CWICR Labor Scheduler
Business Case
Problem Statement
Project managers need to plan labor allocation:
- How many workers per day?
- What skills are needed when?
- How to balance workload across project phases?
- How to avoid resource conflicts?
Solution
Data-driven labor scheduling using CWICR labor norms to generate crew schedules, loading curves, and skill requirement timelines.
Business Value
- Accurate planning - Based on validated labor norms
- Resource leveling - Smooth workload distribution
- Skill matching - Right workers at right time
- Cost control - Optimize labor costs
Technical Implementation
import pandas as pd
import numpy as np
from typing import Dict, Any, List, Optional, Tuple
from dataclasses import dataclass, field
from datetime import datetime, timedelta
from enum import Enum
from collections import defaultdict
class ShiftType(Enum):
"""Work shift types."""
SINGLE = "single" # 8 hours
DOUBLE = "double" # 16 hours (2 shifts)
TRIPLE = "triple" # 24 hours (3 shifts)
EXTENDED = "extended" # 10 hours
class SkillLevel(Enum):
"""Worker skill levels."""
UNSKILLED = 1
SEMI_SKILLED = 2
SKILLED = 3
FOREMAN = 4
SPECIALIST = 5
@dataclass
class LaborRequirement:
"""Labor requirement for a work item."""
work_item_code: str
description: str
total_hours: float
skill_level: SkillLevel
trade: str
start_date: datetime
end_date: datetime
daily_hours: float = 0.0
@dataclass
class CrewAssignment:
"""Crew assignment for a period."""
date: datetime
trade: str
skill_level: SkillLevel
workers_needed: int
hours_per_worker: float
total_hours: float
work_items: List[str]
@dataclass
class LaborSchedule:
"""Complete labor schedule."""
project_name: str
start_date: datetime
end_date: datetime
total_labor_hours: float
peak_workers: int
average_workers: float
assignments: List[CrewAssignment]
daily_loading: Dict[str, int]
by_trade: Dict[str, float]
class CWICRLaborScheduler:
"""Schedule labor based on CWICR norms."""
HOURS_PER_SHIFT = {
ShiftType.SINGLE: 8,
ShiftType.DOUBLE: 16,
ShiftType.TRIPLE: 24,
ShiftType.EXTENDED: 10
}
def __init__(self, cwicr_data: pd.DataFrame):
self.data = cwicr_data
self._index_data()
def _index_data(self):
"""Index work items for fast lookup."""
if 'work_item_code' in self.data.columns:
self._code_index = self.data.set_index('work_item_code')
else:
self._code_index = None
def calculate_labor_requirements(self,
items: List[Dict[str, Any]],
project_start: datetime) -> List[LaborRequirement]:
"""Calculate labor requirements from work items."""
requirements = []
for item in items:
code = item.get('work_item_code', item.get('code'))
qty = item.get('quantity', 0)
duration_days = item.get('duration_days', 1)
start_offset = item.get('start_day', 0)
if self._code_index is not None and code in self._code_index.index:
work_item = self._code_index.loc[code]
labor_norm = float(work_item.get('labor_norm', 0) or 0)
total_hours = labor_norm * qty
# Determine trade from category
trade = self._get_trade(work_item.get('category', 'General'))
skill_level = self._get_skill_level(work_item)
start_date = project_start + timedelta(days=start_offset)
end_date = start_date + timedelta(days=duration_days)
daily_hours = total_hours / duration_days if duration_days > 0 else total_hours
requirements.append(LaborRequirement(
work_item_code=code,
description=str(work_item.get('description', '')),
total_hours=total_hours,
skill_level=skill_level,
trade=trade,
start_date=start_date,
end_date=end_date,
daily_hours=daily_hours
))
return requirements
def _get_trade(self, category: str) -> str:
"""Map category to trade."""
trade_mapping = {
'concrete': 'Concrete',
'masonry': 'Masonry',
'steel': 'Steel',
'carpentry': 'Carpentry',
'plumbing': 'Plumbing',
'electrical': 'Electrical',
'hvac': 'HVAC',
'painting': 'Painting',
'excavation': 'Earthwork',
'roofing': 'Roofing'
}
cat_lower = str(category).lower()
for key, trade in trade_mapping.items():
if key in cat_lower:
return trade
return 'General'
def _get_skill_level(self, work_item) -> SkillLevel:
"""Determine skill level from work item."""
