Critical Path Analyzer
Business Case
Problem Statement
Schedule management requires understanding:
- Which activities are critical?
- How much float exists?
- What delays impact completion?
- Where to focus resources?
Solution
Analyze schedule network to identify critical path, calculate float, and provide actionable schedule insights.
Technical Implementation
import pandas as pd
import numpy as np
from typing import Dict, Any, List, Optional, Set
from dataclasses import dataclass, field
from datetime import date, timedelta
from enum import Enum
from collections import defaultdict
class ActivityStatus(Enum):
NOT_STARTED = "not_started"
IN_PROGRESS = "in_progress"
COMPLETED = "completed"
DELAYED = "delayed"
@dataclass
class Activity:
activity_id: str
name: str
duration: int # days
predecessors: List[str]
early_start: int = 0
early_finish: int = 0
late_start: int = 0
late_finish: int = 0
total_float: int = 0
free_float: int = 0
is_critical: bool = False
status: ActivityStatus = ActivityStatus.NOT_STARTED
percent_complete: float = 0
actual_start: Optional[date] = None
actual_finish: Optional[date] = None
@dataclass
class CriticalPathResult:
critical_path: List[str]
project_duration: int
activities: Dict[str, Activity]
near_critical: List[str] # Float < 5 days
total_float_days: int
class CriticalPathAnalyzer:
"""Analyze project critical path."""
NEAR_CRITICAL_THRESHOLD = 5 # days
def __init__(self, project_start: date):
self.project_start = project_start
self.activities: Dict[str, Activity] = {}
def add_activity(self,
activity_id: str,
name: str,
duration: int,
predecessors: List[str] = None):
"""Add activity to network."""
self.activities[activity_id] = Activity(
activity_id=activity_id,
name=name,
duration=duration,
predecessors=predecessors or []
)
def import_from_dataframe(self, df: pd.DataFrame):
"""Import activities from DataFrame."""
for _, row in df.iterrows():
preds = row.get('predecessors', '')
if pd.isna(preds):
pred_list = []
else:
pred_list = [p.strip() for p in str(preds).split(',') if p.strip()]
self.add_activity(
activity_id=str(row['activity_id']),
name=row['name'],
duration=int(row['duration']),
predecessors=pred_list
)
def _forward_pass(self):
"""Calculate early start and early finish (forward pass)."""
# Topological sort
sorted_activities = self._topological_sort()
for activity_id in sorted_activities:
activity = self.activities[activity_id]
# Early start = max(early finish of all predecessors)
if not activity.predecessors:
activity.early_start = 0
else:
activity.early_start = max(
self.activities[pred].early_finish
for pred in activity.predecessors
if pred in self.activities
)
activity.early_finish = activity.early_start + activity.duration
def _backward_pass(self):
"""Calculate late start and late finish (backward pass)."""
# Find project duration
project_duration = max(a.early_finish for a in self.activities.values())
# Build successors map
successors = defaultdict(list)
for activity_id, activity in self.activities.items():
for pred in activity.predecessors:
if pred in self.activities:
successors[pred].append(activity_id)
# Reverse topological order
sorted_activities = self._topological_sort()[::-1]
for activity_id in sorted_activities:
activity = self.activities[activity_id]
# Late finish = min(late start of all successors)
if activity_id not in successors or not successors[activity_id]:
activity.late_finish = project_duration
else:
activity.late_finish = min(
self.activities[succ].late_start
for succ in successors[activity_id]
)
activity.late_start = activity.late_finish - activity.duration
# Calculate floats
activity.total_float = activity.late_start - activity.early_start
activity.is_critical = activity.total_float == 0
def _topological_sort(self) -> List[str]:
"""Topological sort of activities."""
visited = set()
result = []
def visit(activity_id: str):
if activity_id in visited:
return
visited.add(activity_id)
activity = self.activities.get(activity_id)
if activity:
for pred in activity.predecessors:
if pred in self.activities:
visit(pred)
result.append(activity_id)
for activity_id in self.activities:
visit(activity_id)
return result
def calculate_critical_path(self) -> CriticalPathResult:
"""Calculate critical path and all float values."""
self._forward_pass()
self._backward_pass()
# Find critical path
critical_activities = [
a.activity_id for a in self.activities.values()
if a.is_critical
]
# Near-critical activities
near_critical = [
a.activity_id for a in self.activities.values()
if 0 < a.total_float <= self.NEAR_CRITICAL_THRESHOLD
]
project_duration = max(a.early_finish for a in self.activities.values())
total_float = sum(a.total_float for a in self.activities.values())
return CriticalPathResult(
critical_path=critical_activities,
project_duration=project_duration,
activities=self.activities,
near_critical=near_critical,
total_float_days=total_float
)
def get_schedule_dates(self) -> pd.DataFrame:
"""Get schedule with dates."""
