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显示样式: 排序: IF: - GO 导出
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Fixed-Confidence, Fixed-Tolerance Guarantees for Ranking-and-Selection Procedures ACM Trans. Model. Comput. Simul. (IF 1.38) Pub Date : 2021-02-10 David J. Eckman; Shane G. Henderson
Ever since the conception of the statistical ranking-and-selection (R8S) problem, a predominant approach has been the indifference-zone (IZ) formulation. Under the IZ formulation, R8S procedures are designed to provide a guarantee on the probability of correct selection (PCS) whenever the performance of the best system exceeds that of the second-best system by a specified amount. We discuss the shortcomings
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Fluid Approximation–based Analysis for Mode-switching Population Dynamics ACM Trans. Model. Comput. Simul. (IF 1.38) Pub Date : 2021-02-10 Paul Piho; Jane Hillston
Fluid approximation results provide powerful methods for scalable analysis of models of population dynamics with large numbers of discrete states and have seen wide-ranging applications in modelling biological and computer-based systems and model checking. However, the applicability of these methods relies on assumptions that are not easily met in a number of modelling scenarios. This article focuses
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Green Simulation with Database Monte Carlo ACM Trans. Model. Comput. Simul. (IF 1.38) Pub Date : 2021-01-21 Mingbin Feng; Jeremy Staum
In a setting in which experiments are performed repeatedly with the same simulation model, green simulation means reusing outputs from previous experiments to answer the question currently being asked of the model. In this article, we address the setting in which experiments are run to answer questions quickly, with a time limit providing a fixed computational budget, and then idle time is available
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Novel Approaches to Feasibility Determination ACM Trans. Model. Comput. Simul. (IF 1.38) Pub Date : 2021-01-08 Daniel Solow; Roberto Szechtman; Enver Yücesan
This article proposes two-stage Bayesian and frequentist procedures for determining whether a number of systems—each characterized by the same number of performance measures—belongs to a set Γ defined by a finite collection of linear inequalities. A system is “in (not in) Γ” if the vector of the means is in (not in) Γ, where the means must be estimated using Monte Carlo simulation. We develop algorithms
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Global-local Metamodel-assisted Stochastic Programming via Simulation ACM Trans. Model. Comput. Simul. (IF 1.38) Pub Date : 2020-12-31 Wei Xie; Yuan Yi; Hua Zheng
To integrate strategic, tactical, and operational decisions, stochastic programming has been widely used to guide dynamic decision-making. In this article, we consider complex systems and introduce the global-local metamodel-assisted stochastic programming via simulation that can efficiently employ the simulation resource to iteratively solve for the optimal first- and second-stage decisions. Specifically
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Application of Simulation in Healthcare Service Operations: A Review and Research Agenda ACM Trans. Model. Comput. Simul. (IF 1.38) Pub Date : 2020-12-31 Sudipendra Nath Roy; Bhavin J. Shah; Hasmukh Gajjar
The health system is intricate due to its dynamic nature and critical service requirements. The involvement of multiple layers of health service providers quadrupled this complexity and results in a complicated operating environment. Simulation is often considered an apt technique to model and study complex systems in the literature. The popularity of simulation in the healthcare domain had only accelerated
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Replication of Computational Results Report for “Green Simulation with Database Monte Carlo” ACM Trans. Model. Comput. Simul. (IF 1.38) Pub Date : 2020-12-31 Alessandro Pellegrini
This article presents the reproducibility results associated with the article “Green Simulation with Database Monte Carlo,” by Mingbin Feng and Jeremy Staum. The authors have uploaded their artifact to Zenodo, which ensures a long-term retention of the artifact. The artifact, which is based on a set of R scripts, allows to easily regenerate data for the figures and the tables, it completes successfully
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Discrete-Event Modeling and Simulation of Diffusion Processes in Multiplex Networks ACM Trans. Model. Comput. Simul. (IF 1.38) Pub Date : 2020-12-31 Cristina Ruiz-Martin; Gabriel Wainer; Adolfo Lopez-Paredes
A variety of phenomena (such as the spread of diseases, pollution in rivers, etc.) can be studied as diffusion processes over networks (i.e., the diffusion of the phenomenon over a set of interconnected entities). This research introduces a method to study such diffusion processes in multiplex dynamic networks. We use a formal Modeling and Simulation methodology (in our case, DEVS, Discrete-Event System
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Parametric Scenario Optimization under Limited Data: A Distributionally Robust Optimization View ACM Trans. Model. Comput. Simul. (IF 1.38) Pub Date : 2020-11-23 Henry Lam; Fengpei Li
We consider optimization problems with uncertain constraints that need to be satisfied probabilistically. When data are available, a common method to obtain feasible solutions for such problems is to impose sampled constraints following the so-called scenario optimization approach. However, when the data size is small, the sampled constraints may not statistically support a feasibility guarantee on
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Data Farming: Methods for the Present, Opportunities for the Future ACM Trans. Model. Comput. Simul. (IF 1.38) Pub Date : 2020-11-23 Susan M. Sanchez
