• Korean Journal of Computational Design and Engineering
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• v.17 no.2
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• pp.111-122
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• 2012

Capacity planning plays an important role not only for master production plan but also for facility or layout design in shipbuilding. Product work breakdown structure, attributes of production resources, and production method or process data are associated in order to make the discrete event simulation model of shipyard layout plan. The production amount of each process and the process time is assumed to be stochastic. Based on the stochastic discrete event simulation model, the production capacity of each facility in shipyard is estimated. The stochastic model of product arrival time, process time and transferring time is introduced for each process. Also, the production capacity is estimated for the assumed master production schedule.

• Journal of the Korea Society for Simulation
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• v.11 no.2
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• pp.77-88
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• 2002

Petri nets is a widely used formalism for specification and analysis of concurrent systems which is a subclass of discrete event systems. The DEVS (Discrete Event System Specification) formalism provides a general framework for specification of discrete event systems in a hierarchical, modular form. Often, modeling a discrete event system may employ both Petri Nets and DEVS formalism. In such a case low-level operational logics are modeled by Petri Nets and high-level managements by the DEVS formalism. Analysis of the system requires simulation of the overall system. This paper presents an algorithm for transformation of Petri Nets to DEVS formalism. The transformation enables modelers to simulate an overall system, which consists of DEVS models and Petri Nets models, in a unified DEVS simulation environment such as DEVSim++. An example for such transformation will be given.

• Proceedings of the Korea Society for Simulation Conference
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• 1992.10a
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• pp.5-5
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• 1992

The DEVS(discrete event system specification) formalism describes a discrete event system in a hierarchical, modular form. DEVSIM++ is C++ based general purpose DEVS abstract simulator which can simulate systems to be modeled by the DEVS formalism in a sequential environment. We implement P-DEVSIM++ which is a parallel version of DEVSIM++. In P-DEVSIM++, the external and internal event of models can be processed in parallel. To process in parallel, we introduce a hierarchical distributed simulation technique and some optimistic distributed simulation techniques. But in our algorithm, the rollback of a model is localized itself in contrast to the Time Warp approach. To evaluate its performance, we simulate a single bus multiprocessor architecture system with an external common memory. Simulation result shows that significant speedup is made possible with our algorithm in a parallel environment.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.405-424
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• 2001

Many simulation models have been built to facilitate simulation technique in designing, evaluating, and optimizing supply chains. Simulation is preferred to deal with stochastic natures existing in the supply chain. Moreover simulation has a capability to find local optimum value within each component through entire supply chain. Most of supply chain simulation models have been developed on the basis of discrete-event simulation. Since supply chain systems are neither completely discrete nor continuous, the need of constructing a model with aspects of both discrete-event and continuous simulation is provoked, resulting in a combined discrete-continuous simulation. In this paper, an architecture of combined modeling for supply chain simulation is proposed, which includes the equation of continuous portion in supply chain and how these equations are used in the supply chain simulation models. A simple example of supply chain model dealing with the strategic level of supply chain presented in this paper shows the possibility and the prospect of this approach.

• Journal of the Korea Society for Simulation
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• v.10 no.2
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• pp.75-89
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• 2001

Many simulation models have been built to facilitate simulation technique in designing, evaluating, and optimizing supply chains. Simulation is preferred to deal with stochastic natures existing in the supply chain. Moreover simulation has a capability to find local optimum value within each component through entire supply chain. Most of supply chain simulation models have been developed on the basis of discrete-event simulation. Since supply chain systems are neither completely discrete nor continuous, the need of constructing a model with aspects of both discrete-event and continuous simulation is provoked, resulting in a combined discrete-continuous simulation. In this paper, an architecture of combined modeling for supply chain simulation is proposed, which includes the equation of continuous portion in supply chain and how these equations are used in the supply chain simulation models. A simple example of supply chain model dealing with the strategic level of supply chain presented in this paper shows the possibility and the prospect of this approach.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.145-149
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• 1997

