JDESK WEB-BASED DISCRETE EVENT SIMULATION KERNEL1

Mathematics and Computers in Simulation, 1979

International Journal of Simulation and Process Modelling, 2008

This paper describes the design and functionality of an open-source objectoriented library for executing discrete-event simulation models in the Java programming language. The structure of the library is described in terms of its packages, class structure, and functionalities. The purpose of this paper is to provide an understanding of the library so that practitioners and researchers can better utilize the library for simulation modelling. In addition, the expository nature of this paper can help new users or students learn the basics of discrete-event simulation from the library's design. The capabilities of the library are illustrated through a number of code examples.

The Journal of the Operational Research Society, 2000

The rapid development of the Internet and the introduction of Java as a programming language provide new possibilities for discrete event simulations. This paper surveys some of these developments and discusses the aspects of Java that make it attractive for developing such simulations. It then describes the development of two simulation systems in Java, one a straightforward application of the three-phase approach and the other a system that supports distributed client-server applications. The limitations and advantages of using Java in this way are discussed.

2005

In this paper, we present, verify and analyze the performance of JSDESLib (Java Simple Discrete-Event Simulation Library). It is a simplified and easy-to-use Java-based library for the development of discreteevent simulation tools of parallel systems. Our main objectives are: to propose, develop and implement JSDESLib: as a parallel event-oriented simulation library that simplifies the development of simulation tools; with a next earliest event mechanism and communication management between entities. Other main objectives are: to verify and analyze the performance of JSDESLib. A secondary objective is to present some simulation tools that were developed with that library. The main contributions of this work are: the proposal, implementation and performance analysis of JSDESLib; simplicity and ease usage of a parallel-optimized implementation of that library.

Spring Simulation Multiconference 2010, SpringSim'10, 2010

Creating models and analyzing simulation results can be a difficult and time-consuming task, especially for non-experienced users. Although several DEVS simulators have been developed, the software that aids in the modeling and simulation cycle still requires advanced development skills, and they are implemented using non-standard interfaces, which makes them difficult to extend. The architecture and design of CD++Builder we present here can simplify the construction and simulation of DEVS models, facilitate model reuse and promote good modeling practices by allowing enhanced graphical editing and integration of tools into a single environment. The Eclipse-based environment includes new graphical editors for DEVS coupled models, DEVS-Graphs and C++ atomic models (including code templates that are synchronized with the graphical versions). Integration with Eclipse allows extensibility while simplifying software development, installation and updates.

1998

We discuss the use of the stochastic process algebra (spades) for discrete event simulation, and report on a prototype simulation algorithm that has been implemented. The use of process algebraic techniques in offers a number of advantages with respect to existing simulation techniques. The compositional nature of allows an on-the-fly construction of the simulation model, and hence only the current state need to be saved. , moreover, allows us to model and simulate on a general purpose basis, as the simulation algorithm applies to any well founded process specified in. Finally, specifications in are also amenable to traditional process algebraic analysis of their functional behaviour, like e.g. absence of deadlocks. We illustrate our result with a simulation example of a nontrivial system. ⋆ Supported by the NWO/SION project 612-33-006. analytical study by restricting to the so-called Markovian-process algebras [14, 9, 2,.. . ], the other addresses a more general case by accepting simulation as part of the analysis process [11, 16, 6, 7]. Recently, in [6], we introduced a simple extension of automata with stochastic information by borrowing ideas from timed automata [1] and generalised semi-Markov processes [8]. In addition, we introduced a stochastic process algebra for discrete event simulation, named spades and denoted by. In this paper we discuss the formal foundations of a simulation algorithm for stochastic automata. Since the semantics of is given in terms of stochastic automata, as an immediate consequence we can simulate any system described in. In fact, there is a real advantage in implementing the algorithm directly for. The simulation algorithm can generate the stochastic automaton on-thefly from a given term, that is, only those parts of the stochastic automaton that are actually going to be used are generated, and moreover, in the moment they are needed and not in advance. So, since systems described in can be specified compositionally using recursive specifications, simulation of infinitestate processes is, in principle, possible. Using as simulation modelling language has two additional advantages. On the one hand, allows, in principle, the description of any system which makes the simulation and modelling task general-purpose oriented. By "generalpurpose" we mean that, in contrast to what happens with many simulation packages, does not restrict to a particular methodology such as queuing networks. On the other hand, is a stochastic process algebra and hence provides a formal verification framework which allows for the formal analysis of the functional correctness of the model to be studied. To consolidate our results we give a (non-trivial) example of application of the simulation algorithm. We discuss the modeling of a multiprocessor mainframe with failures [13] and analyse its performance using the implementation of the simulation algorithm. The paper is organised as follows. In Sections 2 and 3 we recall the stochastic automaton model, its semantics, and the process algebra. Section 4 introduces the simulation algorithm: its foundations and a description of its (current) implementation. The multiprocessor mainframe example is studied in Section 5. In Section 6 we discuss related work and conclude the paper. Acknowledgements. The alternative semantics for stochastic automata (see Section 2) resulted from a question posed by Mariëlle Stoelinga. We thank Victor Nicola for always answering our questions and the referees for their constructive remarks. 2 The Stochastic Automaton Model In this section we discuss the semantic basis for our simulation algorithm. We recall the definition of stochastic automata and we give its semantics in terms of probabilistic transition systems.

