Soft with a hard centre: discrete-event simulation in facilitation

Discrete event simulation is normally described as a ‘hard’ OR technique. This may not, however, always be the case. An example of a simulation of a user support helpline is described which, it is argued, has many of the traits of a ‘soft’ OR intervention. In particular, the study involved a facilitated discussion around a simulation model about possible improvements to a problem situation. The nature of the intervention is considered from both a methodological and paradigmatic perspective, and conclusions are drawn about where the intervention lies on a ‘hard’ to ‘soft’ continuum. It is argued that ‘soft’ issues need to be subsumed into the prescribed methodology for discrete-event simulation.     

Comparing discrete-event simulation and system dynamics: users' perceptions

This paper is the first of its type in that it provides an empirical study comparing the two simulation approaches of discrete-event simulation (DES) and system dynamics (SD). Prior comparison work is limited and mostly based on the authors' personal opinions. In the present work, the comparison is based on managers' (executive MBA students) perceptions of two simulation models of the same problem, one in DES and one in SD. The study found that there is no significant difference from the users' point of view between DES and SD in terms of model understanding and model usefulness. Some minor differences were found in terms of complexity and validity of the models, and the model results. The implications of our findings regarding model understanding, model complexity, model validity, model usefulness and model results are discussed.     

General concepts of quality for discrete-event simulation

With the exception of validity, discrete-event simulation quality is a little understood concept. To improve this situation, a model of simulation quality is presented that aims to develop an understanding of the quality concept across a range of approaches to performing discrete-event simulation studies. The model consists of three separate, but related, quality concepts concerning the (i) content, (ii) process, and (iii) outcome of a simulation study. These concepts, their relationships, and their importance are discussed. The concepts are explored further, and a set of dimensions of simulation quality are proposed, through an empirical investigation within a business simulation context. Methods of assessing simulation quality are also discussed, although few approaches exist for evaluating process and outcome quality, since the extant methods concentrate on the technical content of the work. The model presented here may not only be of interest to simulation modellers, but also to the wider management science/operational research community.     

The use of search experimentation in discrete-event simulation practice

The practice of DES experimentation has not been rigorously assessed in over a decade. Past studies of DES practice report little transfer of experimentation theory into real-world application. We conducted an international survey of over 300 modellers to investigate the extent to which simulation optimisation, meta-modelling and design of experiments are used in practice. Over the last decade there has been substantial growth in the use of optimisation and to a lesser extent design of experiments to tackle practical problems. However, users rarely make use of optimisers bundled with commercial software, opting instead for custom or third-party solutions. Outside of academia, the use of methods is hampered by a lack of application knowledge and a persisting view that such techniques are not necessary. It is clear that academics must not become complacent regarding the dissemination of theory into common practice and continue to reach out to industry users.     

Learning from discrete-event simulation: Exploring the high involvement hypothesis

Discussion of learning from discrete-event simulation often takes the form of a hypothesis stating that involving clients in model building provides much of the learning necessary to aid their decisions. Whilst practitioners of simulation may intuitively agree with this hypothesis they are simultaneously motivated to reduce the model building effort through model reuse. As simulation projects are typically limited by time, model reuse offers an alternative learning route for clients as the time saved can be used to conduct more experimentation. We detail a laboratory experiment to test the high involvement hypothesis empirically, identify mechanisms that explain how involvement in model building or model reuse affect learning and explore the factors that inhibit learning from models. Measurement of learning focuses on the management of resource utilisation in a case study of a hospital emergency department and through the choice of scenarios during experimentation. Participants who reused a model benefitted from the increased experimentation time available when learning about resource utilisation. However, participants who were involved in model building simulated a greater variety of scenarios including more validation type scenarios early on. These results suggest that there may be a learning trade-off between model reuse and model building when simulation projects have a fixed budget of time. Further work evaluating client learning in practice should track the origin and choice of variables used in experimentation; studies should also record the methods modellers find most effective in communicating the impact of resource utilisation on queuing.     

Forecasting transport requirements for district primary health care in Ghana: A simulation study

In rural planning in developing countries, random factors such as vehicle breakdown, weather conditions and staff availability are particularly important. These can be dealt with specially well by simulation. Transportation is also particularly important in rural areas. This paper presents a simulation study of transport requirements for a rural primary health care scheme in Ghana where the random factors are unavailability of vehicles due to repairs and unavailability of health specialists and drivers for work due to holidays, etc. The analysis gives a quick method of calculating the expected number of jobs that can be done for given numbers of vehicles, levels of availability etc. The simulation gives the corresponding probability distribution. The analysis also indicates which random factor has most effect on limiting the number of jobs that can be done. Policy conclusions are drawn regarding the number of vehicles per district and the payment of incentives to staff for good attendance.     

