## 1 Introduction
### Pre-Design Evaluation (PDE), Models, and Performance Simulations  
Although facility management and healthcare facility design are strongly supported by reference standards and guidelines, benchmarks, and checklists [1; 26], there are contextual features that need to be considered. Since each design problem is unique, the demands for better designs require a personalized approach, in which the solutions are individually evaluated even when they are based on standards and benchmark studies [27]. In order to evaluate the adequacy of these aspects, it is preferable to consider the key performance indicators (KPIs) that are associated with descriptive techniques [28]. These descriptive techniques can take the form of analytical approaches, simulations, and statistical/empirical modeling [28; 29]. The approaches that are adopted are related to the model's complexity and processing capacity, and the skills that are needed to operate the model [7; 26]. Simulation models emulate the approximate behavior of real systems in great detail. They can be used to model discrete events or system dynamics, or they can be used in agent-based modeling [28; 30]. However, a realistic simulation requires a balance of probability distributions and an accurate and meaningful input dataset [28; 31], which can be obtained through sensors [9]; georeferencing; a global positioning system (GPS), which is used to create hourly routine snapshots of all tagged individuals [12]; and so on. Different models have been applied in order to analyze the mapping of the workflow and the allocation of activities in built environments [12; 27; 32; 33], as well as for other applications, such as generating floorplan layouts [32; 34; 35], lighting simulations [36; 37], analyses of visual quality [38], behavior prediction [39], determining building envelope costs [40], etc. These simulation models mainly support the design phase when the design demands have been identified and the functional requirements have already been defined. Statistical/empirical models rely solely on empirical data to estimate the relationship between the system parameters and the performance measures. They require a large amount of detailed data in order to model a scenario that is useful, i.e., statistically valid. This is a suitable approach for formulating and improving sectoral policies, standards, and indicators, such as KPIs [10; 28]. The application of these models in single cases, such as a building complex, is less common because the conditions for the operation of such models are available only in restricted socioeconomic, technological, and institutional contexts where massive amounts of data are available, for example, in studies with access to data that were recorded through capture technologies, such as real-time location-based services (RTLS) [2; 10; 41]. The modeling of single cases without the availability of reliable sampling--that is, in a unique context and with a relatively small amount of data--demands another approach. Analytical models employ a set of mathematical equations in order to determine the relationship between parameters and the system performance. They are popular methods because they present simple and efficient solutions when they are not used to model stochastic and dynamic systems [28]. The analysis of reduced datasets benefits from the use of this approach. For example, when studying a particular arrangement of activities in an environment in order to support decisions about changes in that arrangement, data collection and analysis need to be fast and accessible. The process must be fast because it is responding to an urgent demand, and it must be accessible so that it can be applied in different contexts. A lightweight strategy that is adaptable to different data volumes, and that has already been adopted in the development of complex systems, is the dependency (or development or design) structures matrix (DSM) [42; 43].