Transaction tagging in highly congested queueing simulations
Queueing Systems: Theory and Applications
Operations Research
Structural and behavioral equivalence of simulation models
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Transforming Petri nets into event graph models
WSC '94 Proceedings of the 26th conference on Winter simulation
The interface between simulation output analysis research and practice
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Eliminating event cancellation in discrete event simulation
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Mathematical programming models of discrete event system dynamics
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Data Structures and Algorithms
Data Structures and Algorithms
Ranking and Selection for Steady-State Simulation: Procedures and Perspectives
INFORMS Journal on Computing
GPSS - finding the appropriate world-view
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Stochastic Petri Nets: Modelling, Stability, Simulation
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Information models for queueing system simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Generalized Lindley-type recursive representations for multiserver tandem queues with blocking
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Winter Simulation Conference
Bayesian Kriging Analysis and Design for Stochastic Simulations
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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This article explores possibilities for designing and executing simulation models with specific analysis goals in mind, and shows that a tight coupling of the modeling and analysis phases in a simulation project can lead to dramatic improvements in the study results. Suggestions are made for how simulation analysis, considered in the explicit context of discrete-event simulation models, can create new opportunities for meaningful research and more efficient modeling. Modeling decisions can play a significant role in the performance of analytical procedures. How a simulation model is designed can enable, inhibit, or even invalidate analytical procedures and methodology research results.