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S. Emrich, D. Wiegand:
"Harnessing Mathematical Simulation to Intensify Building Utilization";
Vortrag: Wissenschaftstag der Fakultät für Raumplanung und Architektur, TU Wien, Wien; 07.11.2012; in: ""Mehr-Wert Architektur & Raumplanung", Programmheft zum Wissenschaftstag 2012", TU Wien Eigenverlag, (2012), S. 36 - 37.

The debate around global warming and necessary consequences is still far from being settled; nevertheless it has become clear that the ecological footprint has to be reduced drastically. But massive Investments in new technology can only be a part of this effort. As replacement of all existing by State of the art buildings is unfeasible, the question arises what to do with them?
A simple but elegant Solution for this is to increase utilization- efficiency. This directly leads to a better ratio Footprint/Utilization - and features positive economic side-effects. Using space in a more efficient manner not only reduces the costs per unit (whatever "unit" this is) but also makes it unnecessary to construct new buildings - saving further resources (material, energy, money,...). As proposed in a study by the ETH Zürich educational facilities hold a high potential yield with respect to such savings (see [1]).
Within the research project "MoreSpace" a model (based on Discrete Event Simulation, Cellular Autamata. Ageni-based techniques and Business Process Modeling - see [2, 3]) for Simulation of room utilization has been developed, with a primary focus on university buildings.
While the overall goal of the Simulation is to increase the utilization of (lecture) rooms, there are several approaches to reach it. Depending on the questions formulated towards the Simulation model, differing requirements are implied which influence the mode of Operation, necessities towards (input) data, model preconditions, depth of system-integration, quality management, etc. Thus three possible deployment-modes, including their peculiarities, are identified and addressed:
1. Day-to-day operations
Example: Daily evaluation of room booking strategies to choose the best.
2. Strategie planning - System assessment
Example: Simulation runs at the end of each semester, based on historical data to test & compare different management strategies.
3. Consulting purposes:
Example: Consulting for Singular evaluation of adequate allocation plan for given student numbers.
Subsequently the starting point has been the analysis of the model preconditions. These were split into structural preconditions and necessary input-data. For the latter an entity relationship model (ERM), describing the database for the Simulation model, has been developed. To identify transformation needs, (quantitative and qualitative) assessment of available data became necessary. This required a stakeholder analysis to investigate their interconnecting relationships. For System integration model preconditions and deployment scenarios' peculiarities are checked for compatibility in a "deployment matrix" which had to be developed.
Finally post processing of simulation results (including data mining, Statistical analysis and visualization) was necessary to draw sensible conclusions, as it is not only necessary to deploy a model successfully, but also to utilize the knowledge generated by its deployment.

Room utilization, Dynamic Simulation, Space Management, Deployment Strategies

http://publik.tuwien.ac.at/files/PubDat_222792.pdf