Allocate and track labor resources across project activities. Balance workload, track attendance, and optimize crew assignments.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Labor management challenges:
Systematic labor allocation and tracking to optimize resource utilization and maintain project schedule.
import pandas as pd
from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import date, timedelta
from enum import Enum
from collections import defaultdict
class Trade(Enum):
CARPENTER = "carpenter"
ELECTRICIAN = "electrician"
PLUMBER = "plumber"
CONCRETE = "concrete"
MASON = "mason"
IRONWORKER = "ironworker"
HVAC = "hvac"
PAINTER = "painter"
LABORER = "laborer"
OPERATOR = "operator"
FOREMAN = "foreman"
class WorkerStatus(Enum):
AVAILABLE = "available"
ASSIGNED = "assigned"
ON_LEAVE = "on_leave"
SICK = "sick"
TERMINATED = "terminated"
class SkillLevel(Enum):
APPRENTICE = "apprentice"
JOURNEYMAN = "journeyman"
MASTER = "master"
@dataclass
class Worker:
worker_id: str
name: str
trade: Trade
skill_level: SkillLevel
hourly_rate: float
company: str
status: WorkerStatus = WorkerStatus.AVAILABLE
certifications: List[str] = field(default_factory=list)
@dataclass
class Assignment:
assignment_id: str
worker_id: str
activity_id: str
activity_name: str
start_date: date
end_date: date
hours_per_day: float
location: str
@dataclass
class AttendanceRecord:
date: date
worker_id: str
activity_id: str
hours_worked: float
overtime_hours: float
status: str # present, absent, late
class LaborAllocation:
"""Manage labor allocation and tracking."""
def __init__(self, project_name: str):
self.project_name = project_name
self.workers: Dict[str, Worker] = {}
self.assignments: List[Assignment] = []
self.attendance: List[AttendanceRecord] = []
def add_worker(self,
worker_id: str,
name: str,
trade: Trade,
skill_level: SkillLevel,
hourly_rate: float,
company: str,
certifications: List[str] = None) -> Worker:
"""Add worker to pool."""
worker = Worker(
worker_id=worker_id,
name=name,
trade=trade,
skill_level=skill_level,
hourly_rate=hourly_rate,
company=company,
certifications=certifications or []
)
self.workers[worker_id] = worker
return worker
def assign_worker(self,
worker_id: str,
activity_id: str,
activity_name: str,
start_date: date,
end_date: date,
hours_per_day: float = 8,
location: str = "") -> Optional[Assignment]:
"""Assign worker to activity."""
if worker_id not in self.workers:
return None
worker = self.workers[worker_id]
# Check for conflicts
conflicts = self.check_conflicts(worker_id, start_date, end_date)
if conflicts:
print(f"Warning: Worker has {len(conflicts)} conflicting assignments")
assignment = Assignment(
assignment_id=f"ASN-{len(self.assignments)+1:04d}",
worker_id=worker_id,
activity_id=activity_id,
activity_name=activity_name,
start_date=start_date,
end_date=end_date,
hours_per_day=hours_per_day,
location=location
)
self.assignments.append(assignment)
worker.status = WorkerStatus.ASSIGNED
return assignment
def check_conflicts(self,
worker_id: str,
start_date: date,
end_date: date) -> List[Assignment]:
"""Check for scheduling conflicts."""
conflicts = []
for assignment in self.assignments:
if assignment.worker_id != worker_id:
continue
# Check overlap
if not (end_date < assignment.start_date or start_date > assignment.end_date):
conflicts.append(assignment)
return conflicts
def record_attendance(self,
date: date,
worker_id: str,
activity_id: str,
hours_worked: float,
overtime_hours: float = 0,
status: str = "present"):
"""Record worker attendance."""
self.attendance.append(AttendanceRecord(
date=date,
worker_id=worker_id,
activity_id=activity_id,
hours_worked=hours_worked,
overtime_hours=overtime_hours,
status=status
))
def get_workers_by_trade(self, trade: Trade) -> List[Worker]:
"""Get available workers by trade."""
return [
w for w in self.workers.values()
if w.trade == trade and w.status in [WorkerStatus.AVAILABLE, WorkerStatus.ASSIGNED]
]
def get_daily_roster(self, target_date: date) -> pd.DataFrame:
"""Get roster for specific date."""
roster = []
for assignment in self.assignments:
if assignment.start_date <= target_date <= assignment.end_date:
worker = self.workers.get(assignment.worker_id)
if worker:
roster.append({
'Worker ID': worker.worker_id,
'Name': worker.name,
'Trade': worker.trade.value,
'Company': worker.company,
'Activity': assignment.activity_name,
'Location': assignment.location,
'Hours': assignment.hours_per_day
})
return pd.DataFrame(roster)
def get_activity_crew(self, activity_id: str) -> List[Dict[str, Any]]:
"""Get crew assigned to activity."""
crew = []
for assignment in self.assignments:
if assignment.activity_id == activity_id:
worker = self.workers.get(assignment.worker_id)
if worker:
crew.append({
'worker_id': worker.worker_id,
'name': worker.name,
'trade': worker.trade.value,
'skill_level': worker.skill_level.value,
'hourly_rate': worker.hourly_rate,
'start_date': assignment.start_date,
'end_date': assignment.end_date
})
return crew
def calculate_labor_cost(self,
activity_id: str = None,
start_date: date = None,
end_date: date = None) -> Dict[str, Any]:
"""Calculate labor costs."""
