Approximating the distribution functions in stochastic networks

DOI10.1016/0305-0548(85)90024-3zbMath0606.90047OpenAlexW2008533147MaRDI QIDQ1085043

Bajis Dodin

Publication date: 1985

Published in: Computers \& Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0305-0548(85)90024-3

zbMATH Keywords

stochastic networkscomputational experiencesapproximating procedures

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Deterministic network models in operations research (90B10)

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The most critical path in a PERT network: A heuristic approach ⋮ A new heuristic for resource-constrained project scheduling in stochastic networks using critical chain concept ⋮ A new analytical algorithm and generation of Gaussian quadrature formula for stochastic network ⋮ Adjustment of the moments of the project completion times when activity times are exponentially distributed ⋮ Mathematical theory of queueing networks ⋮ A new approach to determine the critical path in stochastic activity network ⋮ A new approximation algorithm for obtaining the probability distribution function for project completion time ⋮ Stochastic networks and the extreme value distribution ⋮ Path critical index and activity critical index in PERT networks ⋮ A new structural mechanism for reducibility of stochastic PERT networks

Uses Software

• IMSL Numerical Libraries

Cites Work

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• The use of cutsets in Monte Carlo analysis of stochastic networks
• Topology of series-parallel networks
• On the Expected Duration of PERT Type Networks
• Conditional Monte Carlo: A Simulation Technique for Stochastic Network Analysis
• Bounding Distributions for a Stochastic Acyclic Network

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