of elements and a family of subsets, the set covering problem consists in choosing a minimum number of elements such that each subset contains at least one of the chosen elements. This research focuses on the set covering polytope , which is the convex hull of integer solutions to the set covering problem. We investigate the connection between the study of the facets of the set covering polytope and tilting theory. This theory studies how inequalities can be rotated around their contact points with a polyhedron in order to obtain inequalities inducing higher dimensional faces . To study this connection, we introduce the concept of tilting vectors which characterize the degrees of freedom of rotation of an inequality. These vectors characterize facet-defining inequalities and can be used to tilt inequalities with a similar procedure to the one used for arbitrary polyhedra. Additionally, we demonstrate that the computational effort needed to tilt an inequality can be reduced when the inequality has many null coefficients. Finally, we use the tilting vectors to extend several necessary and/or sufficient conditions for facets of the set covering polytope presented by several previous works of the literature.