Discrete-event simulation: from the pioneers to the present, what next?

Discrete-event simulation is one of the most popular modelling techniques. It has developed significantly since the inception of computer simulation in the 1950s, most of this in line with developments in computing. The progress of simulation from its early days is charted with a particular focus on recent history. Specific developments in the past 15 years include visual interactive modelling, simulation optimization, virtual reality, integration with other software, simulation in the service sector, distributed simulation and the use of the worldwide web. The future is then speculated upon. Potential changes in model development, model use, the domain of application for simulation and integration with other simulation approaches are all discussed. The desirability of continuing to follow developments in computing, without significant developments in the wider methodology of simulation, is questioned

A tutorial on simulation conceptual modeling

© 2017 IEEE. Conceptual modeling is the abstraction of a simulation model from the part of the real world it is representing; in other words, choosing what to model, and what not to model. This is generally agreed to be the most difficult, least understood, but probably the most important activity to be carried out in a simulation study. In this tutorial we explore the definition, requirements and approach to conceptual modeling. First we ask 'where is the model?' We go on to define the term 'conceptual model', to identify the artefacts of conceptual modeling, and to discuss the purpose and benefits of a conceptual model. In so doing we identify the role of conceptual modeling in the simulation project life-cycle. The discussion then focuses on the requirements of a conceptual model, the approaches for documenting a conceptual model, and frameworks for guiding the conceptual modeling activity. One specific framework is described and illustrated in more detail. The tutorial concludes with a discussion on the level of abstraction

Modelling without queues: adapting discrete-event simulation for service operations

Discrete-event simulation (DES), which has largely grown out of modelling manufacturing systems, has increasingly been applied in the service sector. The approach, however, is not always appropriate for modelling service operations. In particular, it cannot help with detailed decisions about the layout of service operations in which the customers are present such as retail outlets and airports. An adapted DES approach is proposed for modelling such systems and the approach is demonstrated through a model of a coffee shop. A key innovation is that queues are not explicitly modelled. The benefit of the approach is that it simplifies the modelling of service systems in which the customers are present by reducing the number of components that need to be modelled. It can also aid decisions about the layout of a system. We ask whether the approach is in fact an agent-based simulation and identify ways in which the approach could be extended

Model development in discrete-event simulation and system dynamics: an empirical study of expert modellers

An empirical study comparing the model development process followed by experts in discrete-event simulation (DES) and system dynamics (SD) modelling is undertaken. verbal protocol analysis (VPA) is used to study the modelling process followed by ten expert modellers (5 SD and 5 DES). Participants are asked to build simulation models based on a case study and to think aloud while modelling. The generated verbal protocols are divided into seven modelling topics: problem structuring, conceptual modelling, data inputs, model coding, verification & validation, results & experimentation and implementation and then analyzed. Our results suggest that all modellers switch between modelling topics, however DES modellers follow a more linear progression. DES modellers focus significantly more on model coding and verification & validation, whereas SD modellers on conceptual modelling. Observations are made revealing some interesting differences in the way the two groups of modellers tackle the case. This paper contributes towards the comparison of DES and SD

The application of discrete event simulation and system dynamics in the logistics and supply chain context

Discrete event simulation (DES) and system dynamics (SD) are two modelling approaches widely used as decision support tools in logistics and supply chain management (LSCM). A widely held belief exists that SD is mostly used to model problems at a strategic level, whereas DES is used at an operational/tactical level. This paper explores the application of DES and SD as decision support systems (DSS) for LSCM by looking at the nature and level of issues modelled. Peer reviewed journal papers that use these modelling approaches to study supply chains, published between 1996 and 2006 are reviewed. A total of 127 journal articles are analysed to identify the frequency with which the two simulation approaches are used as modelling tools for DSS in LSCM. Our findings suggest that DES has been used more frequently to model supply chains, with the exception of the bullwhip effect, which is mostly modelled using SD. Based on the most commonly used modelling approach, issues in LSCM are categorised into four groups: the DES domain, the SD domain, the common domain and the less common domain. The study furthermore suggests that in terms of the level of decision making involved, strategic or operational/tactical, there is no difference in the use of either DES or SD. The results of this study inform the existing literature about the use of DES and SD as DSS tools in LSCM

Is simulation in health different and is it more difficult?