Forty years of discrete-event simulation—a personal reflection

Discrete-event simulation first emerged in the late 1950s and it has grown in popularity steadily to be now recognized as the most frequently used of the classical Operational Research techniques across a range of industries—manufacturing, travel, finance, health and beyond. I have been engaged with such simulation from 1964 up to the present day. This paper reviews the history and evolution of discrete-event simulation from his personal perspective, with a particular interest in software development up to 1992. Extrapolating from that history, the paper goes on to comment on the prospective continuing evolution of simulation and its software.     

Combining discrete-event simulation and system dynamics in a healthcare setting: A composite model for Chlamydia infection

This paper presents a composite model in which two simulation approaches, discrete-event simulation (DES) and system dynamics (SD), are used together to address a major healthcare problem, the sexually transmitted infection Chlamydia. The paper continues an on-going discussion in the literature about the potential benefits of linking DES and SD. Previous researchers have argued that DES and SD are complementary approaches and many real-world problems would benefit from combining both methods. In this paper, a DES model of the hospital outpatient clinic which treats Chlamydia patients is combined with an SD model of the infection process in the community. These two models were developed in commercial software and linked in an automated fashion via an Excel interface. To our knowledge this is the first time such a composite model has been used in a healthcare setting. The model shows how the prevalence of Chlamydia at a community level affects (and is affected by) operational level decisions made in the hospital outpatient department. We discuss the additional benefits provided by the composite model over and above the benefits gained from the two individual models.     

Exploring the model development process in discrete-event simulation: insights from six expert modellers

This paper explores the model development process in discrete-event simulation (DES) by reporting on an empirical study that follows six expert modellers while building simulation models. DES is a widely used modelling approach, however little is known about the modelling processes and methodology adopted by modellers in practice. Verbal Protocol Analysis is used to collect data, where the participants are asked to speak aloud while modelling. The results show that the expert modellers spend a significant amount of time on model coding, verification and validation, and data inputs. The modellers iterate often between modelling activities. Patterns of modelling behaviour are identified, suggesting that the modellers adopt distinct modelling styles. This study is useful in that it provides an empirical view of existing DES modelling practice, which in turn can inform existing research and simulation practice as well as teaching of DES modelling to novices.     

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.     

A statistical process control approach to selecting a warm-up period for a discrete-event simulation

The selection of a warm-up period for a discrete-event simulation continues to be problematic. A variety of selection methods have been devised, and are briefly reviewed. It is apparent that no one method can be recommended above any other. A new approach, based upon the principles of statistical process control, is described (SPC method). Because simulation output data are often highly autocorrelated and potentially non-normal, the batch means method is employed in constructing the control chart. The SPC method is tested on seven data sets and encouraging results are obtained concerning its accuracy. The approach is also discussed with respect to its ease of implementation, simplicity, generality of use and requirements for parameter estimation.     

Automated selection of the number of replications for a discrete-event simulation

Selecting an appropriate number of replications to run with a simulation model is important in assuring that the desired accuracy and precision of the results are attained with minimal effort. If too few are selected then accuracy and precision are lost; if too many are selected then valuable time is wasted. Given that simulation is often used by non-specialists, it seems important to provide guidance on the number of replications required with a model. In this paper an algorithm for automatically selecting the number of replications is described. Test results show the algorithm to be effective in obtaining coverage of the expected mean at a given level of precision and in reducing the bias in the estimate of the mean of the simulation output. The algorithm is consistent in selecting the expected number of replications required for a model output.     

Use of business process simulation: A survey of practitioners

In order to understand the requirements of people engaged in business process simulation (BPS), a survey was conducted among potential business process simulation users. The survey had a 37% response rate and revealed a low usage of simulation in the design, modification and improvement of business processes. It confirms that BPS projects are typically short, relatively non-technical, and rely on good project management for their success. Most BPS users employ general-purpose simulation software rather than purpose-designed business process simulators. There is no evidence of a skills gap, rather a feeling that there is no net gain from employing simulation methods when simpler methods will suffice. These findings are discussed and conclusions drawn.     

