Optimizing Weak Orders via Integer Linear Programming

TL;DR

This paper presents a novel ILP framework for solving rank aggregation problems involving weak orders, including various constraints, and demonstrates its effectiveness through computational experiments.

Contribution

It introduces the first exact ILP formulation for the Optimal Bucket Order Problem and extends it to various constrained rank aggregation scenarios.

Findings

Exact ILP models outperform heuristics in accuracy.

Models efficiently solve instances from the PrefLib library.

Framework supports diverse constraints like fixed buckets and fairness.

Abstract

Rank aggregation problems aim to combine multiple individual orderings of a common set of items into a consensus ranking that best reflects the collective preferences. This paper introduces a general Integer Linear Programming (ILP) framework to model and solve, in an exact way, problems whose solutions are weak orders (a.k.a.\ bucket orders). Within this framework, we consider additional relevant constraints to produce the consensus bucket order, considering configurations with a fixed number of buckets, predefined bucket sizes, top-$k$ type problems, and fairness constraints. All these formulations are developed in a general setting, allowing their application to different rank aggregation contexts. One of these problems is the Optimal Bucket Order Problem (OBOP), for which we propose for the first time an exact formulation and test the variants proposed. The computational study includes, on the one hand, a comparison between the exact results obtained by our models and the heuristic methods proposed by Aledo et al.\ (2018), and on the other hand, an additional evaluation of their performance on a representative set of instances from the PrefLib library. The results confirm the validity and efficiency of the proposed approach, providing a solid foundation for future research on rank aggregation problems with weak orders as consensus rankings.