total_hours = 0
total_overtime = 0
total_cost = 0
by_trade = defaultdict(float)
for record in self.attendance:
# Filter by activity
if activity_id and record.activity_id != activity_id:
continue
# Filter by date
if start_date and record.date < start_date:
continue
if end_date and record.date > end_date:
continue
worker = self.workers.get(record.worker_id)
if not worker:
continue
regular_cost = record.hours_worked * worker.hourly_rate
overtime_cost = record.overtime_hours * worker.hourly_rate * 1.5
total_hours += record.hours_worked
total_overtime += record.overtime_hours
total_cost += regular_cost + overtime_cost
by_trade[worker.trade.value] += regular_cost + overtime_cost
return {
'total_hours': round(total_hours, 1),
'total_overtime': round(total_overtime, 1),
'total_cost': round(total_cost, 2),
'by_trade': dict(by_trade)
}
def get_utilization_report(self,
start_date: date,
end_date: date) -> pd.DataFrame:
"""Get worker utilization report."""
data = []
work_days = (end_date - start_date).days + 1
available_hours = work_days * 8
for worker in self.workers.values():
# Get attendance records
records = [
r for r in self.attendance
if r.worker_id == worker.worker_id
and start_date <= r.date <= end_date
]
worked_hours = sum(r.hours_worked + r.overtime_hours for r in records)
utilization = (worked_hours / available_hours * 100) if available_hours > 0 else 0
data.append({
'Worker ID': worker.worker_id,
'Name': worker.name,
'Trade': worker.trade.value,
'Available Hours': available_hours,
'Worked Hours': round(worked_hours, 1),
'Utilization %': round(utilization, 1)
})
return pd.DataFrame(data).sort_values('Utilization %', ascending=False)
def forecast_labor_needs(self,
activities: List[Dict[str, Any]]) -> Dict[str, Any]:
"""Forecast labor needs for activities."""
needs = defaultdict(lambda: {'hours': 0, 'workers': 0})
for activity in activities:
trade = activity.get('trade', 'laborer')
hours = activity.get('manhours', 0)
duration = activity.get('duration_days', 1)
workers_needed = hours / (duration * 8) if duration > 0 else 0
needs[trade]['hours'] += hours
needs[trade]['workers'] = max(needs[trade]['workers'], int(workers_needed) + 1)
# Check availability
for trade_name, requirement in needs.items():
try:
trade = Trade(trade_name)
available = len(self.get_workers_by_trade(trade))
requirement['available'] = available
requirement['shortage'] = max(0, requirement['workers'] - available)
except ValueError:
requirement['available'] = 0
requirement['shortage'] = requirement['workers']
return dict(needs)
def export_to_excel(self, output_path: str) -> str:
"""Export labor data to Excel."""
with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
# Workers
workers_df = pd.DataFrame([
{
'ID': w.worker_id,
'Name': w.name,
'Trade': w.trade.value,
'Skill': w.skill_level.value,
'Rate': w.hourly_rate,
'Company': w.company,
'Status': w.status.value
}
for w in self.workers.values()
])
workers_df.to_excel(writer, sheet_name='Workers', index=False)
# Assignments
assignments_df = pd.DataFrame([
{
'ID': a.assignment_id,
'Worker': a.worker_id,
'Activity': a.activity_name,
'Start': a.start_date,
'End': a.end_date,
'Hours/Day': a.hours_per_day,
'Location': a.location
}
for a in self.assignments
])
assignments_df.to_excel(writer, sheet_name='Assignments', index=False)
# Roster for today
roster = self.get_daily_roster(date.today())
roster.to_excel(writer, sheet_name='Today Roster', index=False)
return output_path
from datetime import date, timedelta
# Initialize manager
labor = LaborAllocation("Office Building A")
# Add workers
labor.add_worker("W001", "John Smith", Trade.CONCRETE, SkillLevel.JOURNEYMAN, 45, "ABC Concrete")
labor.add_worker("W002", "Mike Jones", Trade.CONCRETE, SkillLevel.APPRENTICE, 30, "ABC Concrete")
labor.add_worker("W003", "Tom Brown", Trade.CARPENTER, SkillLevel.MASTER, 55, "XYZ Carpentry")
# Assign to activity
labor.assign_worker(
worker_id="W001",
activity_id="A-101",
activity_name="Pour Slab Level 3",
start_date=date.today(),
end_date=date.today() + timedelta(days=5),
hours_per_day=10,
location="Level 3"
)
# Record attendance
labor.record_attendance(date.today(), "W001", "A-101", hours_worked=10, overtime_hours=2)
roster = labor.get_daily_roster(date.today())
print(roster)
cost = labor.calculate_labor_cost(activity_id="A-101")
print(f"Total Cost: ${cost['total_cost']:,.2f}")
activities = [
{'trade': 'concrete', 'manhours': 400, 'duration_days': 5},
{'trade': 'carpenter', 'manhours': 200, 'duration_days': 10}
]
needs = labor.forecast_labor_needs(activities)
print(needs)
아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
Create 4D construction simulations by linking BIM elements to project schedules. Generate time-based visualizations, sequence analysis, and construction phasing with Gantt integration.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Analyze 5000+ IFC and Revit projects at scale for patterns, benchmarks, and insights. Big data analysis for construction.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Check construction AI systems against the EU AI Act (Regulation 2024/1689): classify risk of estimation, scheduling, CV and agent tools, document transparency, keep human oversight. Use when deploying or auditing AI in construction.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Orchestrate multiple AI agents for construction workflows: estimator, scheduler, document, QA and safety agents coordinated by a supervisor agent, with human checkpoints. Use when automating end-to-end project processes with agentic AI.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Create Apache Airflow DAGs for construction data pipelines. Orchestrate ETL, validation, and reporting workflows.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Automate as-built documentation and digital handover for construction. Compile project records, generate O&M manuals, create asset databases, and ensure complete project closeout.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.