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Energy optimisation is one of the important issues in the research of wireless sensor networks (WSNs). In the application of monitoring, a large number of sensors are scattered uniformly to cover a collection of points of interest (PoIs) distributed randomly in the monitored area. Since the energy of battery-powered sensor is limited in WSNs, sensors are scheduled to wake up in a large-scale sensor network application. In this paper, we consider how to reduce the energy consumption and prolong the lifetime of WSNs through wake-up scheduling with probabilistic sensing model in the large-scale application of monitoring. To extend the lifetime of sensor network, we need to balance the energy consumption of sensors so that there will not be too much redundant energy in some sensors before the WSN terminates. The detection probability and false alarm probability are taken into consideration to achieve a better performance and reveal the real sensing process which is characterised in the probabilistic sensing model. Data fusion is also introduced to utilise information of sensors so that a PoI in the monitored area may be covered by multiple sensors collaboratively, which will decrease the number of sensors that cover the monitored region. Based on the probabilistic model and data fusion, minimum weight probabilistic coverage problem is formulated in this paper. We also propose a greedy method and modified genetic algorithm based on the greedy method to address the problem. Simulation experiments are conducted to demonstrate the advantages of our proposed algorithms over existing work. [ABSTRACT FROM AUTHOR]

In this paper, the Rayleigh-Lindley (RL) distribution is introduced, obtained by compounding the Rayleigh and Lindley discrete distributions, where the compounding procedure follows an approach similar to the one previously studied by Adamidis and Loukas in some other contexts. The resulting distribution is a two-parameter model, which is competitive with other parsimonious models such as the gamma and Weibull distributions. We study some properties of this new model such as the moments and the mean residual life. The estimation was approached via EM algorithm. The behavior of these estimators was studied in finite samples through a simulation study. Finally, we report two real data illustrations in order to show the performance of the proposed model versus other common two-parameter models in the literature. The main conclusion is that the model proposed can be a valid alternative to other competing models well established in the literature. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to describe a new formulation of the mixed-model sequencing model in such a way as to minimize the risk of stopping the conveyor under the circumstances of system variability and to develop an efficient heuristic method for large-scale, mixed-model assembly lines. The proposed method always produced optimal solutions for randomly constructed small-scale, mixed-model sequencing problems for which the optimal solutions were available through the application of the improved branch and bound method. The method should produce excellent (and almost always optimal) results for actual large-scale, mixed-model sequencing problems. [ABSTRACT FROM AUTHOR]

This paper deals with the severity of ruin in a discrete semi-Markov risk model. It is shown that the work of Reinhard and Snoussi (Stochastic Models, 18) can be extended to cover the case where the premium is an integer value and no restriction on the annual result is imposed. In particular, it is shown that the severity of ruin without initial surplus is solution of a system of equations. It can be obtained by a monotonically converging algorithm when the claims are bounded. [ABSTRACT FROM AUTHOR]

The main reason for the limited use of multivariate discrete models is the difficulty in calculating the required probabilities. The task is usually undertaken via recursive relationships which become quite computationally demanding for high dimensions and large values. The present paper discusses efficient algorithms that make use of the recurrence relationships in a manner that reduces the computational effort and thus allow for easy and cheap calculation of the probabilities. The most common multivariate discrete distribution, the multivariate Poisson distribution is treated. Real data problems are provided to motivate the use of the proposed strategies. Extensions of our results are discussed. It is shown that probabilities, for a large family of multivariate distributions, can be computed efficiently via our algorithms. [ABSTRACT FROM AUTHOR]

This paper develops rolling planning horizon policies to manage material flows in multi-echelon supply — distribution networks with relatively general stochastic demand processes and procurement, transportation, inventory and shortage cost structures. Initially, the problem is formulated as a stochastic program with recourse, and its deterministic equivalent program is approximated by a multi-echelon lot-sizing model based on "risk inflated effective demands." A DRP — decomposition of this approximate model, which can be used with planning time fences or allocation algorithms, is then introduced. The use of expediting actions is also discussed. Finally, through a set of simulation experiments, the solutions obtained with our planning policies are compared with the solutions given by a classical DRP approach using safety stocks. The results show that the approach proposed leads to significant improvements. [ABSTRACT FROM AUTHOR]

This study introduces the directionality effect of the ground motion in the probabilistic seismic risk assessment (PSRA) of lifeline systems. Given an earthquake scenario, the seismic wave strikes each component of the system with a different angle. The angle may vary significantly depending on the shape, the location and the orientation of the structure. An appropriate example of a lifeline system is a highway transportation network, in which under earthquake conditions the bridges are considered the most vulnerable components. The proposed PSRA model requires that the seismic fragility model is a function of a ground motion intensity measure (IM), as in the traditional risk analysis, and the angle of seismic incidence. The model was implemented in a new framework for the PSRA of highway transportation network. In addition, the framework includes new algorithms. One reduces the confidence interval of the results and one increases the computational efficiency. The example used is the highway transportation network serving the Los Angeles area, which has more than 3000 bridges. The results show a considerable difference in the system resilience with or without the seismic directionality taken into consideration. This is important for benefit/cost analysis and it represents a clear departure from the current risk analysis. [ABSTRACT FROM AUTHOR]

In some practical inferential situations, it is needed to mix some finite sort of distributions to fit an adequate model for multi-modal observations. In this article, using evidential analysis, we determine the sample size for supporting hypotheses about the mixture proportion and homogeneity. An Expectation-Maximization algorithm is used to evaluate the probability of strong misleading evidence based on modified likelihood ratio as a measure of support. [ABSTRACT FROM AUTHOR]

Using the solution of one-sided exit problem, a procedure to price Parisian barrier options in a jump-diffusion model with two-sided exponential jumps is developed. By extending the method developed in Chesney, Jeanblanc-Picqué and Yor (1997; Brownian excursions and Parisian barrier options, Advances in Applied Probability, 29(1), pp. 165–184) for the diffusion case to the more general set-up, we arrive at a numerical pricing algorithm that significantly outperforms Monte Carlo simulation for the prices of such products. [ABSTRACT FROM AUTHOR]

Describes an algorithm for finding the probability distribution of the sum of numbers obtained when a number of balanced dice are rolled. Generalization of algorithm to encompass any discrete uniform distribution; Implications for pedagogical applications.