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.     

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 participative and facilitative conceptual modelling framework for discrete event simulation studies in healthcare

Existing approaches to conceptual modelling (CM) in discrete-event simulation do not formally support the participation of a group of stakeholders. Simulation in healthcare can benefit from stakeholder participation as it makes possible to share multiple views and tacit knowledge from different parts of the system. We put forward a framework tailored to healthcare that supports the interaction of simulation modellers with a group of stakeholders to arrive at a common conceptual model. The framework incorporates two facilitated workshops. It consists of a package including: three key stages and sub-stages; activities and guidance; tools and prescribed outputs. The CM framework is tested in a real case study of an obesity system. The benefits of using this framework in healthcare studies and more widely in simulation are discussed. The paper also considers how the framework meets the CM requirements.     

Modelling and simulation in business and industry: insights into the processes and practices of expert modellers

The simulation modelling process and individual practices of simulation modellers impact on the outcome of simulation studies. Despite this, the modelling and simulation literature lacks a rich body of evidence on the actual process and practices of modellers in the real world. This qualitative study aims to address this shortcoming by attempting to discover the underlying simulation modelling process of 20 expert modellers working within their typical contexts. Our study makes a valuable addition to simulation research by investigating how the expert modellers develop their simulation models and how their context may affect their simulation practices. The main finding is that most of the participants do not have a clearly defined or a formal process for developing their models. Instead, they follow a set of key steps and their individual practices depend on the context of the study. A number of contextual factors such as the term of model use, the size of the model and the complexity of the model, may affect the way in which a modeller goes about developing a simulation model. For instance, the extent to which a model is documented depends on the model life. This research contributes to enhancing our understanding of the simulation modelling process in varying contexts.     

Widening requirements capture with soft methods: an investigation of hybrid M&S studies in health care

A simulation study consists of several stages: problem formulation, model implementation, verification and validation, experimentation and output data analysis. The application of multiple techniques in the model implementation stage is referred to as hybrid simulation, which we distinguish in this paper from a hybrid M&S study, the latter referring to studies that apply methods and techniques from disciplines like Operations Research (OR), Systems Engineering and Computer Science to one or more stages of a simulation study. Our paper focuses on the contribution of soft OR methods in the problem formulation stage of a simulation study (and by extension a hybrid M&S study). Soft Systems Methodology (SSM) has, arguably, been the most widely used qualitative approach for eliciting system requirements. In this paper, we present Qualitative System Dynamics (QSD), a soft systems method, as having potential use in the problem formulation stage of a healthcare M&S study. The contribution of this paper is thus twofold: (1) a review of the literature in SSM for healthcare operations management and (2) an examination of QSD as an additional soft OR method, complementing (rather than supplanting) existing approaches, which can further aid the understanding of the system in the problem formulation/conceptual modelling stage of a hybrid M&S study.     

Validation,verification, and testing techniques throughout the life cycle of a simulation study

Life cycle validation, verification, and testing (VV&T) is extremely important for the success of a simulation study. This paper surveys current software VV&T techniques and current simulation model VV&T techniques and describes how they can all be applied throughout the life cycle of a simulation study. The processes and credibility assessment stages of the life cycle are described and the applicability of the VV&T techniques for each stage is stated. A glossary is provided to explicitly define important terms and VV&T techniques.     

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.     

Modelling a Solid-Waste Processing System by Discrete Event Simulation

This paper describes a discrete event simulation model for a solid-waste processing system which is to be installed in Zagreb, Croatia. The conceptual model was developed by activity cycle diagrams, and the computer model was developed using the program generation software package VS6. This paper describes the validation and verification of such a model, the design of simulation experiments and the selection of the system's configuration.     

Conceptual modelling for simulation Part I: definition and requirements

Conceptual modelling is probably the most important aspect of a simulation study. It is also the most difficult and least understood. Over 40 years of simulation research and practice have provided only limited information on how to go about designing a simulation conceptual model. This paper, the first of two, discusses the meaning of conceptual modelling and the requirements of a conceptual model. Founded on existing literature, a definition of a conceptual model is provided. Four requirements of a conceptual model are described: validity, credibility, utility and feasibility. The need to develop the simplest model possible is also discussed. Owing to a paucity of advice on how to design a conceptual model, the need for a conceptual modelling framework is proposed. Built on the foundations laid in this paper, a conceptual modelling framework is described in the paper that follows.     

