ccpm-scheduler 0.12.0

Deterministic Critical Chain Project Management (CCPM) scheduler: validate a project network, build a resource-leveled buffered schedule, verify it, and plot a buffer-aware Gantt chart. Library + CLI, designed for both humans and AI agents.

ccpm-scheduler

Deterministic Critical Chain Project Management (CCPM) scheduling as a Python library and CLI — built for humans, AI agents, and integration with project management tools.

Give ccpm-scheduler a project network (tasks, dependencies, duration estimates, resource assignments) plus resource availability, and it produces a proper Critical Chain schedule:

• Resource-Leveled: Resolves resource contention so no resource is over-allocated.
• As-Late-As-Possible (ALAP) Scheduling: Eliminates Parkinson's Law by scheduling non-critical work to finish just when needed.
• Protected by Buffers: Insulates project completion with a Project Buffer and protects merge points with Feeding Buffers.
• Buffer-Aware Visualizations: Generates rich Gantt charts (.png) and interactive standalone web dependency graphs (.html).
• Fully Deterministic: The same input always produces byte-identical schedule outputs — ideal for automation, scripting, and CI/CD pipelines.

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Quickstart

Installation

Install via pip or run instantly with uv:

# Install via pip
pip install ccpm-scheduler

# Or run directly without installation via uv
uvx ccpm-scheduler --help

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Status & Roadmap

All primary development phases outlined in PLAN.md (Phases 1 through 6) are fully complete, including:

• Behaviour-preserving core engine and typed data model.
• Agent-friendly CLI with machine-readable --json contracts.
• Integration with AI agents (Claude skills) and GUI planners (our-planner).
• Selectable buffer-sizing methods (cap Cut & Paste, hchain 50% chain, rsem root-squared error).

Future Direction: Fractional Resource Assignments

Currently, ccpm-scheduler enforces whole-resource allocations (1.0 capacity per task). Fractional resource assignments (e.g. allocating 0.5 FTE of a resource to a task) are identified as the primary area for future improvement. Future decisions on fractional leveling algorithms will be guided by feedback from real-world usage and community input.

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CLI Usage

Built for humans and AI agents: exit codes form a strict contract (0 = ok, 1 = validation or schedule issue with structured report, 2 = usage error), --json prints machine-readable JSON documents, and outputs are strictly deterministic.

# 1. Validate a project network
ccpm-scheduler validate tasks.csv resources.csv calendar.csv

# 2. Build a resource-leveled, buffered schedule
ccpm-scheduler build tasks.csv resources.csv --calendar calendar.csv \
    --out-dir plan --title "Website relaunch" --buffer-method cap

# 3. Verify schedule integrity against project constraints
ccpm-scheduler check plan/schedule.csv tasks.csv resources.csv calendar.csv

# 4. Plot a buffer-aware Gantt chart with resource utilization
ccpm-scheduler plot plan/schedule.csv plan/gantt.png --resources resources.csv

# 5. Generate an interactive web graph visualization
ccpm-scheduler graph plan/schedule.csv plan/project-network.html \
    --tasks tasks.csv --title "Website relaunch"

# 6. Inspect machine-readable JSON Schemas
ccpm-scheduler schema network

JSON Input & Pipe Support

Networks can be passed as CSV files or as a single JSON document (via file path or - for stdin):

echo '{"tasks": [...], "resources": [...]}' | ccpm-scheduler build - --json

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Library API

Import ccpm-scheduler directly into Python applications:

from ccpm_scheduler import (
    load_network,
    validate_network,
    build_schedule,
    check_schedule,
    plot_schedule,
)

# Load network from CSV or JSON
network = load_network("tasks.csv", "resources.csv", "calendar.csv")

# Validate network rules (cycle detection, resource assignment, duration checks)
report = validate_network(network)
if report.ok:
    # Build schedule using Cut & Paste (cap) buffer sizing
    result = build_schedule(network, title="My Project", buffer_method="cap")
    
    # Verify schedule rules
    assert check_schedule(result.schedule, network).ok
    
    # Plot Gantt chart
    plot_schedule(
        result.schedule,
        "gantt.png",
        resources=network.resources,
        calendar=network.calendar,
    )
    print(result.stats.status_line("My Project"))

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File Contracts

Input Format

• tasks.csv: id, name, realistic_duration, optimal_duration (optional), predecessor_ids, resource_ids, url (optional)
  • realistic_duration: Estimate including safety margin.
  • optimal_duration: Padding-free duration. If omitted, the classic 50% safety cut is applied automatically.
  • predecessor_ids: Semicolon-separated links (A, A:SS+2, A:FF, A:SF).
  • resource_ids: Semicolon-separated resource IDs assigned to the task.
• resources.csv: id, name, capacity, url (optional) (capacity defaults to 1).
• calendar.csv (optional): resource_id, from, to, capacity: Overrides resource capacity on half-open day intervals [from, to).

Output Format

• schedule.csv: Scheduled tasks and buffers with start/finish dates, chain designations (critical, feeding-n), and link attachments (:PB, :FB).
• summary.md: Project summary documenting critical chain duration, project buffer size, feeding buffers, promised completion date, and buffer calculation method (cap, hchain, rsem).
• gantt.png: High-resolution Gantt chart showing critical chain, feeding chains, buffers, dependencies, and daily resource utilization.
• project-network.html: Standalone interactive HTML graph (Vis-network) allowing zooming, panning, node dragging, and resource filtering.

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Development & Testing

We welcome contributions! The repository uses uv for fast dependency management and prek for git pre-commit checks.

Development Setup

# Clone the repository
git clone https://github.com/rnwolf/ccpm-scheduler.git
cd ccpm-scheduler

# Install development dependencies in virtualenv
uv sync

# Run the test suite with coverage report
uv run pytest

# Run pre-commit checks (ruff check & ruff format)
prek run --all-files

Dev Dependencies & Tooling

• Test Runner & Coverage: pytest, pytest-cov (unit, integration, and CLI contract testing with coverage reports).
• Property-Based Testing: hypothesis (generates random DAG project networks to test scheduling invariants).
• Linter & Formatter: ruff (fast linting and code formatting).
• Pre-commit Hooks: prek (verifies ruff rules before git commits).

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Feedback, Suggestions & Issues

If you encounter tool errors, engine bugs, or have suggestions for improvements (such as real-world use cases for fractional resource assignments), please open an issue on GitHub:

👉 Submit a GitHub Issue

When reporting bugs, please attach your project input (tasks.csv, resources.csv, or anonymized JSON) and the error report emitted by --json to help us diagnose and resolve the issue quickly.

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License

Dual-licensed under either of MIT or Apache License 2.0, at your option.