Package 'critpath'

Title: Setting the Critical Path in Project Management
Description: Solving the problem of project management using CPM (Critical Path Method), PERT (Program Evaluation and Review Technique) and LESS (Least Cost Estimating and Scheduling) methods. The package sets the critical path, schedule and Gantt chart. In addition, it allows to draw a graph even with marked critical activities. For more information about project management see: Taha H. A. "Operations Research. An Introduction" (2017, ISBN:978-1-292-16554-7), Rama Murthy P. "Operations Research" (2007, ISBN:978-81-224-2944-2), Yuval Cohen & Arik Sadeh (2006) "A New Approach for Constructing and Generating AOA Networks", Journal of Engineering, Computing and Architecture 1. 1-13, Konarzewska I., Jewczak M., Kucharski A. (2020, ISBN:978-83-8220-112-3), Miszczyńska D., Miszczyński M. "Wybrane metody badań operacyjnych" (2000, ISBN:83-907712-0-9).
Authors: Adam Kucharski
Maintainer: Adam Kucharski <[email protected]>
License: GPL-2
Version: 0.2.2
Built: 2024-12-05 06:51:18 UTC
Source: CRAN

Help Index

Dataset for the CPM method

Description

Fictitious data that is used in the examples. 6 activities, 5 nodes. In this dataset, the activities occur on the edges.

Usage

cpmexample1

Format

A data frame composed of predetermined columns:

from

starting activity node

to

final activity node

label

activity label

time

duration of the activity

Dataset for the CPM method

Description

Example from Miszczyńska D., Miszczyński M. "Wybrane metody badań operacyjnych" (2000, ISBN:83-907712-0-9). 10 activities, 8 nodes. In this dataset, the activities occur on the edges and a list of direct predecessors has been added.

Usage

cpmexample2

Format

A data frame composed of predetermined columns:

label

activity label

pred

preceding activities

time

duration of the activity

Dataset for the LESS method

Description

Fictitious data that is used in the examples. 6 activities, 5 nodes. In this dataset, the activities occur on the edges

Usage

lessexample1

Format

A data frame composed of predetermined columns:

from

starting activity node

to

final activity node

label

activity label

time

normal duration of the activity

bound_time

the shortest duration of the activity

norm_cost

normal cost of the activity

bound_cost

boundary cost of the activity

Dataset for the LESS method

Description

Example from Miszczyńska D., Miszczyński M. "Wybrane metody badań operacyjnych" (2000, ISBN:83-907712-0-9). In this dataset, the activities occur on the edges and a list of direct predecessors has been added.

Usage

lessexample2

Format

A data frame composed of predetermined columns:

label

activity label

pred

preceding activities

time

normal duration of the activity

bound_time

the shortest duration of the activity

norm_cost

normal cost of the activity

bound_cost

boundary cost of the activity

Probability for the given directive term

Description

Probability for the given directive term

Usage

PERT_newprob(new_DT, yourlist)

Arguments

new_DT

The given project completion date. The parameter must be greater than zero.
yourlist

List of objects that make up the solution to the project management problem.

Value

This function calculates the probability of completing the project within the time specified by the user. A normal distribution was assumed.

Examples

y <- solve_pathAOA(pertexample1, deterministic = FALSE)
PERT_newprob(new_DT = 30, y)

A new directive term for any probability

Description

A new directive term for any probability

Usage

PERT_newtime(new_prob = 0.5, yourlist)

Arguments

new_prob

Probability of the project completion. Default set to 0.5.
yourlist

List of objects that make up the solution to the project management problem.

Value

This function computes a new directive term for a probability given by the user. A normal distribution was assumed.

Examples

y <- solve_pathAOA(pertexample1, deterministic = FALSE)
PERT_newtime(new_prob = 0.3, y)

Dataset for the PERT method

Description

Fictitious data that is used in the examples. 9 activities, 8 nodes. In this dataset, the activities occur on the edges

Usage

pertexample1

Format

A data frame composed of predetermined columns:

from

starting activity node

to

final activity node

label

activity label

opt_time

optimistic duration of activity

likely_time

the most likely duration of the activity

pes_time

pesimistic duration of activity

Dataset for the PERT method

Description

Example from Miszczyńska D., Miszczyński M. "Wybrane metody badań operacyjnych" (2000, ISBN:83-907712-0-9). 10 activities, 8 nodes. In this dataset, the activities occur on the edges and a list of direct predecessors has been added.

