• 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.

• 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.

• Proceedings of the Korea Society for Simulation Conference
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• 1992.10a
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• pp.7-7
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• 1992

Extending discrete event modelling formalisms to facilitate greater symbol manipulation capabilities is important to further their use in intelligent control and design of high autonomy systems. This paper defines an extension to the DEVS formalism that facilitates symbolic expression of discrete event times by extending the time base from the real numbers to the field of linear polynomials over the reals. A simulation algorithm is developed to generate the branching trajectories resulting from the underlying non-determinism. To efficiently manage linear polynomial constraints based on feasibility checking algorithm borrowed from linear programming. The extended formalism offers a convenient means to conduct multiple, simultaneous explorations of model behaviors. Examples of application are given with consideration on fault model analysis.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.348-352
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• 1993

A hybrid system contains both continuous variables and discrete event components. This paper presents the new control architecture for hybrid systems, which consists of a conventional controller for the continuous-time variable of the system, a supervisor for discrete event components of the system, and an interface for link between the controller and the plant. The presented controller is suitable for the system operating at the different operating conditions or for system being changing the plant model by enabling and disabling discrete events. This paper shows that the presented controller is better than the conventional controller.

• International Journal of Control, Automation, and Systems
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• v.5 no.4
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• pp.463-470
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• 2007

Colored Petri net model which is a model of discrete event systems is considered in this work. A supervisory controller which enforces reversibility and liveness simultaneously is presented. Furthermore, the algorithms, written by pseudo-code, are presented for the supervisory controller design. A program is developed to implement these algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.816-819
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• 1996

This paper focuses on development of simulation model for SAE J1850 and performance evaluation of a J1850 network. The simulation model has been developed by using discrete event simulation language. SAE J1850 is one of Class B network protocol for general data sharing applications. Through numerous simulation experiments, several important performance factors such as the probability of a successful transmission, average queue delay, and throughput have been evaluated.

• International Journal of Naval Architecture and Ocean Engineering
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• v.4 no.3
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• pp.211-227
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• 2012

To implement a combined discrete event and discrete time simulation such as submarine diving simulation in a distributed environment, e.g., in the High Level Architecture (HLA)/Run-Time Infrastructure (RTI), a HLA interface, which can easily connect combined models with the HLA/RTI, was developed in this study. To verify the function and performance of the HLA interface, it was applied to the submarine dive scenario in a distributed environment, and the distributed simulation shows the same results as the stand-alone simulation. Finally, by adding a visualization model to the simulation and by editing this model, we can confirm that the HLA interface can provide user-friendly functions such as adding new model and editing a model.

• 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.

• Korean Journal of Computational Design and Engineering
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• v.17 no.1
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• pp.45-53
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• 2012

In this paper, oil spreading simulation model is proposed for analyzing the oil spreading phenomenon rapidly when the ocean is polluted by the oil from a stranded ship. The space occupied by the ocean is converted into the latticed cell, and the each cell contains the information, such as the quantity of the oil, the temperature of the ocean, and the direction of current and wind. Two states, such as "clean" and "polluted" are defined in the each cell, and the oil in the cell spreads to the neighbor cells by the spreading rules. There are three spreading rules. First, the oil in the certain cell only spreads to the neighbor cells that contain larger oil than the certain cell. Second, the oil evaporates in proportion to the temperature of the ocean at the every time step. Third, the oil spreading property is affected by the direction and the speed of the current and the wind. The oil spreading simulation model of the each cell is defined by using the combined discrete event and discrete time simulation model architecture with the information and the spreading rules in the cell. The oil spreading simulation is performed when the oil of 10,000 kL is polluted in the ocean environment of 300 m by 300 m with various current and wind.