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

After a Funeral, Before a Test; and Other Stories

After a Funeral, Before a Test; and Other Stories is a collection of nine fictional short stories. Their focus is diverse in regard to multiple aspects of creative fiction: subject matter, theme, style, setting and characters. Despite the array of material, one common method was to provide narration that would invite readers to make their own interpretation rather than to present overt, didactic stories. This narrative strategy was accomplished by using fictional concepts of setting, the objective correlative, and literary minimalism. Other elements include surrealism, Hemingway’s “iceberg effect,” and psychologically complex narrators. Literary influences include F. Scott Fitzgerald’s The Great Gatsby, Ernest Hemingway’s “Big Two-Hearted River,” Anton Chekhov’s “Misery” and Raymond Carver’s “Neighbors.” Theoretical influences are drawn from Suzanne Ferguson’s essay “Defining the Short Story: Impressionism and Form,” Charles E. May’s essay “Chekhov and the Modern Short Story,” and Cynthia Hallett’s book Minimalism and the Short Story

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

Shrinkage Cracking in Concrete Tilt-Up Construction

The purpose of this project is to investigate a particular, undesirable cracking pattern in concrete tilt-up panels that, until now, did not have a known definite cause. The cause of this cracking pattern is hypothesized to be due to shrinkage restraint of the concrete panels. The cracking under investigation occurs at the bottom corners of the Tilt-Up panels, suggesting that the base of the panel is restrained from shrinkage. This project models various components of Tilt-Up Construction that have potential for restraining the panels from shrinking. This project consists of the following main components. The first aspect of this project was to investigate and become familiar with the means and methods of Tilt-Up Construction. To determine the potential shrinkage restraints on the panels, the connections and details associated with Tilt-Up must be thoroughly understood. This involved reviewing typical details of connections as well as contacting engineers and contractors in the field to determine the typical means and methods of Tilt-Up construction and construction sequencing. Once typical construction practices were understood, the first shrinkage restraint investigated was the friction developed by the panel setting pads. Once the panel is ready to lift, it is set on grout pads or plastic shims, typically located at the ends of the panel. To determine the amount of restraint caused by friction, an experiment was conducted to determine the coefficient of static friction. Tests were run to find the coefficient of friction for concrete against grout, and concrete against plastic shims. The third aspect of this project was to develop an effective computer model of stresses in Tilt-Up panels induced by shrinkage restraint. The goal of this model was to be able to run various scenarios, to determine the effects of panel concrete mix design, panel geometry, and construction sequencing. The last aspect of this project was to collect enough data from the computer model to determine whether or not shrinkage restraint induces enough stress in the panel to initiate cracking, determine when the cracking would occur given construction sequencing, as well as determine if the cracking pattern matches the pattern seen out in the field. Conclusions will have to be made on a case by case basis, but the panel specifications in this analysis were chosen from a Home Depot building in San Luis Obispo, CA, an as-built Tilt-Up project. After running about 70 different cases, it was discovered that the grout pads by themselves did not provide enough shrinkage restraint to initiate cracking in the panel. This led to further investigation of panel connections, specifically the panel to slab connection at the pour strip. This paper concludes that when combining the shrinkage restraint from grout pad friction and pour strip reinforcement tension, there is potential for cracking in the panel. Even further, the cracking pattern determined from the computer model provides nearly an exact match to the actual cracks under investigation and measured in the field. Although this report provides evidence for potential cracking in Tilt-Up panels due to shrinkage restraint, recommendations for limiting the potential of cracking in panels will need to be made on a case by case basis

Methyl Bromide Fumigation for controlling the Texas Leafcutting Ant.

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Model Validation and Testing in Simulation: a Literature Review

Model validation is a key activity undertaken during the model development process in simulation. There is a large body of literature on model validation, albeit there exists little convergence in terms of the definitions, types of validity, and tests used. Yet it is not clear what standards should be taken into consideration to avoid developing what could be considered to be invalid or wrong models. In this paper we examine existing literature on model validation with the view to identifying the existing validation approaches and types of tests used to assess model validity. In this review we focus our attention on three domains that usually overlap in methods and techniques: general Operational Research (OR), Modelling & Simulation (M&S) and Computer Science (CS). We analyze each field to identify the aspects of validity considered including the tests used, the validation approach taken, i.e. the suggested level of validity achieved (if this applies) and the reported outcome. The analysis shows that there are common validation practices used in all three fields as well as new ideas that could be adopted in discrete event simulation. Some main points of concurrence include the lack of universal validation, the continuous need for validation, and, the indispensable need for modelers and users to work closely together during the model validation process. This review provides an initial categorization of literature on model validation which can in turn be used as a basis for future work in investigating how and to what extent models are considered sufficiently valid

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