Data farming is a descriptive metaphor that captures the notion of generating data purposefully to maximize the information “yield” from simulation models. Large-scale designed experiments let us grow the simulation output efficiently and effectively. We can explore massive input spaces, uncover interesting features of complex simulation response surfaces, and explicitly identify cause-and-effect relationships
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Hierarchical Gaussian Process Models for Improved Metamodeling ACM Trans. Model. Comput. Simul. (IF 1.38) Pub Date : 2020-11-23 Nicolas Knudde; Vincent Dutordoir; Joachim Van Der Herten; Ivo Couckuyt; Tom Dhaene
Simulations are often used for the design of complex systems as they allow one to explore the design space without the need to build several prototypes. Over the years, the simulation accuracy, as well as the associated computational cost, has increased significantly, limiting the overall number of simulations during the design process. Therefore, metamodeling aims to approximate the simulation response
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Knowledge Discovery in Simulation Data ACM Trans. Model. Comput. Simul. (IF 1.38) Pub Date : 2020-11-23 Niclas Feldkamp; Soeren Bergmann; Steffen Strassburger
This article provides a comprehensive and in-depth overview of our work on knowledge discovery in simulations. Application-wise, we focus on manufacturing simulations. Specifically, we propose and discuss a methodology for designing, executing, and analyzing large-scale simulation experiments with a broad coverage of possible system behavior targeted at generating knowledge about the system. Based
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Dimensionally Aware Multi-Objective Genetic Programming for Automatic Crowd Behavior Modeling ACM Trans. Model. Comput. Simul. (IF 1.38) Pub Date : 2020-07-05 D. Li; J. Zhong
One limitation of current data-driven automatic crowd modeling methods is that the models generated have low interpretability, which limits the practical applications of the models. In this article, we propose a new data-driven crowd modeling approach that can generate universal behavior rules with better interpretability. Higher interpretability helps people better understand and analyze the rules
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Modeling Resources to Simulate Business Process Reliability ACM Trans. Model. Comput. Simul. (IF 1.38) Pub Date : 2020-05-31 Paolo Bocciarelli; Andrea D’Ambrogio; Andrea Giglio; Emiliano Paglia
The combination of process modeling and simulation-based analysis provides a quantitative approach to analyze business processes, and to evaluate design alternatives before committing the required resources, to properly align operations with business strategies, improve operational efficiency, and gain competitive advantage. However, the use of simulation-based analysis is still limited in practice
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ChunkedTejas: A Chunking-based Approach to Parallelizing a Trace-Driven Architectural Simulator ACM Trans. Model. Comput. Simul. (IF 1.38) Pub Date : 2020-05-31 Rajshekar Kalayappan; Avantika Chhabra; Smruti R. Sarangi
Research in computer architecture is commonly done using software simulators. The simulation speed of such simulators is therefore critical to the rate of progress in research. One of the less commonly used ways to increase the simulation speed is to decompose the benchmark’s execution into contiguous chunks of instructions and simulate these chunks in parallel. Two issues arise from this approach
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Toward a Theory of Superdense Time in Simulation Models ACM Trans. Model. Comput. Simul. (IF 1.38) Pub Date : 2020-05-31 James Nutaro
We develop a theory of superdense time that encompasses existing uses of superdense time in discrete event simulations and points to new forms that have not previously been explored. A central feature of our development is a set of axioms for superdense time. The sufficiency of these axioms is demonstrated by using them to prove that a general model of a discrete event simulation procedure, expressed
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Exact Simulation of a Truncated Lévy Subordinator ACM Trans. Model. Comput. Simul. (IF 1.38) Pub Date : 2020-05-31 Angelos Dassios; Jia Wei Lim; Yan Qu
A truncated Lévy subordinator is a Lévy subordinator in R+ with Lévy measure restricted from above by a certain level b. In this article, we study the path and distribution properties of this type of process in detail and set up an exact simulation framework based on a marked renewal process. In particular, we focus on a typical specification of truncated Lévy subordinator, namely the truncated stable
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Inhomogeneous CTMC Birth-and-Death Models Solved by Uniformization with Steady-State Detection ACM Trans. Model. Comput. Simul. (IF 1.38) Pub Date : 2020-05-31 Maciej Rafal Burak; Przemyslaw Korytkowski
Time-inhomogeneous queueing models play an important role in service systems modeling. Although the transient solutions of corresponding continuous-time Markov chains (CTMCs) are more precise than methods using stationary approximations, most authors consider their computational costs prohibitive for practical application. This article presents a new variant of the uniformization algorithm that utilizes
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Multithreaded Stochastic PDES for Reactions and Diffusions in Neurons. ACM Trans. Model. Comput. Simul. (IF 1.38) Pub Date : 2017-09-26 Zhongwei Lin,Carl Tropper,Robert A Mcdougal,Mohammand Nazrul Ishlam Patoary,William W Lytton,Yiping Yao,Michael L Hines
Cells exhibit stochastic behavior when the number of molecules is small. Hence a stochastic reaction-diffusion simulator capable of working at scale can provide a more accurate view of molecular dynamics within the cell. This paper describes a parallel discrete event simulator, Neuron Time Warp-Multi Thread (NTW-MT), developed for the simulation of reaction diffusion models of neurons. To the best