Interval discrete-time Markov chains simulation

In this paper we propose one IDTMC (Interval Discrete-Time Markov Chain) algorithm to improve simulation when significant variabilities exist. The IDTMC models takes into account the effects of variabilities in transition probabilities represented by intervals. IDTMC simulation incorporates variabilities and uncertainties based on imprecise probabilities, where the statistical distribution parameters in the simulation are intervals instead of precise real numbers. Interval arithmetic is used to simulate a set of scenarios simultaneously in each simulation run. A case study is presented for processing uncertainty from an ISPN (Interval Stochastic Petri Net) model. This simulation procedure can be applied to support robust decision making.