Research issues in symbiotic simulation
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Symbiotic simulation is a paradigm in which a simulation system and a physical system are closely associated with each other. This close relationship can be mutually beneficial. The simulation system benefits from real-time measurements about the physical system which are provided by corresponding sensors. The physical system, on the other side, may benefit from the effects of decisions made by the simulation system. An important concept in symbiotic simulation is that of the what-if analysis process which is concerned with the evaluation of a number of what-if scenarios by means of simulation. Symbiotic simulation and related paradigms have become popular in recent years because of their ability to dynamically incorporate real-time sensor data. In this paper, we explain different types of symbiotic simulation and give an overview of the state of the art. In addition, we discuss common research issues that have to be addressed when working with symbiotic simulation. While some issues have been adequately addressed, there are still research issues that remain open.Keywords:
Simulation Modeling
Physical system
Systems simulation
The article presents new possibilities of simulation software and its application to improve the pro-duction structure. In many enterprises, the basic issues are related to the determination of planned tasks for individual positions, calculating the demand for employees, taking into account their skills and qualifications, calculating work costs, determining work efficiency and its dynamics. Therefore proper work organization consists in setting the course of work in such a way as to obtain maximum results with the least amount of work by man and machine. The article presents the problem of per-sonnel allocation to the production line. The basic stages of developing a simulation model of this process are discussed, including all necessary information and inputs. The results shows impact of the selected simulation scenarios to the workload level of the staff and the duration of the production process. In this concept, to solve the problem a simulation model of the production process was built. A new generation of 3D FlexSim simulation environment with an integrated OptQuest optimi-zation module was used.
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Simulation Modeling
Discrete-Event Simulation
Production manager
Line (geometry)
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Discrete-Event Simulation
Simulation language
Simulation Modeling
Systems simulation
Abstraction
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Systems simulation
Simulation Modeling
Abstraction
SIGNAL (programming language)
Complex system
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Discrete-event simulation is a powerful tool for solving many problems, especially in manufacturing. Formulating the problem, building the simulation model, running the model, and analyzing the output are the basic steps in a simulation study. Because building a simulation model can be a difficult and time-consuming task, it will be useful if a decision-maker could reuse a simulation model if possible and change it to solve a different problem or evaluate another option. Thus, it is desirable to have adaptable simulation models that are easy to change with little or no programming effort. Using simulation models in real-time scheduling and operational settings also requires adaptable simulation models that can represent the changing shop floor. Also, as a manufacturing system progresses from a concept to a detailed design, and to an installed and operating facility, the simulation model of the system must change. Adaptable simulation models will reduce the time, effort, and cost of using simulation in these types of scenarios. This paper reviews the concepts of adaptability, suggests some measures for adaptable simulation models, and discusses factors that affect adaptability.
Adaptability
Simulation Modeling
Discrete-Event Simulation
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Systems and models simulation common processes in discrete systems the EZSIM environment analysis of simulation inputs and creation of their effects analysis of simulation output applications of simulation simulation tools and the criteria for their selection future directions for simulation.
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Discrete-Event Simulation
Systems simulation
Dynamic simulation
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Systems simulation modeling techniques offer a method of representing the individual elements of a manufacturing system and their interactions. By developing and experimenting with simulation models, one can obtain a better understanding of the overall physical system. Forest products industries are beginning to understand the importance of simulation modeling to help improve the dynamic performance of their processing and manufacturing systems. However, much knowledge and expertise are needed to accurately represent an actual forest products processing system as a simulation model. The purpose of this paper is to describe some effective process simulation model development strategies. This description points to the depth and breadth of knowledge that are needed to create usable and valid simulation models. To assist in illustrating the simulation modeling life cycle, actual case studies in modeling furniture rough mills are used.
