Academic articles

By creating an integrated simulation environment that models the underlying structure of a pharmaceutical enterprise portfolio it becomes possible to identify the optimal longitudinal allocation of finite resources across the constellation of available investment opportunities. The implementation of a hybrid approach that integrates multiple modeling techniques and analytic disciplines allows for a comprehensive environment that captures the underlying dynamics that drive observed market behavior. The implementation of an object oriented model structure constrains the models complexity by supporting dynamic re-use of both structure and logic.

This paper may be considered as a practical reference for those who wish to add (now sufficiently matured) Agent Based modeling to their analysis toolkit and may or may not have some System Dynamics or Discrete Event modeling background. We focus on systems that contain large numbers of active objects (people, business units, animals, vehicles, or even things like projects, stocks, products, etc. that have timing, event ordering or other kind of individual behavior associated with them). We compare the three major paradigms in simulation modeling: System Dynamics, Discrete Event and Agent Based Modeling with respect to how they approach such systems. We show in detail how an Agent Based model can be built from an existing System Dynamics or a Discrete Event model and then show how easily it can be further enhanced to capture much more complicated behavior, dependencies and interactions thus providing for deeper insight in the system being modeled.

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.

The presentation shows you how to develop simulation models, choose abstraction level and methodology.

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.

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

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

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