Data envelopment analysis (DEA) is a nonparametric approach to evaluate theefficiency of decision making units (DMU) using mathematical programmingtechniques. Almost, all of the previous researches in stochastic DEA have been usedthe stochastic data when the inputs and outputs are normally distributed. But, thisassumption may not be true in practice. Therefore, using a normal distribution wi...

We study the effect of noise on the evolution of the growth factor of density perturbations in the context of the ΛCDM model. Stochasticity is introduced as a Wiener process amplified by an intensity parameter α. By comparing the evolution of deterministic and stochastic cases for different values of α we estimate the intensity level necessary to make noise relevant for cosmological tests based...

We consider a manufacturer’s stochastic production/inventory problem under periodic review and present concepts for safety stock determination to cope with uncertainties that are caused by stochastic demand and different types of yield randomness. Order releases follow a linear control rule. Taking manufacturing lead times into account it turns out that safety stocks have to be considered that ...

The divergence over the years of research paradigms addressing the production planning problemhas led to the development of an extensive set of techniques, each of which can address a particular aspect of the practical problem and none of which provides a complete solution. In particular, most approaches fail to address the circular, non-linear dependency between resource utilization, lead-time...

We consider a class of piecewise-deterministic Markov processes where the state evolves according to a linear dynamical system. This continuous time evolution is interspersed by discrete events that occur at random times and change (reset) the state based on a linear affine map. In particular, we consider two families of discrete events, with the first family of resets occurring at exponentiall...