This paper describes a methodology for the development of models of discrete event system. The methodology is based on transformation of continuous state space into discrete one to homomorphically represent dynamics of continuous processes in discrete events. This paper proposes a formal structure which can coupled discrete event system models within a framework. The structure employs the discrete event specification formalism for the discrete event system models. The proposed formal structure has been applied to develop a discrete event specification model for the complex spectral density analysis of strip for urin analyzer system. For this, spectral density data of strip is partitioned into a set of Phases based on events identified through urine spectrophotometry. For each phase, a continuous system of the continuous model for the urine spectral density analysis has been simulated by programmed C++. To validate this model, first develop the discrets event specification model, then simulate the model in the DEVSIM++ environment. It has the similar simulation results for the data obtained from the continuous system simulation. The comparison shows that the discrete event specification model represents dynamics of the urine spectral density at each phase.

• Journal of the Society of Naval Architects of Korea
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• v.45 no.6
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• pp.656-668
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• 2008

When it comes to design and acquire underwater vehicles such as a submarine and a torpedo according to the process of SBA(Simulation Based Acquisition)/SBD(Simulation Based Design), it is necessary to predict the performance of interest precisely and to perform the test over and over again using the M&S(Modeling and Simulation) of the engineering and the engagement level. In this paper, we research the DEVS(Discrete Event System Specification) and DTSS(Discrete Time System Specification) formalism based standard model architecture for the underwater vehicle which can support both the heterogeneous level of the M&S(Engineering/Engagement) and the different system of the M&S(Discrete Event System and Discrete Time System). To validate this standard modeling architecture, we apply it to the submarine normal diving simulation.

• Journal of the Korea Society for Simulation
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• v.21 no.2
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• pp.31-39
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• 2012

Simulation Framework is a basic software tool used to develop simulation applications. This paper describes the development of a discrete event simulation framework based on DEVS(Discrete EVent System Specification) formalism, using MATLAB language which is widely used in technical computing and engineering disciplines. The newly developed framework utilizing MATLAB object oriented programming combines the convenience of MATLAB language and the sophisticated architecture of the DEVS formalism. Hence, it supports the productivity, flexibility, extensibility that are required for the simulation application software development of the weapon systems engagement. Moreover, it promises a simulation application the increased the computation speed proportional to the number of CPU of a multi-core processor, providing the batch simulation functionality based on MATLAB parallel computing technology.

• Journal of the Korea Society for Simulation
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• v.10 no.1
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• pp.1-12
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• 2001

This article describes the application of discrete event simulation in a process industry (coffee manufacturing) as a daily production-scheduling tool. A large number of end products (around 300), sporadic demand, and limited shelf life of coffee (90 days) make it difficult to generate feasible production schedules manually. To solve this problem, an integrated system was developed incorporating discrete event simulation methodology into scheduling process. The integrated system is comprised of two components: a scheduling program and a simulation model. The scheduling program is used to generate daily schedules for roasting, grinding, and packing coffee. The simulation model uses the generated schedules to simulate the production of coffee and regenerates a modified production schedule. In this paper, each of the components will be described in detail, evaluated in terms of performance factors, and validated with a set of real production data. Although this article focuses on a specific system, we will share our experiences and Intuitions gained and encourage other process industries to develop simulation-based scheduling tools.

• Journal of the Korea Society for Simulation
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• v.13 no.4
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• pp.1-16
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• 2004

The modelling and simulation of the activation process for the heart system is meaningful to understand special excitatory and conductive system in the heart and to study cardiac functions because the heart activation conducts through this system. This thesis proposes two dimensional cellular automaton(CA) model for the activation process of the myocardium and conducted simulation by means of discrete time and discrete event algorithm. In the model, cells are classified into anatomically similar characteristic parts of the heart and each of cells has a set of cells with preassigned properties. Each cell in this model has state variables to represent the state of the cell and has some state transition rules to change values of state variables executed by state transition function. The state transition rule is simple as follows. First, the myocardium cell at rest stay in passive state. Second, if any one of neighborhood cell in the myocardium cell is active state then the state is change from passive to active state. Third, if cell's state is an active then automatically go to the refractory state after activation phase. Four, if cell's state is refractory then automatically go to the passive state after refractory phase. These state transition is processed repeatedly in all cells through the termination of simulation.