A cognitive core for common state standards

The purpose of this paper is to illustrate cognitive challenges introduced by Common Core State Standards for Mathematics (2010) with regard to conceptualizing fractions. We focus on a strand of standards that appear across grades three through five, which is best represented in grade four, by standard 4.NF.4a: “[Students should] understand a fraction a/b as a multiple of 1/b. For example, use a visual fraction model to represent 5/4 as the product 5 × (1/4), recording the conclusion by the equation 5/4 = 5 × (1/4).” We argue that supporting such conceptualizations is a critical challenge for teachers and researchers to address in US education, but one fraught with conceptual hurdles for students. We adopt a scheme theoretic perspective and share the case study of a sixth-grade student, to illustrate the ways of operating students need to develop for meaningful attainment of the new standards.     

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.     

Relevance assumed: a case study of balanced scorecard development using system dynamics

The balanced scorecard (BSC) has become a popular concept for performance measurement. It focuses attention of management on only a few performance measures and bridges different functional areas as it includes both financial and non-financial measures. However, doubts frequently arise regarding the quality of the BSCs developed as well as the quality of the process in which this development takes place. This article describes a case study in which system dynamics (SD) modelling and simulation was used to overcome both kinds of problems. In a two-stage modelling process (qualitative causal loop diagramming followed by quantitative simulation), a BSC was developed for management of one organizational unit of a leading Dutch insurer. This research illustrates how, through their involvement in this development process, management came to understand that seemingly contradictory goals such as customer satisfaction, employee satisfaction and employee productivity were, in fact, better seen as mutually reinforcing. Also, analysis of the SD model showed how, contrary to ex ante management intuition, performance would first have to drop further before significant improvements could be realized. Finally, the quantitative modelling process also helped to evaluate several improvement initiatives that were under consideration at the time, proving some of them to have unclear benefits, others to be very promising indeed.     

A Trajectory for Validating Computational Emulation Models of Organizations

Validation of complex simulation models is a challenging problem in computational organization theory research. In this paper, we describe a validation strategy suitable for emulation simulation systems, and show how a comprehensive validation consists of a sequence of steps that evaluate different aspects of a computational organizational simulation model. We demonstrate how this strategy can be applied to the evaluation of the Virtual Team Alliance (VTA), an emulation simulation system designed to guide managers charged with organizational change. VTA required a &201C;trajectory&201D; of successive validation experiments, before managers where willing to follow the recommendations of VTA. Ultimately, we believe this validation approach can be applied to a wide range of different simulation systems, and will make identification of the strengths and weaknesses of organizational simulations easier.     

Two-worker blocking congestion model with walk speed m in a no-passing circular passage system

This paper introduces a blocking model and closed-form expression of two workers traveling with walk speed m (m = integer) in a no-passing circular-passage system of n stations and assuming n = m + 2, 2m + 2, …. We develop a Discrete-Timed Markov Chain (DTMC) model to capture the workers’ changes of walk, pick, and blocked states, and quantify the throughput loss from blocking congestion by deriving a steady state probability in a closed-form expression. We validate the model with a simulation study. Additional simulation comparisons show that the proposed throughput model gives a good approximation of a general-sized system of n stations (i.e., n > 2), a practical walk speed system of real number m (i.e., m ? 1), and a bucket brigade order picking application.     

An evaluation of the applicability of system dynamics to patient flow modelling

The objective of this research is to determine whether Systems Dynamics (SD) is a beneficial method for modelling hospital patient flow from a strategic planning perspective. While discrete event simulation has frequently been used as a tool for analysing and improving patient flow in health care settings, the desire to assess and understand patient flow and resource demand from a more strategic, and therefore aggregate, perspective led to the use of SD. To evaluate the suitability of such an approach, a model was developed in collaboration with the General Campus at The Ottawa Hospital with particular attention paid to the delays experienced by patients in the emergency department. The modelling techniques used, model validation and scenarios tested with the model are discussed, accompanied by comments regarding the appropriateness of SD for such a model.     

