## Time-oriented Analysis of Projects Using Stochastic Network Techniques |

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accumulative activation function accumulative activation process activated as soon activations of node activities emanating activities leading acyclic SWN arcs assumption A2a Borel sets chapter condition 3.14 Constructing the system corresponding counting density defined according denote directed graph N=[V,E dominated convergence theorem dummy activities duration entrance characteristic entrance node essential sets evaluation executed exit characteristic expected number finite Furthermore GERT networks given in figure go to step graph theory Hence holds ieP(j ieV-S jeV-S Karlsruhe maxW measurable function MeP(S(j NEUMANN/STEINHARDT 11 node jeV node jeV-{l obtain partial graph partial project predecessor probability space probability theory procedure quantities random variables reachable section 3.3 sequence simple accumulative SWN sinks specified ST exit STEOR network stochastic network stochastic process stochastically independent stochastically weighted network structure of order Suppose SWN of order teF+ temporal analysis terminal events theorem 1.5 tivation topologically ordered transition measure twice-weighted network yields