Accurately predicting whether consent for organ donation will be granted is essential for optimizing timing and resource use in donor management.This study develops and evaluates machine learning models to estimate the likelihood of obtaining consent based on donor and contextual factors.The goal is to support early clinical decision-making by identifying cases where consent is more or less likely.Using real-world data from a regional transplant center operating under an opt-in system, we conduct data preprocessing, feature selection, and model training with various algorithms.Model performance is assessed using standard classification metrics, and key predictors of consent outcomes are identified.Results show accuracy levels exceeding 80%, highlighting the importance of including information about the relatives responsible for the decision.We also find that prediction accuracy varies with donor nationality, being higher for non-Italian donors.These findings demonstrate the value of predictive analytics in improving organ procurement efficiency and reducing unnecessary costs.

The insurgence of COVID-19 requires fast mass vaccination, hampered by scarce availability and uncertain supply of vaccine doses and a tight schedule for boosters.In this paper, we analyze planning strategies for the vaccination campaign to vaccinate as many people as possible while meeting the booster schedule.We compare a conservative strategy and q-days-ahead strategies against the clairvoyant strategy.The conservative strategy achieves the best trade-off between utilization and compliance with the booster schedule.Q-days-ahead strategies with q < 7 provide a larger utilization but run out of stock in over 30% of days.

Flows over time problems relate to finding optimal flows over a capacitated network where transit times on network arcs are explicitly considered. In this article, we study the problem of determining a minimum cost origin‐destination path where the cost and the travel time of each arc depend on the time taken to travel from the origin to that particular arc along the path. We provide computational complexity results for this problem and an exact solution algorithm based on an enumeration scheme on the corresponding time expanded network. Finally, we show the efficiency of our approach through a number of experimental tests. © 2016 Wiley Periodicals, Inc. NETWORKS, Vol. 69(1), 23–32 2017

Abstract This article addresses the problem of allocating users to radio resources in the downlink of an OFDMA cellular system. We consider a classical multicellular environment with a realistic interference model and a margin adaptive approach, i.e., we aim at minimizing total transmission power while maintaining a certain given rate for each user. We discuss computational complexity issues of the resulting model and present a heuristic approach that finds optima under suitable conditions or reasonably good solutions in the general case. Computational experiments show the effectiveness of the proposed heuristic in a comparison with both a commercial state‐of‐the‐art optimization solver and other approaches from the literature. © 2011 Wiley Periodicals, Inc. NETWORKS, 2011

In this article we consider multicriteria formulations of classical online problems in which an algorithm must simultaneously perform well with respect to two different cost measures. Every strategy for serving a sequence of requests is characterized by a pair of costs and therefore there can be many different minimal or optimal incomparable solutions. The adversary is assumed to choose from one of these minimal strategies and the performance of the algorithm is measured with respect to the costs the adversary pays servicing the sequence according to its determined choice of strategy. We consider a parametric family of functions which includes all the possible selections for such strategies. Then, starting from a simple general method that combines any multicriteria instance into a single-criterion one, we provide a universal multicriteria algorithm that can be applied to different online problems. In the multicriteria k -server formulation with two different edge weightings, for each function class, such a universal algorithm achieves competitive ratios that are only an O (log W ) multiplicative factor away from the corresponding determined lower bounds, where W is the maximum ratio between the two weights associated to each edge. We then extend our results to two specific functions, for which nearly optimal competitive algorithms are obtained by exploiting more knowledge of the selection properties. Finally, we show how to apply our framework to other multicriteria online problems sharing similar properties.

We address a class of single-machine, hard scheduling problems with the objective of minimizing the maximum tardiness. The jobs can be preempted, but whenever a job is started or resumed, there is a recovery interval affecting its progress. Such a feature is motivated by certain application environments and generalizes the usual preemption concept. For three different cases of this problem, we propose a heuristic algorithm, based on the partial enumeration of feasible schedules. A packing formulation solved by means of a column generation approach is used to certify the quality of the heuristic solution. An extensive computational experience shows the effectiveness of the approach on different classes of instances and shows that real-size problems can be solved to optimality in an acceptable amount of time.