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Markov Regenerative Stochastic Petri Nets
TLDR
We introduce a new class of stochastic Petri nets that can be analyzed by means of Markov regenerative processes and constitutes a true generalization of all the above classes. Expand
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Markov and Markov-Regenerative pert Networks
TLDR
This paper investigates pert networks with independent and exponentially distributed activity durations with finite-state, absorbing, continuous-time Markov chains with upper triangular generator matrices. Expand
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Dynamic Scheduling of Outpatient Appointments Under Patient No-Shows and Cancellations
TLDR
We find that the open access policy, a recently proposed popular scheduling paradigm that calls for “meeting today's demand today,” can be a reasonable choice when the patient load is relatively low. Expand
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Fluid stochastic Petri nets: Theory, applications, and solution techniques
TLDR
We introduce a new class of fluid stochastic Petri nets in which one or more places can hold fluid rather than discrete tokens. Expand
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Modeling and Analysis of Stochastic Systems
Introduction What in the World Is a Stochastic Process? How to Characterize a Stochastic Process What Do We Do with a Stochastic Process? Discrete-Time Markov Chains: Transient Behavior DefinitionExpand
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FSPNs: Fluid Stochastic Petri Nets
TLDR
In this paper we introduce a new class of stochastic Petri nets in which one or more places can hold fluid rather than discrete tokens. Expand
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A New Class of Multivariate Phase Type Distributions
  • V. Kulkarni
  • Mathematics, Computer Science
  • Oper. Res.
  • 1 May 1989
TLDR
A new class of multivariate phase type distributions (denoted by MPH*) is defined, based upon the total accumulated reward until absorption in a finite state, continuous time Markov chain. Expand
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Retrial queues with server subject to breakdowns and repairs
TLDR
In this paper we consider a single server retrial queue where the server is subject to breakdowns and repairs where the retrial customers behave independently of each other. Expand
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Fluid models for single buffer systems
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Introduction to modeling and analysis of stochastic systems
Introduction.- Discrete-Time Markov Models.- Poisson Processes.- Continuous-Time Markov Models.- Generalized Markov Models.- Queueing Models.- Brownian Motion.
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