Usage

pertexample2

Format

A data frame composed of predetermined columns:

label

activity label

pred

preceding activities

opt_time

optimistic duration of activity

likely_time

the most likely duration of the activity

pes_time

pesimistic duration of activity

An ALAP chart

Description

An ALAP chart

Usage

plot_alap(yourlist, show_dummy = FALSE, bar_size = 10)

Arguments

yourlist

List of objects that make up the solution to the project management problem.
show_dummy

Decides whether dummy activities should be included in the chart. If so, set it to TRUE (set to FALSE by default).
bar_size

Thickness of the bar drawn for activity (set to 10 by default).

Value

Draws an ALAP (activities start and finish As Late As Possible) chart broken down into critical ("CR") and non-critical ("NC") activities. Marks total float.

Examples

x <- solve_pathAOA(cpmexample1, deterministic = TRUE)
plot_alap(x)

An ASAP chart

Description

An ASAP chart

Usage

plot_asap(yourlist, show_dummy = FALSE, bar_size = 10)

Arguments

yourlist

List of objects that make up the solution to the project management problem.
show_dummy

Decides whether dummy activities should be included in the chart. If so, set it to TRUE (set to FALSE by default).
bar_size

Thickness of the bar drawn for activity (set to 10 by default).

Value

Draws an ASAP (activities start and finish As Soon As Possible) chart broken down into critical ("CR") and non-critical ("NC") activities. Marks total floats.

Examples

x <- solve_pathAOA(cpmexample1, deterministic = TRUE)
plot_asap(x)

A Gantt chart

Description

A Gantt chart

Usage

plot_gantt(yourlist, show_dummy = FALSE, bar_size = 10)

Arguments

yourlist

List of objects that make up the solution to the project management problem.
show_dummy

Decides whether dummy activities should be included in the chart. If so, set it to TRUE (set to FALSE by default).
bar_size

Thickness of the bar drawn for activity (set to 10 by default).

Value

Draws a Gantt chart broken down into critical ("CR") and non-critical ("NC") activities. Marks total floats.

Examples

x <- solve_pathAOA(cpmexample1, deterministic = TRUE)
plot_gantt(x)

A graph of connections between nodes

Description

A graph of connections between nodes

Usage

plot_graphAOA(input_data, predecessors = FALSE, solved = NULL, fixed_seed = 23)

Arguments

input_data

Data frame describing the problem.
predecessors

TRUE if the user data contains a list of immediately preceding activities
solved

List of objects that make up the solution to the project management problem.
fixed_seed

Optional parameter setting random seed to user value to get similar looking plots each time the function is run (set to 23 by default).

Value

The function draws a graph showing dependencies between nodes. The "solved" parameter determines whether there is a critical path in the graph. In that case, you must solve the problem first. In the examples below, the function first draws the graph only on the basis of the data frame and then after determining the critical path.

Examples

plot_graphAOA(cpmexample1)
x <- solve_pathAOA(cpmexample1, TRUE)
plot_graphAOA(solved = x)

The cumulative distribution function of the normal distribution

Description

The cumulative distribution function of the normal distribution

Usage

plot_norm(yourlist)

Arguments

yourlist

List of objects making up the solution to the project management problem

Value

Draws a graph of the normal distribution with the expected directive term from the PERT method and the standard deviation for this term. The chart also includes lines indicating the schedules of the risk-taker and the belayer.

Examples

y <- solve_pathAOA(pertexample1, deterministic = FALSE)
plot_norm(y)

Total cost change plot

Description

Total cost change plot

Usage

plot_TC(your_list)

Arguments

your_list

List containing solved problem

Value

Based on the results of the LESS method, a graph of the total cost value of all iterations is created

Examples

z <- solve_lessAOA(lessexample1, 50, 15)
plot_TC(z)

Determines the solution using the LESS method. Relationships between activities can be given as a list of predecessors or start and end node numbers.

Description

Determines the solution using the LESS method. Relationships between activities can be given as a list of predecessors or start and end node numbers.

Usage

solve_lessAOA(input_data, ICconst, ICslope, predecessors = FALSE)

Arguments

input_data

Data frame containing the graph structure and activity durations. For the LESS method and start/end nodes you need 7 columns (the order matters):

  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 Normal duration of activities.

  5. crash_time Crash (the shortest possible) duration of activities.

  6. norm_cost Normal costs.

  7. crash_cost Crash costs.

For the LESS method and predecessors list you need 6 columns (the order matters):

  1. label Activity labels.

  2. pred List of predecessors.

  3. time Normal duration of activities.

  4. crash_time Crash (the shortest possible) duration of activities.

  5. norm_cost Normal costs.

  6. crash_cost Crash costs.

ICconst

Intercept of the indirect cost function.
ICslope

Slope of the indirect cost function.
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.

Value

A list made of a graph and a result set.

Examples

z <-  solve_lessAOA(lessexample1, 50, 15)

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)