Hierarchical simulation of complex production systems by coupling of models

Zülch, Gert; Jonsson, Uwe; Fischer, Julian

doi:10.1016/s0925-5273(01)00198-0

Cited by 34 publications

(14 citation statements)

References 2 publications

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“…The execution of a large model might be split across many computers 67,68,69 or separate models on several computers could be linked and run concurrently 70,71 . Simulation models might be linked to remote third party software during execution, such as databases, artificial intelligence software or real-time systems 72,73 .…”

Section: Simulation On the World Wide Web And Distributed Simulationmentioning

confidence: 99%

Discrete-event simulation: from the pioneers to the present, what next?

Robinson

2005

Journal of the Operational Research Society

176

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Discrete-event simulation is one of the most popular modelling techniques. It has developed significantly since the inception of computer simulation in the 1950s, most of this in line with developments in computing. The progress of simulation from its early days is charted with a particular focus on recent history. Specific developments in the past 15 years include visual interactive modelling, simulation optimisation, virtual reality, integration with other software, simulation in the service sector, distributed simulation and the use of the world wide web.The future is then speculated upon. Potential changes in model development, model use, the domain of application for simulation and integration with other simulation approaches are all discussed. The desirability of continuing to follow developments in computing, without significant developments in the wider methodology of simulation, is questioned.

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Section: Simulation On the World Wide Web And Distributed Simulationmentioning

confidence: 99%

Discrete-event simulation: from the pioneers to the present, what next?

Robinson

2005

Journal of the Operational Research Society

176

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“…al., 2001) and parallel and distributed simulation paradigm, i.e., implementing more models, executed over more calculation processes using computers and/or multi-processors, see for example, (Gan & McGinnis, 2001), (Brun et. al., 2002) and (Zulch et. al., 2002).…”

mentioning

confidence: 94%

Modeling of Supply Chain Contextual-Load Model for Instability Analysis

Saad¹,

Kadirkamanathan²,

Bennett³

2008

Supply Chain

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“…There are always detailed data available for the manufacturing functions. But for the aggregate management levels data is not usually available and in most cases only rough estimates exist (Zulch, Jonsson, and Fischer 2002;Mandal and Sohal, 1998;Anthony, Dearden, and Bedford 1989).…”

Section: Introductionmentioning

confidence: 99%

Enterprise scheduling: Hybrid and hierarchical issues

Pastrana¹,

Helal²,

Mendizabal³

2010

Proceedings of the 2010 Winter Simulation Conference

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We build a hybrid discrete-continuous simulation model of the manufacturing enterprise system. This model consists of an overall system dynamics model of the manufacturing enterprise and connected to it are a number of discrete event simulations for selected operational and tactical functions. System dynamics modeling best fits the macroscopic nature of activities at the higher management levels while the discrete models best fit the microscopic nature of the operational and tactical levels. An advanced mechanism based on information theory is used for the integration of the different simulation modeling modalities. In addition, the impact of the decisions at the factory level in scheduling are analyzed at the management level. The different models of control are discussed. INTRODUCTIONThe advances in information and computing technologies in addition to the unprecedented levels of competition and fast pace of changes in the business environment have created a more flattened enterprise system and changed the way enterprises should be managed. This is creating challenges to using simulation tools. The presence of manufacturing and nonmanufacturing functions in the manufacturing enterprise, the different types of behavior in the system, differences between management levels in the scope of planning, frequency of decision making, and levels of details they deal with, indicate that a single simulation approach cannot offer all that is needed in a simulation of such a complex system. The traditional use of discrete event simulation (DES) to simulate the manufacturing systems has narrowed the scope to detailed statistical analyses at the operational levels of the system. At that level, the main concerns have been the flow of materials and the levels of inventories and work in process, and the performance at the individual level of resources, units of products or processes (Smith 2003; Lee et al. The need to simulate the whole system (aggregate and detailed management level functions) has challenged DES, which was inadequate to approximate the continuous behavior in the system generally and particularly at the aggregate levels, or communicate appropriately the financial computations to higher management (Lee et al. 2002;Johnson and Eberlein 2002;Barton, Love, and Taylor 2001). DES works effectively with problems of narrow scopes, but LW LV ³« incompatible with a globDO SRLQW RI YLHZ´ /LQ HW al. 1998). And it does not address the stability of the system (Rabelo et al. 2005) that should be consi-3350 978-1-4244-9865-9/10/$26.00 ©2010 IEEE

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Hierarchical simulation of complex production systems by coupling of models

Cited by 34 publications

References 2 publications

Discrete-event simulation: from the pioneers to the present, what next?

Discrete-event simulation: from the pioneers to the present, what next?

Modeling of Supply Chain Contextual-Load Model for Instability Analysis

Enterprise scheduling: Hybrid and hierarchical issues

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