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Challenge

Construction Permitting Critical Path Delay Predictor

Complex adaptive reuse and conversion projects encounter long permitting bottlenecks across multiple municipal departments. Build a network graph optimization pipeline using NetworkX and LightGBM to identify critical path delay bottlenecks in municipal permitting schedules. Achieve 85%+ accuracy in predicting total permit review lead times.

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Challenge brief

What you are building

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

Develop a schedule network graph and delay prediction pipeline using NetworkX and LightGBM to reduce permitting friction.

Delivery guide

How work is evaluated

Evaluation

Evaluates critical path calculation precision and total duration prediction error.

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

Critical Path Alignment

Critical path calculation must equal sum of longest dependency chain

Binary check

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

Dimension 2

Lead Time Prediction MAE

Mean Absolute Error in days compared to real municipal review outcomes • target: 2.5 • 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

  • Model municipal department permit review dependencies as Directed Acyclic Graphs (DAGs)

  • Train LightGBM regression models to predict department-level review durations

  • Perform Critical Path Method (CPM) graph traversals using NetworkX to identify bottlenecks

  • Output schedule risk reports highlighting high-delay dependency nodes

Resources and assets

Reference links and supporting material

Dataset notes

Historical construction permitting logs showing agency review durations and dependency sequences.

Challenge setup

Evaluates critical path calculation precision and total duration prediction error.

Challenge input

JSON with department_nodes, dependency_edges, estimated_days

NetworkX

Executes DAG topological sorting and critical path computations

Evaluated output

JSON with total_lead_time_days, critical_path_nodes

Evaluation checks
  • Critical path calculation must equal sum of longest dependency chain
  • Mean Absolute Error in days compared to real municipal review outcomes • target: 2.5 • range: 0-10
Configured evaluation evidence
  • Benchmark: GraphCPMBench
  • Lead Time Prediction MAE target: 2.5
  • 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
  • Exa · Required
  • Ellipsis · Optional
  • AutoGluon · Optional

Evaluation contract

  • Critical Path Alignment · Weight 1
  • Lead Time Prediction MAE · Weight 1

Metrics

  • Lead Time Prediction MAE · Target 2.5

Benchmarks

  • GraphCPMBench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON with department_nodes, dependency_edges, estimated_days

    Output: JSON with total_lead_time_days, critical_path_nodes

    Execution harness: Python

Evaluation tools

  • NetworkX · Graph Engine

Recipe freeze marker

No recipe freeze time is recorded.

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