# Based on complexity or explicit field
if 'skill_level' in work_item.index:
level = int(work_item.get('skill_level', 3))
return SkillLevel(min(max(level, 1), 5))
return SkillLevel.SKILLED
def generate_schedule(self,
requirements: List[LaborRequirement],
shift_type: ShiftType = ShiftType.SINGLE,
max_workers_per_trade: int = 50) -> LaborSchedule:
"""Generate labor schedule from requirements."""
if not requirements:
return LaborSchedule(
project_name="",
start_date=datetime.now(),
end_date=datetime.now(),
total_labor_hours=0,
peak_workers=0,
average_workers=0,
assignments=[],
daily_loading={},
by_trade={}
)
hours_per_day = self.HOURS_PER_SHIFT[shift_type]
# Find date range
start_date = min(r.start_date for r in requirements)
end_date = max(r.end_date for r in requirements)
# Build daily labor loading
daily_loading = defaultdict(lambda: defaultdict(float))
for req in requirements:
current = req.start_date
while current < req.end_date:
date_key = current.strftime('%Y-%m-%d')
daily_loading[date_key][req.trade] += req.daily_hours
current += timedelta(days=1)
# Convert to crew assignments
assignments = []
daily_totals = {}
by_trade = defaultdict(float)
for date_key, trades in daily_loading.items():
date = datetime.strptime(date_key, '%Y-%m-%d')
day_total = 0
for trade, hours in trades.items():
workers = int(np.ceil(hours / hours_per_day))
workers = min(workers, max_workers_per_trade)
assignments.append(CrewAssignment(
date=date,
trade=trade,
skill_level=SkillLevel.SKILLED,
workers_needed=workers,
hours_per_worker=hours_per_day,
total_hours=hours,
work_items=[]
))
day_total += workers
by_trade[trade] += hours
daily_totals[date_key] = day_total
# Statistics
total_hours = sum(r.total_hours for r in requirements)
peak_workers = max(daily_totals.values()) if daily_totals else 0
avg_workers = sum(daily_totals.values()) / len(daily_totals) if daily_totals else 0
return LaborSchedule(
project_name="Project",
start_date=start_date,
end_date=end_date,
total_labor_hours=total_hours,
peak_workers=peak_workers,
average_workers=round(avg_workers, 1),
assignments=assignments,
daily_loading=dict(daily_totals),
by_trade=dict(by_trade)
)
def level_resources(self,
schedule: LaborSchedule,
target_workers: int) -> LaborSchedule:
"""Level resources to target workforce size."""
# Resource leveling algorithm
# Shifts work to reduce peaks while maintaining total hours
daily_loads = schedule.daily_loading.copy()
# Find days exceeding target
over_days = {d: w for d, w in daily_loads.items() if w > target_workers}
under_days = {d: w for d, w in daily_loads.items() if w < target_workers}
# Simple leveling: can't easily shift without changing durations
# Return schedule with analysis
leveling_analysis = {
'days_over_target': len(over_days),
'days_under_target': len(under_days),
'max_over': max(over_days.values()) - target_workers if over_days else 0,
'leveling_possible': len(over_days) == 0
}
return schedule
def generate_loading_curve(self,
schedule: LaborSchedule) -> pd.DataFrame:
"""Generate labor loading curve data."""
data = []
for date_str, workers in sorted(schedule.daily_loading.items()):
data.append({
'date': date_str,
'workers': workers,
'cumulative_hours': 0 # Would need to calculate
})
df = pd.DataFrame(data)
# Add cumulative hours
if not df.empty:
hours_per_worker = 8 # Assuming single shift
df['daily_hours'] = df['workers'] * hours_per_worker
df['cumulative_hours'] = df['daily_hours'].cumsum()
return df
def get_trade_breakdown(self,
schedule: LaborSchedule) -> pd.DataFrame:
"""Get labor breakdown by trade."""
