One cannot make truly fair decisions using integer linear programs unless one controls the selection probabilities of the (possibly many) optimal solutions. For this purpose, we propose a unified framework when binary decision variables represent agents with dichotomous preferences, who only care about whether they are selected in the final solution. We develop several general-purpose algorithms to fairly select optimal solutions, for example, by maximizing the Nash product or the minimum selection probability, or by using a random ordering of the agents as a selection criterion (Random Serial Dictatorship). We also discuss in detail how to extend the proposed methods when agents have cardinal preferences. As such, we embed the “black-box” procedure of solving an integer linear program into a framework that is explainable from start to finish. Lastly, we evaluate the proposed methods on two specific applications, namely kidney exchange (dichotomous preferences), and the scheduling problem of minimizing total tardiness on a single machine (cardinal preferences). We find that while the methods maximizing the Nash product or the minimum selection probability outperform the other methods on the evaluated welfare criteria, methods such as Random Serial Dictatorship perform reasonably well in computation times that are similar to those of finding a single optimal solution.

We study the assignment of indivisible objects to individuals when transfers are not allowed. Previous literature has mainly focused on efficiency (from an ex-ante and ex-post perspective), and individually fair assignments. Consequently, egalitarian concerns have been overlooked. We are inspired by the assignment of apartments in housing cooperatives where families regard the egalitarianism of the assignments as a first-order requirement. In particular, they want to avoid assignments where some families get their most preferred apartment, while others get options ranked very low in their preferences. Based on Rawls' idea of fairness, we introduce the notion of Rawlsian assignments. We prove that there always exists a unique Rawlsian assignment, which is ordinally efficient, and satisfies equal treatment of equals. We illustrate our analysis with preference data from housing cooperatives. Our results show that the Rawlsian assignment substantially improves, from an egalitarian perspective, both the probabilistic serial mechanism, and the mechanism currently in use.