An unconventional clustering problem: User Service Profile Optimization

We consider the problem of clustering N users into K groups such that users in the same group are assigned a common service profile over M commodities. The profile of each group k sets for each commodity m the maximum of the service quality that users in the k-th group are willing to pay. The objective is to find the clustering that maximizes the total service user quality, which corresponds to the revenue of the service provider. This Service Profile Optimization Problem (SPOP) emerges in various applications, as for example the bit-loading in Hybrid Fiber Coax data distribution systems. We propose a Mixed Integer Linear Programming (MILP) model for the problem, that allows to use state-of-the-art MILP solvers as the core tool in an original powerful heuristic. We show complexity and performance gains with respect to previously proposed methods and a direct application of a state of the art MILP solver.