Kidney exchange programs enable willing, but incompatible, donor-patient pairs to swap donors, thus allowing persons suffering from organ failure to access transplantation. Choosing which pairs to match requires solving a stochastic online optimization problem where patients and donors arrive over time. Despite this, most of the related scientific literature has focused on deterministic offline models. In this paper, we present a simple approach to employ a model for the offline Kidney Exchange Problem (KEP) as the basis of an on-line anticipatory algorithm. Our approach grounds on existing techniques for the on-line KEP, but it generalizes them and provides a more accurate estimate of the expected impact of current decisions. In an experimentation based on a state-of-the-art donor pool generation method, the approach provides improvements in terms of quality and is able to deal with realistic instance size in reasonable time.

We study the problem of matching a set of applicants to a set of posts, where each applicant has an ordinal preference list, which may contain ties, ranking a subset of posts. A matching M is popular if there exists no matching M' where more applicants prefer M' to M . Several notions of optimality are studied in the literature for the case of strictly ordered preference lists. In this paper we address the case involving ties and propose novel algorithmic and complexity results for this variant. Next, we focus on the NP-hard case where additional copies of posts can be added in the preference lists, called Popular Matching with Copies. We define new dominance rules for this problem and present several novel graph properties characterising the posts that should be copied with priority. We present a comprehensive set of experiments for the popular matching problem with copies to evaluate our dominance rules as well as the different branching strategies. Our experimental study emphasizes the importance of the dominance rules and characterises the key aspects of a good branching strategy.

We propose a constraint programming approach to the popular matching problem. We show that one can use the Global Cardinality Constraint to encode the problem even in cases that involve ties in the ordinal preferences of the applicants.

The power consumption of a data centre (DC)can be attributed to the power consumed for running the servers and to the computer room air conditioner (CRAC)power for cooling them. The challenge is to distribute the load among servers, controlling the number of active servers and optimally balancing IT and cooling power requirement. This goal demands integration of thermal, power and workload models that minimises a non-linear energy utilisation function. In this paper first we encode this problem using a non-linear objective function and use local search for solving it. We carryout simulation experiments using data provided by the Bluesimtool. The results encourage the effectiveness of our approach, showing that system-wide energy utilisation can be reduced using a holistic approach.