View source: R/criticalpath_methodsAOE.R
solve_pathAOA | R Documentation |
Finds a solution using CPM and PERT methods. Relationships between activities can be given as a list of predecessors or start and end node numbers.
solve_pathAOA(
input_data,
deterministic = TRUE,
predecessors = FALSE,
pert_param = 0
)
input_data |
Data frame containing the structure of the graph and the duration of the activity. For the CPM method and start/end nodes you need 4 columns (the order is important, not the name of the column):
For the CPM method and predecessors list you need 3 columns (the order is important, not the name of the column):
For the PERT method and start/end nodes you need 6 columns (the order is important, not the name of the column):
For the PERT method and predecessors list you need 5 columns (the order is important, not the name of the column):
|
deterministic |
A logical parameter specifying the solution method.
If set to |
predecessors |
TRUE if the user data contains a list of immediately preceding activities
If set to |
pert_param |
A parameter that controls the method of calculating the expected value and variance in the PERT method. 0 - classic formula (default), 1 - 1st and 99th percentile of the beta distribution, 2 - 5th and 95th percentile of the beta distribution, 3 - 5th and 95th percentiles of the beta distribution with modification by (Perry and Greig, 1975), 4 - Extended Pearson's and Tukey's formula (Pearson and Tukey, 1965), 5 - Golenko-Ginzburg's full formula (Golenko-Ginzburg, 1988), 6 - Golenko-Ginzburg's reduced formula (Golenko-Ginzburg, 1988), 7 - Farnum's and Stanton's formula (Farnum and Stanton, 1987). |
The list is made of a graph, schedule and selected partial results.
x <- solve_pathAOA(cpmexample1, deterministic = TRUE)
y <- solve_pathAOA(pertexample1, deterministic = FALSE)
x <- solve_pathAOA(cpmexample2, deterministic = TRUE, predecessors = TRUE)
y <- solve_pathAOA(pertexample2, deterministic = FALSE, predecessors = TRUE)
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