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Abstract. Closeness centrality is a class of distance-based measures in the network analysis literature to quantify reachability of a given vertex (or a group of vertices) by other network agents. [...]

Abstract. Influenza (flu) is a serious public health concern. The first line of defense is the flu shot, whose composition is updated annually to adjust for frequent mutations of the [...]

Many patients face difficulties when accessing medical facilities, particularly in rural areas. To alleviate these concerns, medical centers may offer transportation to eligible patients. However, the operation of such services [...]

Implantable cardioverter defibrillators (ICDs) include small, battery-powered generators, the longevity of which depends on a patient's rate of consumption. Generator replacement, however, involves risks, including death. Hence, a trade-off exists between prematurely exposing the patient to these risks and allowing for the possibility that the device is unable to deliver therapy when needed. Currently, replacements are performed using a one-size-fits-all approach. Here, we develop a Markov decision process model to determine patient-specific optimal replacement policies as a function of patient age and the remaining battery capacity. We analytically establish that the optimal policy is of threshold-type in the remaining capacity, but not necessarily in patient age. Based on clinical data, we conduct a large computational study that suggests that under the optimal policy, patients undergoing initial implantation at age 30-40, 41-60, and 61-80 see an approximate decrease in the total expected number of replacements of 8%-14%, 8%-15% and 8%-19%, respectively, while achieving the same or greater expected lifetime. Keywords: optimal replacement; Markov decision processes; threshold policy, 1. Introduction Over 650,000 people in the United States currently rely on implanted cardiac devices for defibrillation and/or pacing, and this number is growing exponentially (Meier 2010, Stellbrink and Trappe [...]

Scheduling elective procedures in an operating suite is a formidable task because of competing performance metrics and uncertain surgery durations. In this paper, we present an optimization framework for batch [...]

We discuss two possible parallel strategies for randomized restart algorithms. Given a set of available algorithms, one can either choose the best performing algorithm and run its multiple copies in [...]

In this paper we consider the order-preserving submatrix (OPSM) problem. This problem is known to be NP-hard. Although in recent years some heuristic methods have been presented to find OPSMs, [...]