Statistical tools for simulation practitioners
Statistical tools for simulation practitioners
An experimental procedure for simulation response surface model identification
Communications of the ACM
Discrete event simulations and parallel processing: statistical properties
SIAM Journal on Scientific and Statistical Computing
Monotonicity in generalized semi-Markov processes
Mathematics of Operations Research
Ranking, selection and multiple comparisons in computer simulations
WSC '94 Proceedings of the 26th conference on Winter simulation
Retrospective simulation response optimization
WSC '91 Proceedings of the 23rd conference on Winter simulation
ACM Computing Surveys (CSUR)
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Simulation optimization: methods and applications
Proceedings of the 29th conference on Winter simulation
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Sensitivity analysis and optimization within stochastic discrete event simulation require the ability to rapidly estimate performance measures under different parameter values. One technique, termed "rapid learning", aims at enumerating all possible sample paths under different parameter values of the model based on the observed sample path under the nominal parameter value. There are two necessary conditions for this capability: observability, which asserts that every state observed in the nominal path is always richer in terms of feasible events than the states observed in the constructed paths, and constructability, which, in addition to observability, requires that the lifetime of an event has the same distribution as its residual life. This paper asserts that the verification of the observability condition is an NP-hard search problem. This result, in turn, implies that it is algorithmically not possible to find parameter values satisfying observability; hence, it encourages the development of heuristic procedures. Further implications are also discussed.