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Challenge

Subcontractor Delay & Construction Schedule Risk Forecasting

Construction delays cascade through trade interdependencies, causing major budget overruns on complex buildouts. Build a Critical Path Method (CPM) and schedule risk simulation engine using Python and NetworkX to forecast project completion delays under subcontractor supply chain disruptions. Achieve less than 3 days variance in predicted project completion dates.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Develop a construction Critical Path Method (CPM) schedule forecasting pipeline using NetworkX to predict project completion delays.

Delivery guide

How work is evaluated

Evaluation

Evaluates accuracy of Critical Path duration calculations and predicted project completion days.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

path_exact

Verifies exact critical path task sequence.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

duration_mae

Mean Absolute Error in total estimated project days. • target: 0.5 • range: 0-10

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Construct Directed Acyclic Graphs (DAG) representing construction activity work breakdowns

  • Implement Forward and Backward pass algorithms to calculate task float and Critical Path

  • Simulate Monte Carlo schedule delay distributions for trade dependency chains

  • Output quantitative schedule buffer recommendations for project superintendents

Resources and assets

Reference links and supporting material

Dataset notes

Dataset of construction task networks, task dependencies, baseline durations, and historical trade delays.

Challenge setup

Evaluates accuracy of Critical Path duration calculations and predicted project completion days.

Challenge input

JSON object with baseline_tasks, delayed_task_id, delay_days

NetworkX

Standard Python graph processing library.

Evaluated output

JSON object with updated_total_days, schedule_variance_days, new_critical_path

Evaluation checks
  • Verifies exact critical path task sequence.
  • Mean Absolute Error in total estimated project days. • target: 0.5 • range: 0-10
Configured evaluation evidence
  • Benchmark: CPMGraphAnalysisBench
  • Duration Mae target: 0.5
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • Hamming · Required
  • Decipher AI · Optional
  • Zapier Interfaces · Optional

Evaluation contract

  • path_exact · Weight 1
  • duration_mae · Weight 1

Metrics

  • duration_mae · Target 0.5

Benchmarks

  • CPMGraphAnalysisBench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON object with baseline_tasks, delayed_task_id, delay_days

    Output: JSON object with updated_total_days, schedule_variance_days, new_critical_path

    Execution harness: Python

Evaluation tools

  • NetworkX · DAG CPM Solver

Recipe freeze marker

No recipe freeze time is recorded.

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