labor-allocation について
labor-allocationは、建設プロジェクトの労働者配置と追跡を行うスキルです。日次名簿、労務費計算、人員需要の予測に対応し、資源利用と工程の管理を支援します。
主な機能
- 01職種と技能を含む作業員の登録
- 02日次名簿の取得
- 03労務費の計算
- 04人員需要の予測
活用シーン
- 01日々の作業員名簿の作成
- 02作業単位の労務費計算
- 03計画中の作業に必要な人員の推定
インストール
コマンドをコピーしてターミナルで実行します。
$
npx skills add https:/ / github.com/ datadrivenconstruction/ ddc_skills_for_ai_agents_in_construction --skill labor-allocationインストール後、Claude Code で/labor-allocationを入力して読み込まれたか確認します。
同梱ファイルclaw.jsoninstructions.md
詳細
労働力配分マネージャー
ビジネス上の課題
問題点
労務管理における課題:
- 作業員を作業に割り当てる
- 作業負荷のバランスを取る
- 出勤状況を追跡する
- 生産性を最適化する
ソリューション
リソースの活用を最適化し、プロジェクトのスケジュールを維持するための、体系的な労働力の配分と追跡。
技術的な実装
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
クイックスタート
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)
一般的なユースケース
1. 日次配置表
roster = labor.get_daily_roster(date.today())
print(roster)
2. 人件費
cost = labor.calculate_labor_cost(activity_id="A-101")
print(f"Total Cost: ${cost['total_cost']:,.2f}")
3. 必要人員の予測
activities = [
{'trade': 'concrete', 'manhours': 400, 'duration_days': 5},
{'trade': 'carpenter', 'manhours': 200, 'duration_days': 10}
]
needs = labor.forecast_labor_needs(activities)
print(needs)
リソース
- DDC Book:第 3.1 章 - 労務管理
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