Academic articles

A combined discrete-continuous simulation model for analyzing train-pedestrian interactions


Computer simulation has defined itself as a reliable method for the analysis of stochastic and dynamic complex systems in both academic and practical applications. This is largely attributed to the advent and evolution of several simulation taxonomies, such as, Discrete Event Simulation, Continuous Simulation, System Dynamics, Agent-Based Modeling, and hybrid approaches, e.g., combined discrete-continuous simulation, etc. Each of these simulation methods works best for certain types of problems. In this paper, a discrete-continuous simulation approach is described for studying train and pedestrian traffic interactions for purposes of decision support. A practical operations problem related to commodity train operation within two small towns in Alberta, Canada, is then used to demonstrate the implementation of the approach within the Simphony.NET simulation system. Simulation results generated are presented.

Modeling of healthcare systems: past, current and future trends


Increasing demand for healthcare services, due to changes in demographic shifts and constraints in healthcare funding, make it harder to manage effective, sustainable healthcare systems. Many healthcare modeling exercises have been undertaken with the aim of supporting the decision-making process. This paper reviews all of the 456 articles published by the Winter Simulation Conference over the past 48 years (1967–2015) on the subject of modeling and healthcare system simulation, and analyzes the relative frequency of approaches used. A multi-dimensional taxonomy is applied to encompass the modeling techniques, problem applications and decision levels reported in the articles. One of the most significant changes in the modeling of healthcare systems is the fact that Discrete-event Simulation (DES) is no longer used as an autonomous method, but rather as an integral part of the solution. The mixed-methods, hybrid and multi-paradigm approaches feature strongly in the current direction of modeling in healthcare systems.

Do hybrid simulation models always increase flexibility to handle parametric and structural changes?


Are hybrid simulation models always beneficial? When should one modeling paradigm be used more than another? How does one know the right balance has been reached between different simulation techniques for the system under investigation? We illustrate selected insights into hybrid simulation through the use of a discrete event simulation (DES) model and a hybrid DES agent based model (ABM) of the obstetrics department at Akershus University Hospital. Design decisions are not straightforward, and have different impacts on model development and ability to address different scenarios or potential changes. In the DES model, the majority of the logic and code representing patient pathways is contained within the structure of the model. In the AB-DES model, a selection of the code is shifted from the model structure to the patient entities. Scenarios are presented which illustrate strengths and weaknesses of each model. These are reflected on and future work is suggested.

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.

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.

Coordination of production and ordering policies undercapacity disruption and product write-off risk: ananalytical study with real-data based simulations of a fastmoving consumer goods company


Performance impacts of ordering and production control policies in the presence of capacity disruptions are studied on the real-life example of a retail supply chain with product perishability considerations. Constraints on product perishability typically result in reductions in safety stock and increases in transportation frequency. Consideration of the production capacity disruption risks may lead to safety stock increases. This trade-off is approached with the help of a simulation model that is used to compare supply chain performance impacts with regard to coordinated and non-coordinated ordering and production control policies.

Dynamic Recovery Policies for Time-Critical Supply Chains under Conditions of Ripple Effect


We consider time critical supply chains in the Australia dairy industry and re-covery policies in the presence of the ripple effect. Ripple effect is the impact of a dis-ruption on supply chain economic performance and disruption-based scope of changes needed in the supply structures and parameters to preserve the resilience. First, we de-scribe the ripple effect in general and one example of the ripple effect in the dairy supply chain in Australia. Second, we present a model for reactive recovery policies in the dairy supply chain under conditions of the ripple effect and exemplify them on a simulation example. The results of this study can be used in future for comparing proactive and re-active approaches to tackling the ripple effect from resilience and flexibility views.

Simulation-based Ripple Effect Modelling in the Supply Chain


In light of low-frequency/high-impact disruptions, the ripple effect has recently been intro-duced into academic literature on supply chain management. The ripple effect in the supply chain results from disruption propagation from the initial disruption point to the supply, pro-duction and distribution networks. While optimization modelling dominates this research field, the potential of simulation modelling still remains under-explored. The objective of this study is to reveal research gaps that can be closed with the help of simulation modelling.