To make a fair and satisfactory nurse shift schedule, this paper proposes a novel preference satisfaction function, in which numbers of the preferred work shifts and days-off of the nursing staff are balanced, and ranks for preferences and number of the preference ranks satisfied so far are also considered. Such a preference function is capable of equivalently and fairly planning the nurse preference schedule to improve the total satisfaction. Additionally, distributed sensors can be applied to collect the information on hospital beds to provide the schedule planner to determine the lowest required amount of manpower for each work shift, to avoid the working overload of the nursing staff. To solve the nursing schedule problem, we propose a genetic algorithm (GA) with an immigrant scheme, in which utilization of the immigrant scheme is helpful in efficiently reducing amount of infeasible solutions due to practical scheduling constraints, so that the GA can efficiently find better solutions for larger-scale problems. Performance of the proposed GA with and without solution recovery scheme is evaluated by conducting a comprehensive experimental analysis on three different-size instances.

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Time-constrained service plays an important role in ubiquitous services. However, the resource constraints of ubiquitous computing systems make it difficult to satisfy timing requirements of supported strategies. In this study, we study scheduling strategies for mobile data program with timing constraints in the form of deadlines. Unlike previously proposed scheduling algorithms for mobile systems which aim to minimize the mean access time, our goal is to identify scheduling algorithms for ubiquitous systems that ensure requests meet their deadlines. We present a study of the performance of traditional real-time strategies, and demonstrate that traditional real-time algorithms do not always perform the best in a mobile environment. We propose an efficient scheduling algorithm, called scheduling priority of mobile data with time constraint(SPMT), which is designed for timely delivery of data to mobile clients. The experimental results show that our approach outperforms other approaches over performance criteria.

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Many time-constraint applications operate on continuous queries and need real-time data services in ondemand mobile environments. Providing deadline guarantees for queries over dynamic multiple data is a challenging problem due to continuous query rates and time-varying contents. The accurately approximated optimum distributions is used to obtain analytical expressions for performance metrics such as average real-time miss rate and outage probability. The normal probability distribution function of client request to channel hopping is approximated. In the analysis multiple channel model based on currently transmitting is considered over independent and not identically distributed wireless channels. In addition the performance of currently transmitting is compared with the QoS-based management scheme for real-time query processing. The numerical results are validated by linear programming simulations. It is shown that for broadcasting real-time data placement, this contribution is very useful and efficient for exact performance analysis and design of wireless multi-channel links.

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