Pattern-based decompositions for human resource planning in home health care services

Home health care services play acrucial role in reducing the hospitalization costs due to the increase of chronic diseases of elderly people. At the same time, they allow us to improve the quality of life for those patients that receive treatments at their home. Optimization tools are therefore necessary to plan service delivery at patients' homes. Recently, solution methods that jointly address the assignment of the patient to the caregiver (assignment), the definition of the days (pattern) in which caregivers visit the assigned patients (scheduling), and the sequence of visits for each caregiver (routing) have been proposed in the scientific literature. However, the joint consideration of these three level of decisions may be not affordable for large providers, due to the required computational time. In order to combine the strength and the flexibility guaranteed by a joint assignment, scheduling and routing solution approach with the computational efficiency required for large providers, in this study we propose a new family of two-phase methods that decompose the joint approach by incrementally incorporating some decisions into the first phase.The concept of pattern is crucial to perform such a decomposition in a clever way. Several scenarios are analyzed by changing the way in which resource skills are managed and the optimization criteria adopted to guide the provider decisions. The proposed methods are tested on realistic instances. The numerical experiments help us to identify the combinations of decomposition techniques, skill management policies and optimization criteria that best fit with problem instances of different size

The Doppel system for controlled testing of sensor network apps

Application software or apps for wireless sensor networks vary widely and are customized to run on resource-constrained sensor nodes. This places restrictions on how it can be tested especially when running on the target hardware and operating as a network. Any changes to the app for testing could result in non-trivial observer effects especially if the testing methodology requires use of the already scarce resources. This leads us to the question of whether a system capable of testing sensor network apps while not using resources of the sensor nodes could be built. The objective of this research was to answer this question by designing and building the Doppel system which allows testing sensor network apps operating as a network. We present an architecture that utilizes sensor nodes to provide the required sensory input and exercise control over the sensor nodes that are executing the app under test. In our architecture, each sensor node executing the app under test is paired with a modified sensor node called the control node. We showcase an implementation of the architecture using the MICAz sensor node platform and TinyOS operating system software. Evaluation results in a network setting are also presented. Our architecture provides the benefits of both hardware-based and software-based approaches to testing sensor network apps. To manage the system efficiently when scaling up the system, there arises a need to find the optimal placement of base stations, what data each base station holds for operating the system and how the data needs to be routed. We modeled this optimization problem as the well-known facility location problem, and provided a hybrid algorithm that uses simulated annealing and a standalone solver to solve the problem.Ph.D., Electrical Engineering -- Drexel University, 201

Proceedings of the 26th International Symposium on Theoretical Aspects of Computer Science (STACS'09)

The Symposium on Theoretical Aspects of Computer Science (STACS) is held alternately in France and in Germany. The conference of February 26-28, 2009, held in Freiburg, is the 26th in this series. Previous meetings took place in Paris (1984), Saarbr¨ucken (1985), Orsay (1986), Passau (1987), Bordeaux (1988), Paderborn (1989), Rouen (1990), Hamburg (1991), Cachan (1992), W¨urzburg (1993), Caen (1994), M¨unchen (1995), Grenoble (1996), L¨ubeck (1997), Paris (1998), Trier (1999), Lille (2000), Dresden (2001), Antibes (2002), Berlin (2003), Montpellier (2004), Stuttgart (2005), Marseille (2006), Aachen (2007), and Bordeaux (2008). ..