Study of storm surge due to Typhoon Linda (1997) in the Gulf of Thailand using a three dimensional ocean model

Numerical integrations using the three dimensional ocean model based on the princeton ocean model (POM) were applied for the study of both sea level elevation and ocean circulation patterns forced by the wind fields during typhoons that moved over the Gulf of Thailand (GoT). The simulation concerned a case of Typhoon Linda which occurred during November 1-4, 1997. Typhoon Linda was one of the worst storms that passed the Gulf of Thailand and hit the southern coastal provinces of Thailand on November 3, 1997. It caused flooding and a strong wind covering large areas of agriculture and fisheries, which destroyed households, utilities and even human lives. The model is the time-dependent, primitive equation, Cartesian coordinates in a horizontal and sigma coordinate in the vertical. The model grid has 37 × 97 orthogonal curvilinear grid points in the horizontal, with variable spacing from 2 km near the head of the GoT to 55 km at the eastern boundary, with 10 sigma levels in the vertical conforming to a realistic bottom topography. Open boundary conditions are determined by using radiation conditions, and the sea surface elevation is prescribed from the archiving, validation and interpretation of satellite oceanographic data (AVISO). The initial condition is determined from the spin up phase of the first model run, which was executed by using wind stress calculated from climatological monthly mean wind, restoring-type surface heat and salt and climatological monthly mean freshwater flux. The model was run in spin up phase until an ocean model reached an equilibrium state under the applied force. A spatially variable wind field taken from the European Centre for Medium-Range Weather Forecasts (ECMWF) is used to compute the wind stress directly from the velocity fluctuations. Comparison of tendency between the sea surface elevations from model and the observed significant wave heights of moored buoys in the Gulf of Thailand under Seawatch project is investigated. The model predicts the sea level elevation up to 68.5 cm at the Cha-Am area located in the north of where the typhoon strands to the shore. Results of sea level elevation show that there is an area of peak set-up in the upper gulf, particularly in the western coast, and the effects of the storm surge are small at the lower gulf. During the entire period of this study, the surge in the gulf was induced by the northeasterly wind blowing over it.     

A novel group ranking model for revealing sequence and quantity knowledge

The aggregation of individuals’ preferences into a consensus ranking is a group ranking problem which has been widely utilized in various applications, such as decision support systems, recommendation systems, and voting systems. Gathering the comparison of preferences and aggregate them to gain consensuses is a conventional issue. For example, b > c ? d ? a indicates that b is favorable to c, and c (d) is somewhat favorable but not fully favorable to d (a), where > and ? are comparators, and a, b, c, and d are items. Recently, a new type of ranking model was proposed to provide temporal orders of items. The order, b&c → a, means that b and c can occur simultaneously and are also before a. Although this model can derive the order ranking of items, the knowledge about quantity-related items is also of importance to approach more real-life circumstances. For example, when enterprises or individuals handle their portfolios in financial management, two considerations, the sequences and the amount of money for investment objects, should be raised initially. In this study, we propose a model for discovering consensus sequential patterns with quantitative linguistic terms. Experiments using synthetic and real datasets showed the model’s computational efficiency, scalability, and effectiveness.     

Validation and verification of social processes within agent-based computational organization models

The use of simulation modeling in computational analysis of organizations is becoming a prominent approach in social science research. However, relying on simulations to gain intuition about social phenomena has significant implications. While simulations may give rise to interesting macro-level phenomena, and sometimes even mimic empirical data, the underlying micro and macro level processes may be far from realistic. Yet, this realism may be important to infer results that are relevant to existing theories of social systems and to policy making. Therefore, it is important to assess not only predictive capability but also explanation accuracy of formal models in terms of the degree of realism reflected by the embedded processes. This paper presents a process-centric perspective for the validation and verification (V&V) of agent-based computational organization models. Following an overview of the role of V&V within the life cycle of a simulation study, emergent issues in agent-based organization model V&V are outlined. The notion of social contract that facilitates capturing micro level processes among agents is introduced to enable reasoning about the integrity and consistency of agent-based organization designs. Social contracts are shown to enable modular compositional verification of interaction dynamics among peer agents. Two types of consistency are introduced: horizontal and vertical consistency. It is argued that such local consistency analysis is necessary, but insufficient to validate emergent macro processes within multi-agent organizations. As such, new formal validation metrics are introduced to substantiate the operational validity of emergent macro-level behavior. Levent Yilmaz is Assistant Professor of Computer Science and Engineering in the College of Engineering at Auburn University and co-founder of the Auburn Modeling and Simulation Laboratory of the M&SNet. Dr. Yilmaz received his Ph.D. and M.S. degrees from Virginia Polytechnic Institute and State University (Virginia Tech). His research interests are on advancing the theory and methodology of simulation modeling, agent-directed simulation (to explore dynamics of socio-technical systems, organizations, and human/team behavior), and education in simulation modeling. Dr. Yilmaz is a member of ACM, IEEE Computer Society, Society for Computer Simulation International, and Upsilon Pi Epsilon. URL: http://www.eng.auburn.edu/~yilmaz