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Decision making studies in agriculture are often made difficult by the complex and dynamic nature of bio‐economic systems. Simulation is one of the newer systems research techniques which as yet has had limited use in farm management research. This paper discusses some methodological aspects of simulation with specific reference to grazing systems. Problems arising in the development and use of simulation models are discussed and the need for inter‐disciplinary co‐operation to overcome data problems is indicated. One approach to experimentation is illustrated by reference to a model of a sheep grazing system and the problem of cropping for winter grazing. It is concluded that simulation is a potentially useful technique for management‐oriented systems research in agriculture.
Simulation Modeling
Agricultural management
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Research background: In today’s global world and many major market players, companies are forced to streamline and optimize their processes. They can use many methods for this purpose. One of the modern and innovative methods is process simulation. Simulation is experimenting with a computer model of a real production system in order to optimize the production process. Purpose of the article: The purpose of this article is to point out how it is possible to optimize processes in a manufacturing plant using a simulation tool. Methods: The main methods used were mainly method of analysis and simulation software Tecnomatix Plant Simulation, version 15.0.5 from Siemens company. The authors used the method of analysis both in the processing of the theoretical basis of the problem, as well as in the analysis of processes in the manufacturing company. This analysis provided important information inputs for creating a simulation model that reflects the current state of material flows in the company. Findings & Value added: The results were obtained based on performed experiments with the created initial model of the current state of material flows. These experiments were performed using a tool of genetic algorithms that are part of the simulation software. The parameter that was assessed in the individual experiments was the production time of the entire production plan. Based on the process simulation, it was possible to reduce this time, which increased the production efficiency and throughput of material flows in the production plant.
Simulation software
Simulation Modeling
Process Simulation
Discrete-Event Simulation
Material Flow
Simulation-based optimization
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Symbiotic simulation is a paradigm in which a simulation system and a physical system are closely associated with each other. This close relationship can be mutually beneficial. The simulation system benefits from real-time measurements about the physical system which are provided by corresponding sensors. The physical system, on the other side, may benefit from the effects of decisions made by the simulation system. An important concept in symbiotic simulation is that of the what-if analysis process which is concerned with the evaluation of a number of what-if scenarios by means of simulation. Symbiotic simulation and related paradigms have become popular in recent years because of their ability to dynamically incorporate real-time sensor data. In this paper, we explain different types of symbiotic simulation and give an overview of the state of the art. In addition, we discuss common research issues that have to be addressed when working with symbiotic simulation. While some issues have been adequately addressed, there are still research issues that remain open.
Simulation Modeling
Physical system
Systems simulation
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THIS PART OF THE BOOK DEALS WITH THE FOLLOWING: MATHEMATICAL SIMULATION OF VARIABLE CONDITIONS IN GROUND WATER, CUENCA,J; MATHEMATICAL MODELS FOR SIMULATING EARTHQUAKES, SAMARTIN,A; GENERAL PRINCIPLE OF THE SIMULATION OF OPERATIONAL SYSTEMS, PROGRAMMING LANGUAGES, CUENCA,J; SIMULATION OF THE OPERATION OF HYDRAULIC RESOURCES SYSTEM, CUENCA,J; TEST SIMULATION. ESTIMATION OF STATISTICAL PARAMETERS, MARTIN,V; USE OF SIMULATION FOR THE CONTROL OF PROJECTS, CERCOS,R; SIMULATION OF URBAN ROAD TRAFFIC, SANCHEZ,V; APPLICATION OF SIMULATION TO PORT TRAFFIC. SIMULATION OF HARBOUR OPERATIONS, NAVACERRADA,J; APPLICATION OF SIMULATION TO HARBOUR TRAFFIC. SIMULATION OF THE OPERATION OF A CRUDE OIL HARBOUR, FERRER,I. SIMULATION MODEL FOR CALCULATING GROUND PERSONNEL IN AIRPORTS, GASCO,JL. SEE ALSO IRRD ABSTRACTS NOS 103509 AND 103511.
Simulation Modeling
Traffic simulation
Port (circuit theory)
Stochastic simulation
Systems simulation
Network traffic simulation
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