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Motivation and Overview
Published in Naim A. Kheir, Systems Modeling and Computer Simulation, 2018
High-level simulation languages are also compiler oriented, similar to high-level programming languages, but are specifically used for simulation applications. Most simulation languages require less programming time; moreover, it is simpler to change a model after it is written. It is also easier to debug such programs. A unique feature of simulation languages is their basic building blocks. Among the earlier simulation languages are MIDAS, DYSAC, DSL, GASP, MIMIC, DYNAMO, GPSS, SIMULA, CSSL (Continuous System Simulation Language), and CSMP. The standard functional blocks in CSMP, for example, include first- and second-order transfer functions in addition to nonlinear functions (dead space, hysteresis, saturation, and so on); see Speckhart and Green, 1976. Other simulation languages include ACSL (Advanced Continuous Simulation Language), DARE-P and DARE-Interactive, C-SIMSCRIPT and SIMSCRIPT II-5, Ada, GASP IV, SDL, SIMNON, SLAM, and SIMAN. For a summary of the features of 15 (widely used) simulation languages, the reader may consult Cellier (1983); for a recent simulation software listing, see the SCS Directory (1994). Sargent suggests a list of factors to be considered in selecting languages for a specific problem or for an organization (1978).
Simulation Analysis
Published in Paul J. Fortier, George R. Desrochers, Modeling and Analysis of Local Area Networks, 1990
Paul J. Fortier, George R. Desrochers
The problem with all three techniques is that in order to use them, a modeler must formulate the problem in terms of the available structure of the technique. It cannot be formulated in a natural way and then translated easily. The burden of fitting it into a framework lies on the modeler and the simulation language. The solution is to provide a combined language that has the features of all three techniques. In such a language the modeler can build simulations in a top-down fashion, leaving details to latter levels. For instance, in our early bank teller system, we could initially model it as a single queue with n servers (tellers). The queuing discipline is first come, first served and the service discipline can be any simple distribution such as exponential. This simple model will provide us with a sanity check of the correctness of our model and with bounds to quickly determine the system’s limits. We could next decide to model the teller’s service in greater detail by dropping this component’s level down to the event modeling level.
Modeling and Simulation Concepts
Published in Gabriel A. Wainer, Discrete-Event Modeling and Simulation, 2017
In the 1960s, techniques for discrete-event simulation (based on the ideas just discussed) became very popular. In many cases, this resulted in the definition of advanced simulation languages like SLAM, Arena, Simula, or SimScript [9–11]. Although simulation languages can address complex problems, their use lacks the formality of previously existing modeling methods. Using a simulation language helps with problem solving and experimentation, but in most cases their foundation is not rigorous, making the resulting simulation software difficult to test, maintain, and verify. Likewise, changes in the language can produce serious effects in existing models because their semantics are usually not formally defined. Simulation languages do not provide a method abstract enough to think about the problems to solve or to prove properties of the entities under study, which could improve the final quality of the analysis while reducing end costs. Another problem faced by simulation language–based solutions is more subtle and complex to address. The source of many of these problems can be experienced by solving Exercise 1.2 or 1.5, and it is summarized in Figure 1.8: as most simulation languages were not derived from a formal modeling framework, the modeling phase, actually skipped. We start by collecting data from experiments, and we build a piece of software (the simulator), trying to mimic the problem under study (skipping the intermediate modeling phase). Although this method is still useful in many cases, the result is a single-use program approach, which can have several problems (we will use Example 1.2 to discuss some of these problems):
A Physiological-Based Pharmacokinetic Model For The Broad Spectrum Antimicrobial Zinc Pyrithione: II. Dermal Absorption And Dosimetry In The Rat
Published in Journal of Toxicology and Environmental Health, Part A, 2021
Gary L. Diamond, Nicholas P. Skoulis, A. Robert Jeffcoat, J Frank Nash
Data used in this analysis were processed in Excel (v. 2016, Microsoft). Noncompartmental pharmacokinetic analyses were conducted using WinNonlin (v. 4.01; Pharsight, Palo Alto, CA). The PBPK model was constructed and implemented in Advanced Continuous Simulation Language (acslX, v. 3.4.1.2). Parameter estimation was performed in acslX using the Nelder-Mead and/or conjugate gradient algorithms, set to minimize relative error for each parameter. Dose–response modeling was performed using SAS/STAT software, version 9.3 of the SAS System for Windows (SAS) and Benchmark Dose Software 2.7. (Environmental Protection Agency, 2018, https://www.epa.gov/bmds). Preliminary dose–response modeling relied on logistic regression modeling (SAS PROC LOGISTIC). These preliminary analyses were used to evaluate and identify internal dose metrics estimated from the PBPK model for further analysis and estimation of BMD10 and BMDL10, using multiple models available in the EPA BMDS. Models that did not satisfy a chi-square goodness of fit (p < .1) were rejected from further consideration. Models that met the goodness of fit criteria were evaluated further with the Akaike Information Criterion and range of BMDL10 estimates.
Operational mine planning in block cave mining: a simulation-optimisation approach
Published in International Journal of Mining, Reclamation and Environment, 2021
Shahrokh Paravarzar, Hooman Askari-Nasab, Yashar Pourrahimian, Xavier Emery
Hunt (1994) [8] simulated a molybdenum mine to optimise the operation parameters considering the capacity of train, trunk and ore passes. The SLAMSYSTEM simulation language was used to simulate the mining environment. The mining system in this model was presented as an LHD-Ore pass-Truck-Crusher (LOTC) system. In this system, the materials are dumped by the LHD to the ore pass and then hauled by train to the crusher. The recorded parameters obtained from simulation model for future analysis were train wait, load wait and crusher queue time. Other statistics such as the number of buckets dumped by the LHD, train cycle time, exit and entrance time were registered during the simulation. The results were presented for three months in three resolutions: day, working day in a month and monthly. The results were solved for three different assumed conditions. Based on the presented mining process, the simulation results suggested decreasing the number of LHD and increasing the number of trains for the operation.
Personal reflections … on over 50 years in computer simulation
Published in International Journal of Parallel, Emergent and Distributed Systems, 2020
In 1979 I am invited to present a position paper at the Sorrento Workshop on International Standardization of Simulation Languages in Sorrento (actually held in St. Agata), Italy. My paper, entitled ‘To Be or Not to Be: Is That the Question’ postulates that the existence of a lingua franca for continuous simulation renders a consensus on a standard much less challenging than that for discrete-event simulation. I meet Harry Markowitz, the creator of the Simscritpt family of languages and a future Nobel laureate in Economics. Among the five Americans, Harry and I are the only two with a focus on discrete-event SPLs. Of course, Stephen Mathewson shares that bias. Among the notable attendees with predominant interest in continuous simulation are Joseph S. Gauthier (Advanced Continuous Simulation Language, ACSL), Hilding Elmqvist, and Peter R. Benyon. Another group has principal interests in combined languages: Fancois E. Cellier, Dirk L. Ketternis, and R. W. Sierenberg. The organisers of the workshop, and a number of similar venues to follow in the next decade, comprise yet another group, exemplifying a catholic approach to the language standardisation challenge; e.g. Tuncer I. Ören, Maurice S. Elzas, and Bernard P. Zeigler. The 29 participants from 14 countries share ideas, voice opinions, and venture speculations. No productive standards creation effort ensues.