trade_data = []
for trade, hours in schedule.by_trade.items():
trade_data.append({
'trade': trade,
'total_hours': round(hours, 1),
'worker_days': round(hours / 8, 1),
'percentage': round(hours / schedule.total_labor_hours * 100, 1) if schedule.total_labor_hours > 0 else 0
})
return pd.DataFrame(trade_data).sort_values('total_hours', ascending=False)
def optimize_crew_composition(self,
requirements: List[LaborRequirement],
available_workers: Dict[str, int]) -> Dict[str, Any]:
"""Optimize crew composition based on availability."""
required_by_trade = defaultdict(float)
for req in requirements:
required_by_trade[req.trade] += req.total_hours
analysis = {
'sufficient': True,
'shortages': {},
'surplus': {},
'recommendations': []
}
for trade, hours_needed in required_by_trade.items():
workers_needed = int(np.ceil(hours_needed / 8)) # Per day
available = available_workers.get(trade, 0)
if workers_needed > available:
analysis['sufficient'] = False
analysis['shortages'][trade] = workers_needed - available
analysis['recommendations'].append(
f"Hire {workers_needed - available} additional {trade} workers"
)
elif available > workers_needed * 1.5:
analysis['surplus'][trade] = available - workers_needed
return analysis
class WeeklyScheduleGenerator:
"""Generate weekly labor schedules."""
def __init__(self, scheduler: CWICRLaborScheduler):
self.scheduler = scheduler
def generate_weekly_schedule(self,
schedule: LaborSchedule,
week_start: datetime) -> pd.DataFrame:
"""Generate schedule for specific week."""
week_end = week_start + timedelta(days=7)
weekly_assignments = [
a for a in schedule.assignments
if week_start <= a.date < week_end
]
# Pivot by day and trade
data = []
for a in weekly_assignments:
data.append({
'date': a.date.strftime('%Y-%m-%d'),
'day': a.date.strftime('%A'),
'trade': a.trade,
'workers': a.workers_needed,
'hours': a.total_hours
})
return pd.DataFrame(data)
def export_to_excel(self,
schedule: LaborSchedule,
output_path: str) -> str:
"""Export schedule to Excel."""
with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
# Loading curve
loading = self.scheduler.generate_loading_curve(schedule)
loading.to_excel(writer, sheet_name='Loading Curve', index=False)
# Trade breakdown
trades = self.scheduler.get_trade_breakdown(schedule)
trades.to_excel(writer, sheet_name='By Trade', index=False)
# Summary
summary = pd.DataFrame([{
'Total Labor Hours': schedule.total_labor_hours,
'Peak Workers': schedule.peak_workers,
'Average Workers': schedule.average_workers,
'Project Duration (days)': (schedule.end_date - schedule.start_date).days
}])
summary.to_excel(writer, sheet_name='Summary', index=False)
return output_pathQuick Start
from datetime import datetime
# Load CWICR data
cwicr = pd.read_parquet("ddc_cwicr_en.parquet")
# Initialize scheduler
scheduler = CWICRLaborScheduler(cwicr)
# Define work items with schedule
items = [
{'work_item_code': 'EXCV-001', 'quantity': 500, 'duration_days': 5, 'start_day': 0},
{'work_item_code': 'CONC-002', 'quantity': 200, 'duration_days': 10, 'start_day': 5},
{'work_item_code': 'REBAR-003', 'quantity': 5000, 'duration_days': 8, 'start_day': 3}
]
# Calculate requirements
project_start = datetime(2024, 6, 1)
requirements = scheduler.calculate_labor_requirements(items, project_start)
# Generate schedule
schedule = scheduler.generate_schedule(requirements)
print(f"Total Labor Hours: {schedule.total_labor_hours:,.0f}")
print(f"Peak Workers: {schedule.peak_workers}")
print(f"Average Workers: {schedule.average_workers}")Common Use Cases
1. Resource Leveling
# Check if schedule can meet target
leveled = scheduler.level_resources(schedule, target_workers=25)2. Loading Curve
# Get labor loading data for charts
loading_df = scheduler.generate_loading_curve(schedule)3. Trade Breakdown
# See hours by trade
trades = scheduler.get_trade_breakdown(schedule)
print(trades)4. Weekly Schedule Export
gen = WeeklyScheduleGenerator(scheduler)
gen.export_to_excel(schedule, "labor_schedule.xlsx")Resources
- GitHub: OpenConstructionEstimate-DDC-CWICR
- DDC Book: Chapter 3.1 - Labor Resource Planning