A Methodological Framework for Business-Oriented Modeling of IT Infrastructure Ariel Landau, Segev Wasserkrug, Dagan Gilat, Natalia Razinkov, Aviad Sela, and Sarel Aiber. 2004 Winter Simulation Conference (WSC’04), December 5-8, Washington, D.C., USA

The creation of IT simulation models for uses such as capacity planning and optimization is becoming more and more widespread. Traditionally, the creation of such models required deep modeling and/or programming expertise, thus severely limiting their extensive use. Moreover, many modern intelligent tools now require simulation models in order to carry out their function. For these tools to be widely deployable, the derivation of simulation models must be made possible without requiring excessive technical knowledge.
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Multimethod Simulation Modeing using AnyLogic Andrei Borshchev, INFORMS Roundtable Meeting Fall 2007, Seattle, USA

The presentation shows you how to develop simulation models, choose abstraction level and methodology.
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Decreased length of stay after addition of healthcare provider in emergency department triage Theodore Eugene Day, Abdul Rahim Al-Roubaie, Eric Jonathan Goldlust; EMJ Online First, August 22, 2012

The objective of this paper was to determine the effects of adding a healthcare provider in triage on average length of stay (LOS) and proportion of patients with >6 h LOS. The other goal was to assess the accuracy of computer simulation in predicting the magnitude of such effects on these metrics.
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A multi-paradigm, whole system view of health and social care for age-related macular degeneration Joe Viana, Stuart Rossiter, Andrew A. Channon, Sally C. Brailsford, Andrew Lotery, WSC-2012, Berlin

This paper presents a hybrid simulation model for the management of an eye condition called age-related macular degeneration, which particularly affects the elderly. The model represents not only the detailed clinical progression of disease in an individual, but also the organization of the hospital clinic in which patients with this condition are treated and the wider environment in which these patients live (and their social care needs, if any, are met). The model permits a ‘whole system’ societal view which captures the interactions between the health and social care systems
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Supply chain multi-structural (re)-design Ivanov D.A., International Journal of Integrated Supply Management, No. 5(1), 19-37, 2009

In the framework of supply chain (re)- design (SCD), different structures (functional, organizational, informational, etc.) are (re)- formed. These structures are interrelated and change in their dynamics. How is it possible to avoid structural incoherency and consistency and to achieve comprehensiveness by (re)- designing supply chains? This paper introduces a new approach to simultaneous multi-structural SCD with structure dynamics considerations. We elaborate a new conceptual model and propose new tools for multi-structural SCD – multi-structural macro-states and dynamical alternative multi-graphs. The research approach is theoretically based on the combined application of operations research, agent-based modelling, and control theory. The results show the multi-structural and interdisciplinary treatment allows comprehensive and realistic SCD problem formulation and solution. We emphasize the flexibility of the proposed approach and optimization-supported simulation. The proposed methodology enhances managerial insight into supply chains at the strategic and tactical levels and serves to assist decision-makers in SCD
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A hybrid simulation optimization approach for supply chains Christian Almeder, Margaretha Preusser. EUROSIM 2007, September 9-13, Ljubljana, Slovenia

The main idea of our approach is to combine discrete-event simulation and exact optimization for supply chain network models. Simulation models are constructed in order to mimic a real system including all necessary stochastic and nonlinear elements. Such simulation models are used as proving grounds for analyzing and improving a real situation on a trial-and-error basis. A traditional optimization method on top of a simulation model has major disadvantages: The optimization method uses the simulation model as a black-box. Information about the structure of the problem is not available and cannot be used for an intelligent optimization strategy
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