Optimal Run Length for Discrete-event Distributed Cluster-based Simulations

Optimal Run Length for Discrete-event Distributed Cluster-based Simulations

In scientific simulations the results generated usually come from a stochastic process. New solutions with the aim of improving these simulations have been proposed, but the problem is how to compare these solutions since the results are not deterministic. Consequently how to guarantee that the output results are statistically trusted. In this work we apply a statistical approach in order to de...

Partial Observation in Distributed Supervisory Control of Discrete-Event Systems

Distributed supervisory control is a method to synthesize local controllers in discrete-eventsystems with a systematic observation of the plant. Some works were reported on extending this methodby which local controllers are constructed so that observation properties are preserved from monolithic todistributed supervisory control, in an up-down approach. In this paper, we find circumstances in ...

On the distributed orchestration of stochastic discrete event simulations

Discrete event simulations are a powerful technique for modeling stochastic systems with multiple components where interactions between these components are governed by the probability distribution functions associated with them. Complex discrete event simulations are often computationally intensive with long completion times. This paper describes our solution to the problem of orchestrating th...

An Information Taxonomy for Discrete Event Simulations

Input Modeling Techniques for Discrete-event Simulations

Most discrete-event simulation models have stochastic elements that mimic the probabilistic nature of the system under consideration. A close match between the input model and the true underlying probabilistic mechanism associated with the system is required for successful input modeling. The general question considered here is how to model an element (e.g., arrival process, service times) in a...