Agile design meets hybrid models: using modularity to enhance hybrid model design and use

Dynamic modeling offers many benefits to understand the dynamics of complex systems. Hybrid modeling attempts to bring together the complementary benefits of differing dynamic modeling approaches, such as System Dynamics and Agent-based modeling, to bear on a single research question. We present here, by means of an example, a hybrid modeling technique that allows different modules to be specified separately from their implementation. This enables each module to be designed and constructed on an ad-hoc basis. This approach results in 3 benefits: it facilitates incremental development, a key focus in agile software design; it enhances the ability to test and learn from the behavior of a dynamic model; and it can help with clearer thinking about model structure, especially for those of a hybrid nature.

Hospital processes within an integrated system view: a hybrid simulation approach

Processes in hospitals or in other healthcare institutions are usually analyzed and optimized isolated for enclosed organizations like single hospital wards or certain clinical pathways. However, many workflows should be considered in a broader scope in order to better represent the reality, i.e., in combination with other processes and in contexts of macro structures. Therefore, an integrated view is necessary which enables to combine different coherences. This can be achieved by hybrid simulation. In this case, processes can be modeled and simulated by discrete simulation techniques (i.e., DES or ABS) at the meso-level. However, holistic structures can be comfortably implemented using continuous methods (i.e., SD). This paper presents a theoretical approach that enables to consider reciprocal influences between processes and higher level entities, but also to combine hospital workflows with other subjects (e.g., ambulance vehicles).

Using hybrid simulation modeling to assess the dynamics of compassion fatigue in veterinarian general practitioners

Veterinarians have experienced disturbing trends related to workplace-induced stress. This is partly attributed to high levels of compassion fatigue, the emotional strain of unalleviated stress from interactions with those suffering from traumatic events. This paper presents a three-stage hybrid model designed to study the dynamics of compassion fatigue in veterinarians. A discrete event simulation that represents the work environment is used to generate client and patient attributes, and the veterinarian’s utilization throughout the day. These values become inputs to a system dynamics model that simulates the veterinarian’s interpretation of the work environment to produce quantifiable emotional responses in terms of eight emotions. The emotional responses are mapped to the Professional Quality of Life Scale, which enables the calculation of compassion satisfaction, burnout, and secondary traumatic stress measures. A pilot study using the hybrid model was conducted to assess the viability of the proposed approach, which yielded statistically significant results.

An agent-based framework to study occupant multi-comfort level in office buildings

With the trend towards energy efficient buildings that diminish fossil fuel usage and carbon emissions, achieving high energy performance became a necessity. Allowing occupants to be actively involved during the design and operation phases of buildings is vital in fulfilling this goal without jeopardizing occupant satisfaction. Although different occupant behavior types were considered in prior research efforts, recent tools did not however examine simultaneously visual, thermal and acoustic comfort levels. This paper presents work targeted at efficiently studying occupant multi-comfort level using agent-based modeling with the ultimate aim of reducing energy consumption within academic buildings. The proposed model was capable of testing different parameters and variables affecting occupant behavior. Several scenarios were examined and statistical results demonstrated that the presence of different occupant behavior types is deemed necessary for a more realistic overall model, and the absence of windows results in an acoustic satisfaction with a decrease in (HVAC) use.

Improving patient access to a public hospital complex using agent simulation

This paper uses agent based simulation to assess the effect of redesigning the points of access to a major public hospital complex in Chile, where nearly 15,000 people will pass through daily. The study is carried out by simulating pedestrian traffic in order to calculate density maps and service levels in hospital access and ramps. The simulation allows us to evaluate the flow of people and assess the layout performance, by identifying high patient flow areas and congested pedestrian traffic zones. By using this approach, it is possible to suggest changes to the original design and to improve pedestrian flow at hospital access points and ramps. The suggested changes reveal that pedestrian indicators could be improved, which in turn would improve the level of satisfaction of patients, relatives, and hospital personnel. A higher satisfaction level would help to reduce stress linked to hospital facilities and crowded spaces.

Process modeling for simulation: observations and open issues

We review the state of the art of process modeling for discrete event simulation, make a number of observations and identify a number of issues that have to be tackled for promoting the use of process modeling in simulation. Process models are of particular interest in model-based simulation engineering approaches where the executable simulation model (code) is obtained with the help of textual or visual models. We present an illustrative example of model-based simulation development.

Discrete simulation software ranking – a top list of the worldwide most popular and used tools

This paper documents a work on all-purpose discrete event simulation tools evaluation. Selected tools must be suitable for process design (e.g. manufacturing or services industries). Rather than making specific judgments of the tools, authors tried to measure the intensity of usage or presence in different sources, which they called “popularity”. It was performed in several different ways, including occurrences in the WWW and scientific publications with tool name and vendor name. This work is an upgrade to the same study issued 5 years ago (2011), which in its turn was also an upgrade of 10 years ago (in 2006). It is obvious that more popularity does not assure more quality, or being better to the purpose of a simulation tool; however, a positive correlation may exist between them. The result of this work is a short list, of 19 commercial simulation tools, with probably the nowadays’ most relevant ones.

Evaluation of modeling tools for autocorrelated input processes

Queuing systems of any domain oftentimes exhibit correlated arrivals that considerably influence system behavior. Unfortunately, the vast majority of simulation modeling applications and programming languages do not provide the means to properly model the corresponding input processes. In order to obtain valid models, there is a substantial need for tools capable of modeling autocorrelated input processes. Accordingly, this paper provides a review of available tools to fit and model these processes. In addition to a brief theoretical discussion of the approaches, we provide tool evaluation from a practitioners perspective. The assessment of the tools is based on their ability to model input processes that are either fitted to a trace or defined explicitly by their characteristics, i.e., the marginal distribution and autocorrelation coefficients. In our experiments we found that tools relying on autoregressive models performed the best.

Outplacement time and probability estimation using discrete event simulation

In today’s rapidly changing technological scenario, tech giants revise their strategic alignment every couple of years. As a result, their workforce has to be adapted to the organization’s strategy. Members of the workforce who are neither relevant to the strategic alignment, nor can be made relevant by reskilling, have to be either outplaced (i.e. placed in an another job within organization) or separated from the organization. In geographies like Europe, where the cost of separation is very high, it becomes very important to make the right decision for each employee. In this paper, we describe a simulation based methodology to find the probability and time of outplacement of an employee. These numbers are inputs to a global problem of making the optimal decision for the entire workforce.

ADD-MORE: automated dynamic display of measures of risk and error

Simulation is commonly used for decision-making on the design and operation of manufacturing (Negahban and Smith 2014), healthcare (Mielczarek and Uzialko-Mydlikowska 2012), and military (Naseer, Eldabi, and Jahangirian 2009) systems as well as in supply chain management (Terzi and Cavalieri 2004), marketing (Negahban and Yilmaz 2014), and social sciences (Axelrod 1997). The work-in-progress (WIP) in a production line, number of patients waiting for treatment at an emergency department (ED), space utilization of a distribution center, and future sales/demand for a new technology are examples of typical performance measures estimated/predicted through simulation. Due to the stochastic nature of the different components of such dynamic systems, many of the inputs of a simulation model are random (e.g., stochastic processing times on machines in a production line, patient arrivals into an emergency department, the number of SKUs in an order received by a warehouse, or consumers’ purchasing behavior and word-of-mouth after the launch of a new product). As a result, the output (performance measures) are also random variables making the assessment of the level of error in the predictions of the simulation model and the level of uncertainty in the possible values (i.e., distribution) of the measure(s) of interest critical for effective decision-making.