Simulation Modeling and Analysis

By Fourth Edition, Averill M. Law. 1982

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In Mathematical Optimization

Citations in the corpus

Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information (2018, methodology): “We refer to, for example, Asmussen and Glynn (2010) and Law (2014) for further details on data sampling and simulations.”
Accelerated gradient methods for nonconvex nonlinear and stochastic programming (2013, neutral): “, in simulation-based optimization [1,2,9,19].”
Process Flexibility in Supply Chains (2003, background): “The 95% confidence intervals for the expected shortfall estimates were calculated (Law and Kelton 1991) and found to be within ±3% of the estimates.”
Control variates for estimation based on reversible Markov chain Monte Carlo samplers (2012, background): “This issue has been studied extensively in the classical context of estimation based on independent identically distributed samples; see, for example, Lavenberg and Welch (1981), Law and Kelton (1982), Nelson (2004) and Glasserman (2004), pages 200–202.”
Conceptual modelling for simulation Part I: definition and requirements (2008, background): “Others have produced similar sets of principles (or guidelines), for instance, Morris (1967), Musselman (1992), Powell (1995), Pritsker (1998) and Law and Kelton (2000).”
Optimization for Simulation : Theory vs . Practice (2002, methodology): “In addition to its honored place in queueing theory, this model is often the first system used in textbooks to illustrate discrete-event simulation (e.g., Law and Kelton 2000).”
The application of discrete event simulation and system dynamics in the logistics and supply chain context (2012, background): “Furthermore, decisions made within LSCM involve different levels of the DSS hierarchy: operational, tactical and strategic [83, 132].”
Random Number Generation (2008, methodology): “…proposed various ways of combining RNGs to produce streams of random numbers with less regularity and better “randomness” properties; see, e.g., Collings (1987), Knuth (1998), Gentle (2003), Law and Kelton (2000), L’Ecuyer (1994), Fishman (1996), Marsaglia (1985), and other references given there.”
Selecting the best system when systems are revealed sequentially (2007, methodology): “When simulation observations are obtained within a single replication of a steady-state simulation, then techniques such as batch means allow assumption 1 to hold approximately (see, for instance, Law and Kelton (2000)).”
Stochastic root finding via retrospective approximation (2001, background): “The concepts necessary to develop such simulation procedures are discussed in standard textbooks, for example Law and Kelton (2000) and Banks et al. (1996).”
Performance Modeling and Design of Computer Systems: Queueing Theory in Action (2013, methodology): “It looks like we should be able to apply the Accept-Reject method, but it is hard to find the right distribution to match up to (for more discussion see [116], p.”
An Object-Oriented Random-Number Package with Many Long Streams and Substreams (2002, background): “…good statistical properties, are often required in simulation studies, for instance, to make independent replications or to associate distinct " streams " of random numbers with different sources of randomness in the system to facilitate synchronization for variance reduction (Law and Kelton 2000).”
Quantum random number generators (2016, background): “Thus, random number generation is needed in simulations in engineering, network, manufacturing, business and computer science problems (Bratley et al., 1987; Fishman, 1978; Law and Kelton, 2000).”
Grand Challenges in Modeling and Simulation of Complex Manufacturing Systems (2004, background): “Each of them will be discussed below, with the grandest challenge discussed first.”
Agent-based modeling and simulation (2009, unknown): “5 WHY AND WHEN ABMS We conclude by offering some ideas on the situations for which agent-based modeling can offer distinct advantages to con-ventional simulation approaches such as discrete event simulation (Law 2007), systems dynamics (Sterman 2000) and other quantitative modeling techniques.”
Digital twin paradigm: A systematic literature review (2021, background): “digital twin aims to integrate data across different domains into virtual models (Kusiak, 2018).”
Verification and validation of simulation models (2005, unknown): “The standard statistical techniques and data collection methods used in simulation output analysis (Banks et al. 2000, Law and Kelton 2000) could be used for developing the model range of accuracy, e.g., the methods of replication and (nonoverlapping) batch means.”