As a general branch of project scheduling problems, resource investment problem (RIP) considers resource availabilities as decision variables to determine a level of employed resources minimizing the costs of the project. In addition to costs (cash outflows), researchers in the later extensions of the RIP took payments (cash inflows) received from clients into account and used the net present v...

In this paper, we extend the mean-variance portfolio model to explicitly account for uncertainty about the estimated expected returns and/or the underlying return-generating model. We do this by first imposing an additional constraint on the mean-variance portfolio optimization program that restricts each parameter to lie within a specified confidence interval of its estimated value, and then b...

This paper uses Taylor series expansions and the assumption of small risks to derive a co-moment criterion that firms should maximize so that the resulting equilibrium is Pareto optimal. This is done in two models of production under uncertainty: the state-of-nature (SN ) model in which the firms’ outputs depend on states of nature and financial markets are complete with respect to these states...

Current assets and cash in enterprise are maintained for risk reduction purposes. The basic financial purpose of an enterprise is maximization of its value. Cash and current assets management should also contribute to realization of this fundamental aim. The enterprise value maximization strategy is executed with a focus on risk and uncertainty. This article presents the consequences that can r...

As more and more information on individuals and companies are placed in the cloud, concerns are beginning to grow about just how safe an environment it is. It is better to prevent security threats before they enter into the systems and there is no way how this can be prevented without knowing where they come from. The issue of resource allocation and revenue maximization is also equally importa...

Foraging theory has been the inspiration for several decision-making algorithms for task-processing agents facing random environments. As nature selects for foraging behaviors that maximize lifetime calorie gain or minimize starvation probability, engineering designs are favored that maximize returned value (e.g. profit) or minimize the probability of not reaching performance targets. Prior for...

this paper examines whether combining generalized hyperbolic skew-t distribution, recently introduced in the field of insurance, and extreme value theory (evt) could result in a modeling of loss function that could model central value as well as extreme value in appropriate manner. the data used in this study are the amount of property damage and bodily injury covered under automobile liability...

The expectation maximization (EM) algorithm computes maximum likelihood estimates of unknown parameters in probabilistic models involving latent variables. More pragmatically speaking, the EM algorithm is an iterative method that alternates between computing a conditional expectation and solving a maximization problem, hence the name expectation maximization. We will in this work derive the EM ...

Clustering multivariate data that are contaminated by noise is a complex issue, particularly in the framework of mixture model estimation because noisy data can significantly affect the parameters estimates. This paper addresses this problem with respect to likelihood maximization using the Expectation-Maximization algorithm. Two different approaches are compared. The first one consists in defi...

Influence maximization in social networks has been studied extensively in computer science community for the last decade. However, almost all of the efforts have been focused on the progressive influence models, such as independent cascade (IC) and Linear threshold (LT) models, which cannot capture the reversibility of choices. In this paper, we present the Heat Conduction (HC) model which is a...