solve_pathAOA {critpath}

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.

Description

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.

Usage

solve_pathAOA(
  input_data,
  deterministic = TRUE,
  predecessors = FALSE,
  pert_param = 0
)

Arguments

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):

1. from The number of the node where the activity starts.

2. to The number of the node where the activity ends.

3. label Activity labels.

4. time Activities durations.

For the CPM method and predecessors list you need 3 columns (the order is important, not the name of the column):

1. label Activity labels.

2. pred List of predecessors.

3. time Activities durations.

For the PERT method and start/end nodes you need 6 columns (the order is important, not the name of the column):

1. from The number of the node where the activity starts.

2. to The number of the node where the activity ends.

3. label Activity labels.

4. opt_time Optimistic duration of activities.

5. likely_time The most likely duration of the activity.

6. pes_time Pessimistic duration of activities.

For the PERT method and predecessors list you need 5 columns (the order is important, not the name of the column):

1. label Activity labels.

2. pred List of predecessors.

3. opt_time Optimistic duration of activities.

4. likely_time The most likely duration of the activity.

5. pes_time Pessimistic duration of activities.

deterministic

A logical parameter specifying the solution method. If set to TRUE (default), the CPM method is used. If is set to FALSE, the PERT method is used.

predecessors

TRUE if the user data contains a list of immediately preceding activities If set to FALSE (default), start nad end nodes are used. If is set to TRUE, predecessors list is used.

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).

Value

The list is made of a graph, schedule and selected partial results.

Examples

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)