data = []
for activity in self.activities.values():
early_start_date = self.project_start + timedelta(days=activity.early_start)
early_finish_date = self.project_start + timedelta(days=activity.early_finish)
late_start_date = self.project_start + timedelta(days=activity.late_start)
late_finish_date = self.project_start + timedelta(days=activity.late_finish)
data.append({
'Activity ID': activity.activity_id,
'Name': activity.name,
'Duration': activity.duration,
'Early Start': early_start_date,
'Early Finish': early_finish_date,
'Late Start': late_start_date,
'Late Finish': late_finish_date,
'Total Float': activity.total_float,
'Critical': 'Yes' if activity.is_critical else 'No'
})
return pd.DataFrame(data)
def analyze_delay_impact(self,
activity_id: str,
delay_days: int) -> Dict[str, Any]:
"""Analyze impact of delay on project."""
activity = self.activities.get(activity_id)
if not activity:
return {}
absorbed_by_float = min(delay_days, activity.total_float)
project_delay = max(0, delay_days - activity.total_float)
# Find affected activities
affected = []
if project_delay > 0:
# Activities that could be affected (successors)
for a in self.activities.values():
if activity_id in a.predecessors:
affected.append(a.activity_id)
return {
'activity': activity_id,
'delay_days': delay_days,
'available_float': activity.total_float,
'absorbed_by_float': absorbed_by_float,
'project_delay': project_delay,
'affected_activities': affected,
'is_critical_delay': project_delay > 0
}
def suggest_acceleration(self,
target_reduction: int) -> List[Dict[str, Any]]:
"""Suggest activities to accelerate to meet target."""
result = self.calculate_critical_path()
suggestions = []
# Focus on critical activities
for activity_id in result.critical_path:
activity = self.activities[activity_id]
# Assume can reduce by 20% max
max_reduction = int(activity.duration * 0.2)
if max_reduction > 0:
suggestions.append({
'activity': activity_id,
'name': activity.name,
'current_duration': activity.duration,
'max_reduction': max_reduction,
'reason': 'Critical path activity'
})
# Sort by potential impact
return sorted(suggestions, key=lambda x: x['max_reduction'], reverse=True)
def export_analysis(self, output_path: str) -> str:
"""Export analysis to Excel."""
result = self.calculate_critical_path()
with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
# Summary
summary_df = pd.DataFrame([{
'Project Start': self.project_start,
'Project Duration': result.project_duration,
'Project Finish': self.project_start + timedelta(days=result.project_duration),
'Critical Activities': len(result.critical_path),
'Near-Critical Activities': len(result.near_critical),
'Total Float (days)': result.total_float_days
}])
summary_df.to_excel(writer, sheet_name='Summary', index=False)
# Schedule
schedule_df = self.get_schedule_dates()
schedule_df.to_excel(writer, sheet_name='Schedule', index=False)
# Critical Path
critical_df = pd.DataFrame([
{
'Activity': a_id,
'Name': self.activities[a_id].name,
'Duration': self.activities[a_id].duration
}
for a_id in result.critical_path
])
critical_df.to_excel(writer, sheet_name='Critical Path', index=False)
return output_pathQuick Start
from datetime import date
# Initialize analyzer
analyzer = CriticalPathAnalyzer(project_start=date(2024, 6, 1))
# Add activities
analyzer.add_activity("A", "Site Preparation", 10, [])
analyzer.add_activity("B", "Foundation Excavation", 15, ["A"])
analyzer.add_activity("C", "Foundation Concrete", 20, ["B"])
analyzer.add_activity("D", "Structural Steel", 30, ["C"])
analyzer.add_activity("E", "MEP Rough-in", 25, ["C"])
analyzer.add_activity("F", "Exterior Walls", 20, ["D"])
analyzer.add_activity("G", "Interior Finish", 15, ["E", "F"])
# Calculate critical path
result = analyzer.calculate_critical_path()
print(f"Project Duration: {result.project_duration} days")
print(f"Critical Path: {result.critical_path}")Common Use Cases
1. Analyze Delay Impact
impact = analyzer.analyze_delay_impact("C", delay_days=5)
print(f"Project Delay: {impact['project_delay']} days")2. Get Schedule Dates
schedule = analyzer.get_schedule_dates()
print(schedule[['Activity ID', 'Early Start', 'Late Finish', 'Total Float']])3. Acceleration Suggestions
suggestions = analyzer.suggest_acceleration(target_reduction=10)
for s in suggestions:
print(f"{s['activity']}: can reduce {s['max_reduction']} days")Resources
- DDC Book: Chapter 4.2 - Schedule Analysis