Open AccessBook
Simulation Modeling and Analysis
Averill M. Law,W. David Kelton +1 more
TLDR
The text is designed for a one-term or two-quarter course in simulation offered in departments of industrial engineering, business, computer science and operations research.Abstract:
From the Publisher:
This second edition of Simulation Modeling and Analysis includes a chapter on "Simulation in Manufacturing Systems" and examples. The text is designed for a one-term or two-quarter course in simulation offered in departments of industrial engineering,business,computer science and operations research.read more
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Stochastic root finding via retrospective approximation
Huifen Chen,Bruce W. Schmeiser +1 more
TL;DR: In this paper, the authors introduce the family of retrospective approximation (RA) algorithms, which solves a sequence of sample-path equations that are based on increasing Monte Carlo sample sizes.
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Performance of an ant colony optimisation algorithm in dynamic job shop scheduling problems
TL;DR: In this paper, the authors identify appropriate application domains of ant colony optimization (ACO) in the area of dynamic job shop scheduling problem and test ACO in a shop floor scenario with three levels of machine utilisations, three different processing time distributions, and three different performance measures for intermediate scheduling problems.
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Performance comparison of battery power consumption in wireless multiple access protocols
TL;DR: The analysis here shows that protocols that aim to reduce the number of contentions perform better from an energy consumption perspective, although the receiver usage time, however, tends to be higher for protocols that require the mobile to sense the medium before attempting transmission.
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Empirical comparison of search algorithms for discrete event simulation
TL;DR: This paper compares the Hooke–Jeeves pattern search, Nelder–Mead simplex, simulated annealing, and genetic algorithm optimization algorithms on variations of four industrial case study simulation problems.
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Neural network as a simulation metamodel in economic analysis of risky projects
TL;DR: An artificial neural network (ANN) model for economic analysis of risky projects is presented and it is indicated that more predictive capability can be achieved by coupling conventional simulation with neural network approaches.