Natural discrete-event process forecasting: A decision support system

A decision support system for the analysis and forecasting of natural discrete-event processes is considered. The corresponding method is based on the sample path analysis using event-to-event operations. An application of the method in the computer-aided decision support system for the long-range weather forecasting is mentioned.     

On the definition of a computational fluid dynamic solver using cellular discrete-event simulation

The Discrete Event System Specification (DEVS) has rarely been applied to the physics of motion. To explore the formalism's potential contribution to these applications, we need to investigate the definition of moving gases, liquids, rigid bodies, and deformable solids. Here, we show how to use Cell-DEVS to analyze the movement and interactions of fluids using computational fluid dynamics (CFD). We describe a set of rules that produce the same patterns as traditional CFD implementations. We present the inner workings of the CFD algorithm, the incorporation of solid barriers, and the adoption of variable time steps within the context of biomechanical simulations.     

Use of discrete-event simulation to evaluate strategies for the prevention of mother-to-child transmission of HIV in developing countries

HIV/AIDS affects over 40 million people worldwide, and more than 70% of these people live in Africa. Mother-to-child transmission of HIV accounts for over 90% of all HIV infections in children under the age of 15 years. However, implementing HIV prevention policies in Africa is extremely difficult because of the poor medical and socio-economic infrastructure. In this paper, we present a discrete-event simulation model that evaluates the relative benefits of two potentially affordable interventions aimed at preventing mother-to-child transmission of HIV, namely anti-retroviral treatment at childbirth and/or bottlefeeding strategies. The model uses rural Tanzanian data and compares different treatment policies. Our results demonstrate that strategic guidelines about breastfeeding are highly dependent on the assumed increase in infant mortality due to bottlefeeding, the efficacy of anti-retroviral treatment at childbirth, and the maternal health stage. The cost of averted infections, though low by Western standards, may represent significant obstacles to policy implementation in developing countries.     

Conceptual modelling and the project process in real simulation projects: a survey of simulation modellers

A survey was used to obtain information on the processes and methods used by simulation experts in real projects. The 102 survey respondents answered questions about their most recent simulation project. This paper presents some of the survey results, focussing mainly on conceptual modelling and the pattern of time allocation to different topics. There are a wide range of findings that include the modellers making changes to the initial conceptual model during subsequent tasks in most of the projects usually by adding complexity, model coding taking on average about twice the time of other topics, and the topics generally occurring in single blocks of time (at the resolution of the survey data collection) but with considerable overlaps. The results give an insight into the way experts approach simulation projects and their problem solving strategies. A potential application is in training novice modellers, particularly in developing ‘craft skills’. The results also provide an empirical basis for further research, especially in conceptual modelling.     

A simulation case study to improve staffing decisions at mass immunization clinics for pandemic influenza

Mass immunization clinics (MICs) are an important component of pandemic influenza control strategies in many jurisdictions. Decisions about staffing levels at MICs affect several factors of concern to public health authorities: total vaccination volume, patient wait-times, operating costs, and intra-facility influenza transmission risk. We present a discrete-event simulation of an MIC to assess how strongly staffing changes affect these factors. The simulation is based on data from Canadian clinics responding to pandemic H1N1 in 2009. This study is the first to model flu transmission risk at an MIC, and the first to relate such risk to staffing decisions. We show that the marginal benefit of adding staff is greatly underestimated if indirect waiting costs and intra-facility infections are not considered.     

Efficiency and total quality management in health care organizations: A dynamic frontier approach

This paper analyses hospital performance using Data Envelopment Analysis (DEA) and the Malmquist productivity index. We follow two approaches to quantify movements in productivity: (1) the traditional approach that only considers output and input variables; and (2) a more comprehensive approach that incorporates movements in quality and restricts some achievements, if quality is reduced. On the premise that the indicator for quality (nosocomial infections) is equivalent to a bad output, we explore the characteristics of, and compare the results of, the different technological ways to incorporate quality (good or bad attributes, strong or weak disposability technological assumptions). After discussing the virtues and limitations of the existing possibilities, the paper presents a better formulation that allows the preservation of TQM postulates. The decomposition in the Malmquist productivity index shows an improvement in productivity and a positive technical change, especially when quality is introduced. This paper forms part of a more extensive research work, financed by the Spanish Science and Technology Ministry (ref. SEC2003-047707).