This paper describes the application of discrete event simulation to study continuous material flow. Logistics is an integrated part of most manufacturing companies. The purpose of this study is to determine the required logistics operations to allow continuous operations of a chemical manufacturing plant. The application has been used to provide critical decision support.

UNLABELLED Möbius has found numerous applications in computational biology to build and solve stochastic models of biological processes. It provides the user with a modeling workflow and several sophisticated features that are not available in the simulation tools commonly used by computational biologists. AVAILABILITY Möbius is free for academic users. It can be downloaded from www.mobius.ui...

The simulation study of networks remains attractive due to desire to achieve better important traits such as scalability and performance. This paper describes a biologically inspired discrete-event modelling approach for simulating networks. It introduces a synergistic modelling approach by incorporating key attributes of honeybees and their societal properties into a set of simulation models d...

Models of industrial processes often contain discrete phenomena superimposed on the continuous system behavior. Simulation of batch processes, start-up and shutdown procedures, fault diagnosis and alarms fall under this category. Models for such processes require a mathematical theory for both its continuous and discrete state transitions. A key problem in hybrid simulation lies in the detectio...

Computer simulation is widely used to support the design of any kind of complex system and to create computer-generated "virtual worlds" where humans and/or physical devices are embedded (e.g. aircraft flight simulators [20]). However, both the generation of simulation models and the execution of simulations can be time and cost expensive. While there are already several ways to increase the sp...

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...

Discrete event modeling and simulation reinforcement learning are two frameworks suited for cyberphysical system design, which, when combined, can give powerful tools optimization or decision making process example. This paper describes how discrete could be integrated into concepts in order to assist the realization of systems, more specially considering temporal, hierarchical, multi-agent asp...

To perform dextrous manipulation e ciently, it is necessary to coordinate the interactions of many component processes. This paper investigates one approach to coordination: discrete-event systems. The applicability of discrete-event systems to the modeling of dextrous manipulation tasks is studied. Discrete-event control theory o ers formal methods for determining whether a coordinator of the ...

Natural systems can offer important concepts for modeling network systems. A biologically-inspired discrete-event modeling approach is described for studying networks’ scalability and performance traits. Key adaptive and emergent attributes of honeybees and their societal properties are incorporated into a set of simulation models that are developed using the Discrete Event System Specification...