Activity Networks: Project Planning and Control by Network Models |
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Page 18
... activity may be started , and ( c ) how far can an activity be delayed with no delay in the total duration of the project ... durations . The reader will notice that we limit ourselves in the present chapter to questions of analysis ( as ...
... activity may be started , and ( c ) how far can an activity be delayed with no delay in the total duration of the project ... durations . The reader will notice that we limit ourselves in the present chapter to questions of analysis ( as ...
Page 63
... activity durations lead immediately to the following theorem , which characterizes the node realization times as well as the activity durations . Theorem 2.1 : In an optimal schedule , activities either do or do not possess float .
... activity durations lead immediately to the following theorem , which characterizes the node realization times as well as the activity durations . Theorem 2.1 : In an optimal schedule , activities either do or do not possess float .
Page 91
... durations of all activities are completely specified by specifying the duration of the arcs of any arbores- cence . Lemma 2.2 : Under the cost structure of Eq . 2.19 , the ... activity durations were assumed Strictly Convex Cost Function 91.
... durations of all activities are completely specified by specifying the duration of the arcs of any arbores- cence . Lemma 2.2 : Under the cost structure of Eq . 2.19 , the ... activity durations were assumed Strictly Convex Cost Function 91.
Contents
2 TEMPORAL CONSIDERATIONS | 18 |
3 COST CONSIDERATIONS | 31 |
DIGRAPHS AND LINE DIGRAPHS 39 | 39 |
Copyright | |
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Common terms and phrases
activity durations activity networks adjacency matrix algorithm allocation analysis analytical application approach approximation arc durations assume assumption Beta beta distribution Chapter completion computing Consider constraints construction cost function critical path critical path method defined denote determine discussion distributed dual equal equations equivalent evaluation example exclusive-or expected duration expected value feasible Figure flow GERT given Hence heuristic kâ kâ labeled Laplace transform line digraph linear lower bound Management Sci Mason's rule minimize Monte Carlo network of Fig Normally distributed obtained Oper optimal optimum parameters PERT estimate PERT model possible precedence primal probability problem procedure project duration random random variables reader reduce representation result sample schedule Section Semi-Markov Processes SF lag shown in Fig slack solution specified subnetwork subset Table tasks terminal node Theorem tion variables variance vector Yâ yields z-transform