In this paper, a reliability approach on reconfiguration decisions in a supply chain network is studied based on coupling the simulation concepts and artificial neural network. In other words, due to the limited budget for warehouse relocation in a supply chain, the failure probability is assessed for determining the robust decision for future supply chain configuration. Traditional solving ...

This paper discusses the analysis of stochastic Petri nets using the proxelbased simulation method. The paradigm of the proxel (”probability element”) was recently introduced in order to provide a new algorithmic approach to analysing discrete-state stochastic models such as are represented by stochastic Petri nets (SPNs) or queuing systems. Proxel-based simulation is not related to either of t...

In this study, the stochastic restricted and unrestricted two-parameter estimators of fixed and random effects are investigated in the linear mixed measurement error models. For this purpose, the asymptotic properties and then the comparisons under the criterion of mean squared error matrix (MSEM) are derived. Furthermore, the proposed methods are used for estimating the biasing parameters. Fin...

Several discrete, as well as continuous, stochastic approaches have been developed for the time-series simulation of biochemical systems. Stochastic approaches, in general, are needed because chemical reactions involve discrete, random collisions between individual chemical species. One of the well-known discrete stochastic approaches is the computationally demanding Gillespie stochastic simula...

Stochastic volatility models are increasingly important in practical derivatives pricing applications, yet relatively little work has been undertaken in the development of practical Monte Carlo simulation methods for this class of models. This paper considers several new algorithms for time-discretization and Monte Carlo simulation of Heston-type stochastic volatility models. The algorithms are...

Recent studies through biological experiments have indicated that noise plays a very important role in determining the dynamic behaviour of biological systems. The advances in stochastic modelling of genetic regulatory networks and cell signalling transduction pathways have proposed chemical reaction systems in which some key reactants have small copy numbers. Over the last years extensive rese...

The method of stochastic simulation is proposed to model the atmospheric effect on InSAR measurements based on sample data. Test results show that 37.44% reduction in the standard devisation of the atmospheric errors can be achieved with the method of stochastic simulation, compared to 25.69% with the method of Kriging interpolator. The relative improvement of the former over the latter amounts...

We examine, in a discrete-stochastic setting, the benefits and liabilities of replacing the three-reaction set 1 2 3 S S S → ⇌ with a single 3 S -producing reaction. We develop a novel criterion for deciding whether such an abridgment can be accomplished in a way that accurately replicates the production of 3 S molecules, and we derive a formula for estimating the consequent speedup in stochast...

3 Discrete stochastic models are widely used to describe current engineer4 ing and logistics problems. The stochastic simulation of such models can get 5 very expensive if the models are stiff or rare system events are of interest. 6 Proxels are a state space-based simulation technique that does not have these 7 drawbacks. They implicitly use a discrete-time Markov chain to determin8 istically ...

Stochastic chemical kinetics describes the time evolution of a well-stirred chemically reacting system in a way that takes into account the fact that molecules come in whole numbers and exhibit some degree of randomness in their dynamical behavior. Researchers are increasingly using this approach to chemical kinetics in the analysis of cellular systems in biology, where the small molecular popu...