Ecosystem modeling is traditional done using flexible, agent based models, or by using ordinary differential equations to estimate behavior over large populations. Both of these approaches have positive and negative aspects. The DEVS modeling system is a discrete event modeler that may be able to combine the positive aspects of discrete event simulation with the ability to run giant simulations...

| Modal logic is introduced into the modeling of discrete-event systems. Analysis within this framework includes formal reasoning about what supervisors know or do not know about a given system. This model can be used to develop control strategies that solve decentralized discrete-event control problems. When a problem cannot be solved using fully decentralized supervisors, reasoning about know...

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...

The system presented bridges the gap between three different research areas: discrete event simulation, heuristic optimization methods and distributed systems technology. Its goal is to provide a framework which supports an efficient implementation of simulation optimization projects, including heuristic optimum seeking procedures and parallel execution of experiments. It is written completely ...

Multi-fidelity simulation is an effective approach to balancing speed and accuracy in expensive simulation, its performance affected by the quality of multi-fidelity models. Building high-quality models non-trivial, especially for complex systems, because current manual modeling methods require sufficient domain knowledge experience, increasing labor time costs. Motivated issues, this paper foc...