It is often stated that health simulation is quite different and even that it is more difficult. But, is simulation in health really different to simulation in other sectors? In this paper we explore this question through a survey of simulation modellers and academics. We elicit their opinions across a range of factors concerning the difficulties of health modelling against modelling in other domains. The results seem to corroborate the view that health simulation is different and that it is more difficult. However, further investigation into the backgrounds of those responding and the development of objective measures for the factors surveyed may show quite a different picture

Is simulation in health different?

It is often stated that health simulation is quite different and even that it is more difficult than in other sectors. But, is simulation in health really different to simulation in other sectors elsewhere? In this paper we explore this question through a survey of simulation modellers and academics. We elicit their opinions across a range of factors concerning the difficulties of health modelling against modelling in other domains. The analysis considers the responses of the whole group of respondents and the sub-group of respondents who have experience both in and outside of health modelling. The results show that, overall, there is a perception that health modelling is different and that it is more difficult across a range of factors. The implications for simulation research and practice in health are discussed

An experimental investigation into the role of simulation models in generating insights

It is often claimed that discrete-event simulation (DES) models are useful for generating insights. There is, however, almost no empirical evidence to support this claim. To address this issue we perform an experimental study which investigates the role of DES, specifically the simulation animation and statistical results, in generating insight (an ‘Aha!’ moment). Undergraduate students were placed in three separate groups and given a task to solve using a model with only animation, a model with only statistical results, or using no model at all. The task was based around the UK’s NHS111 telephone service for non-emergency health care. Performance was measured based on whether participants solved the task with insight, the time taken to achieve insight and the participants’ problem-solving patterns. The results show that there is some association between insight generation and the use of a simulation model, particularly the use of the statistical results generated from the model. While there is no evidence that insights were generated more frequently from statistical results than the use of animation, the participants using the statistical results generated insights more rapidly

Using LIWC to choose simulation approaches: A feasibility study

Can language usage help determine which model approach is best suited to provide decision makers with desired insights? This research addresses that question through an investigation of Linguistic Inquiry and Word Count (LIWC), which calculates the presence of more than 80 language dimensions in text samples, and permits construction of custom dictionaries. This article demonstrates use of LIWC to ensure better problem/model fit within the context of selecting a decision support tool. We selected two simulation tools as research instruments to investigate a broader question on the usefulness of LIWC to guide choice of DSS tool. The tools selected were System Dynamics (SD) and Discrete Event Simulation (DES). First, we tested LIWC to analyze practitioners’ language use when developing models. LIWC pointed out significant linguistic differences consistent with prior theoretical work, based on model development approach in a number of dimensions. These differences provided a basis for developing a custom dictionary for use on the second part of our study. The second part of the study focused on language used by decision makers in problem statements and used the linguistic clues identified in the first part of the study to ensure problem/model fit. Results indicated problem statements contained linguistic clues related to the type of information desired by problem solvers. The article concludes with a discussion about how LIWC and similar tools can help determine which DSS tools are suited to particular applications

A preliminary study on the role of simulation models in generating insights

The generation of insight from simulation models has received little attention in the discrete-event simulation (DES) literature. Often DES studies claim to have supported problem understanding and problem solving by creating new and effective ideas, however little empirical evidence exists to support these statements. This paper presents the design of an experimental study which aims to understand the role of simulation models in generating insights. Study participants are asked to solve a task based on a problem of a telephone service for non-emergency health care. One independent variable is manipulated: the features of the simulation model, forming three conditions. Participants either use the animation or only the statistical results of the model or no model at all to solve the task. The paper provides a preliminary analysis of the pilot tests, which indicates that simulation models may assist users in gaining better understanding and in achieving divergent thinking