CrazyCPM

This piece of work is dedicated to Dmitry Gerget, my scientific advisor at the Higher School of Economics.

Critical Path Method and PERT Analysis Library

A comprehensive Python library for project management analysis using Critical Path Method (CPM) and Program Evaluation and Review Technique (PERT). CrazyCPM provides network analysis capabilities with statistical modeling and professional visualization.

License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.

Features

Core Analysis

• Activity on Arrow - Most of CPM software use Activity on Node networks CrazyCPM implements Activiy on Arrow network modeling
• Critical Path Method (CPM) - Identify critical activities and project duration
• PERT Analysis - Statistical modeling with uncertainty quantification
• Multiple Duration Formats - Support for various input methods:
  • Direct duration and variance
  • Three-point PERT estimates (optimistic, most likely, pessimistic)
  • Two-point PERT estimates (optimistic, pessimistic)

Advanced Capabilities

• Network Optimization - Automatic (sub)optimal dummy activity generation
• Statistical analysis - Computation of project statistical parameters using propagation techniques
• Probabilistic Estimates - Quantile estimates and completion probabilities
• Ready for large and small projects - The modified PERT distribution is used in probabilistic computations which enables correct estimates even for small projects

Technical Features

• High Performance - C backend for computational efficiency
• Flexible Input Formats - Multiple link dependency formats
• Comprehensive Export - Dictionary and pandas DataFrame output
• Visualization - Graphviz-based network diagrams

Installation

Prerequisites

• Python 3.9 or higher
• C compiler (GCC, Clang, or MSVC)
• Graphviz system installation

Install from GitHub

# Clone the repository
git clone https://github.com/shkolnick-kun/crazy_cpm.git
cd crazycpm

Install system dependencies (Ubuntu/Debian)

sudo apt-get install graphviz build-essential cython

Install

pip install .

Dependencies

The package requires the following Python dependencies:

• numpy
• pandas
• graphviz
• betapert
• cython

Quick Start

Basic Usage

from crazy_cpm import NetworkModel

# Define your Work Breakdown Structure (WBS)
wbs = {
    1: {'letter': 'A', 'duration': 5.0, 'variance': 1.0, 'name': 'Design'},
    2: {'letter': 'B', 'duration': 3.0, 'name': 'Development'},
    3: {'letter': 'C', 'optimistic': 2, 'most_likely': 3, 'pessimistic': 5, 'name': 'Testing'}
}

# Define activity dependencies
links = [[1, 2], [2, 3]]  # A → B → C

# Create and analyze the network model
model = NetworkModel(wbs, links=links)

# Get results as DataFrames
activities_df, events_df = model.to_dataframe()

# Generate visualization
model.viz('project_network')

Advanced PERT Analysis

# Mixed duration formats with probabilistic analysis
wbs_advanced = {
    1: {'letter': 'A', 'optimistic': 3, 'most_likely': 5, 'pessimistic': 8},
    2: {'letter': 'B', 'optimistic': 2, 'pessimistic': 6},
    3: {'letter': 'C', 'duration': 4.0, 'variance': 0.5}
}

# Create model with custom probability level
model_pert = NetworkModel(wbs_advanced, links=links, p=0.95)

# Access probabilistic estimates
activity = model_pert.get_activity_by_wbs_id(1)
print(f"95% quantile early start: {activity.early_start_pqe}")

Input Formats

Activity Duration Specifications

Three-point PERT:

activity_data = {
    'letter': 'X',
    'optimistic': 3.0,      # Best-case scenario
    'most_likely': 5.0,     # Most probable duration
    'pessimistic': 8.0      # Worst-case scenario
}

Two-point PERT:

activity_data = {
    'letter': 'Y',
    'optimistic': 2.0,
    'pessimistic': 6.0      # most_likely is calculated automatically
}

Direct Parameters:

activity_data = {
    'letter': 'Z',
    'duration': 4.5,        # Mean duration
    'variance': 0.25        # Duration variance (optional)
}

Dependency Link Formats

Two-row format:

links = [
    [1, 2, 3],  # Source activities
    [2, 3, 4]   # Destination activities
]

Two-column format:

links = [
    [1, 2],     # Activity 1 → Activity 2
    [2, 3],     # Activity 2 → Activity 3
    [3, 4]      # Activity 3 → Activity 4
]

Dictionary format:

links = {
    'src': [1, 2, 3],
    'dst': [2, 3, 4]
}

API Reference

NetworkModel Class

The main class for network analysis:

model = NetworkModel(
    wbs_dict,           # Work Breakdown Structure dictionary
    lnk_src=None,       # Source activities (old format)
    lnk_dst=None,       # Destination activities (old format)
    links=None,         # Dependency links (new formats)
    p=0.95,             # Probability level for quantile estimates
    default_risk=0.3,   # Default risk factor
    debug=False         # Enable debug mode
)

Key Methods

• to_dataframe() - Export results to pandas DataFrames
• to_dict() - Export results to dictionary format
• viz(output_path) - Generate network visualization

Output Examples

Activities DataFrame

id	wbs_id	letter	duration	variance	early_start	late_start	reserve	name
1	1	A	5.0	1.0	0.0	0.0	0.0	Design
2	2	B	3.0	0.0	5.0	5.0	0.0	Development

Events DataFrame

id	stage	early	late	reserve
1	0	0.0	0.0	0.0
2	1	5.0	5.0	0.0
3	2	8.0	8.0	0.0

Visualization

The library generates network diagrams using Graphviz:

# Generate and save visualization
dot = model.viz('my_project')

# Customize output path
model.viz(output_path='/path/to/project_network')

The visualization includes:

• Nodes: events
• Edges: activities
• Red elements: Critical path (zero time reserve)
• Orange elements: Near-critical activities and events
• Black elements: Non-critical activities and events
• Dashed lines: Dummy activities