Suppose that we have a timetable of a round-robin tournament with a number of teams, and distances among their homes. The home-away assignment problem is to find a home-away assignment that minimizes the total traveling distance of the teams. We propose a formulation of the home-away assignment problem as an integer program, and a rounding algorithm based on Bertsimas, Teo and Vohra's dependent randomized rounding method [2]. Computational experiments show that our method quickly generates feasible solutions close to optimal.

Sports scheduling concerns how to construct a schedule of a sports competition mathematically. Many sports competitions are held as round-robin tournaments. In this paper, we consider a particular class of round-robin tournaments. We report some properties of round-robin tournaments and enumerate home-away tables satisfying some practical requirements by computer search.

This paper is concerned with a mixed-integer optimization (MIO) approach to selecting a subset of relevant features from among many candidates. For ordinal classification, a sequential logit model and an ordered logit model are often employed. For feature subset selection in the sequential logit model, Sato et al.[22] recently proposed a mixed-integer linear optimization (MILO) formulation. In their MILO formulation, a univariate nonlinear function contained in the sequential logit model was represented by a tangent-line-based approximation. We extend this MILO formulation toward the ordered logit model, which is more commonly used for ordinal classification than the sequential logit model is. Making use of tangent planes to approximate a bivariate nonlinear function involved in the ordered logit model, we derive an MILO formulation for feature subset selection in the ordered logit model. Our computational results verify that the proposed method is superior to the L1-regularized ordered logit model in terms of solution quality.