Enabling Technologies for Simulation Science VI, 2002

SimPackJ/S is the JavaScript and Java version of SimPack, which means SimPackJ/S is a collection of JavaScript and Java libraries and executable programs for computer simulations. The main purpose of creating SimPackJ/S is that we allow existing SimPack users to expand simulation areas and provide future users with a freeware simulation toolkit to simulate and model a system in web environments. One of the goals for this paper is to introduce SimPackJ/S. The other goal is to propose translation rules for converting C to JavaScript and Java. Most parts demonstrate the translation rules with examples. In addition, we discuss a 3D dynamic system model and overview an approach to 3D dynamic systems using SimPackJ/S. We explain an interface between SimPackJ/S and the 3D language--Virtual Reality Modeling Language (VRML). This paper documents how to translate C to JavaScript and Java and how to utilize SimPackJ/S within a 3D web environment.

2010

This paper provides simulation practitioners and consumers with a grounding in how discrete-event simulation software works. Topics include discrete-event systems; entities, resources, control elements and operations; simulation runs; entity states; entity lists; and entity-list management. The implementation of these generic ideas in AutoMod, SLX, and Extend is described. The paper concludes with several examples of "why it matters" for modelers to know how their simulation software works, including coverage of SIMAN (Arena), ProModel, and GPSS/H as well as the other three tools. 2 ABOUT DISCRETE-EVENT SIMULATION 2.1 The Transaction-Flow World View The "transaction-flow world view" often provides the basis for discrete-event simulation. In this world view, a system is visualized as consisting of discrete units of traffic that move ("flow") from point to point in the system while competing with each other for the use of scarce resources. The units of traffic are sometimes called "transactions," giving rise to the phrase "transaction flow." Numerous systems fit the preceding description. Included are many manufacturing, material handling, transportation, health care, civil, natural resource, communication, defense, and information processing systems, and queuing systems in general.

2012

Discrete-event simulation represents modeling, simulating, and analyzing systems utilizing the computational and mathematical techniques, while creating a model construct of a conceptual framework that describes a system. The system is father simulates by performing experiment(s) using computer implementation of the model and analyzed to draw conclusions from output that assist in decision making process. Discrete event simulation technologies have been extensively used by industry and academia to deal with various industrial problems. By late 1990s, the discrete event simulation was in doldrums as global manufacturing industries went through radical changes. The simulation software industry also went through consolidation. The changes have created new problems, challenges and opportunities to the discrete event simulation. This chapter reviews the discrete event simulation technologies; discusses challenges and opportunities presented by both global manufacturing and the knowledge ...

IEEE Transactions on Software Engineering, 2000

This paper develops a message-based approach to discrete-event simulation. Although niessage-based simulators have the same expressive power as traditional discrete-event simulation languages, they provide a more natural environment for simulating distributed systems. In message-based simulations, a physical system is modeled by a set of message-communicating processes. The events in the system are modeled by message-communications. The paper proposes the entity construct to represent a message-communicating process operating in simulated time. A general wait until construct is used for process scheduling and message-communication. Based on these two notions, the paper proposes a language fragment comprising a small set of primitives. The language fragment can be implemented in any general-purpose, sequential programming language to construct a message-based simulator. We give an example of a message-based simulation language, called MAY, developed by implementing the language fragment in Fortran. MAY is in the public domain and is available on request.