site-logistics-optimization

Site Logistics Optimization

Overview

This skill implements optimization algorithms for construction site logistics. Minimize delays, reduce costs, and improve safety through data-driven planning of deliveries, equipment placement, and material storage.

Optimization Areas:

• Material delivery scheduling
• Crane and equipment positioning
• Storage area allocation
• Site traffic flow
• Workforce routing
• Just-in-time delivery

Quick Start

from dataclasses import dataclass
from typing import List, Dict, Tuple
from datetime import datetime, timedelta
import heapq

@dataclass
class Delivery:
    delivery_id: str
    material_type: str
    quantity: float
    required_date: datetime
    unload_duration_min: int
    storage_area: str
    priority: int = 1  # 1=highest

@dataclass
class TimeSlot:
    start: datetime
    end: datetime
    is_available: bool = True
    delivery_id: str = None

def schedule_deliveries(deliveries: List[Delivery],
                       slots_per_day: int = 8,
                       unload_bays: int = 2) -> Dict[str, TimeSlot]:
    """Simple delivery scheduling"""
    # Sort by priority and required date
    sorted_deliveries = sorted(deliveries, key=lambda d: (d.priority, d.required_date))

    schedule = {}
    bay_schedules = {i: [] for i in range(unload_bays)}

    for delivery in sorted_deliveries:
        # Find available slot
        target_date = delivery.required_date.replace(hour=8, minute=0)

        for bay in range(unload_bays):
            # Check if bay has capacity
            bay_end = max([s.end for s in bay_schedules[bay]], default=target_date)

            if bay_end <= target_date:
                slot_start = target_date
            else:
                slot_start = bay_end

            slot_end = slot_start + timedelta(minutes=delivery.unload_duration_min)

            # Check if within working hours (8:00-18:00)
            if slot_end.hour <= 18:
                slot = TimeSlot(
                    start=slot_start,
                    end=slot_end,
                    is_available=False,
                    delivery_id=delivery.delivery_id
                )
                bay_schedules[bay].append(slot)
                schedule[delivery.delivery_id] = {
                    'bay': bay,
                    'slot': slot
                }
                break

    return schedule

# Example
deliveries = [
    Delivery("D001", "concrete", 50, datetime(2024, 1, 15, 9, 0), 45, "Zone-A", 1),
    Delivery("D002", "rebar", 10, datetime(2024, 1, 15, 10, 0), 30, "Zone-B", 2),
    Delivery("D003", "formwork", 20, datetime(2024, 1, 15, 9, 0), 60, "Zone-A", 1),
]

schedule = schedule_deliveries(deliveries)
for d_id, info in schedule.items():
    print(f"{d_id}: Bay {info['bay']}, {info['slot'].start.strftime('%H:%M')}-{info['slot'].end.strftime('%H:%M')}")

Comprehensive Logistics Optimization

Site Layout Model

from dataclasses import dataclass, field
from typing import List, Dict, Tuple, Optional
from datetime import datetime, date, timedelta
from enum import Enum
import numpy as np
from scipy.optimize import linear_sum_assignment
import heapq

class ZoneType(Enum):
    CONSTRUCTION = "construction"
    STORAGE = "storage"
    UNLOADING = "unloading"
    STAGING = "staging"
    ACCESS = "access"
    EQUIPMENT = "equipment"
    OFFICE = "office"

@dataclass
class SiteZone:
    zone_id: str
    zone_type: ZoneType
    area_sqm: float
    capacity: float  # Depends on type (tons, units, etc.)
    current_usage: float = 0
    position: Tuple[float, float] = (0, 0)  # x, y coordinates
    access_points: List[Tuple[float, float]] = field(default_factory=list)
    restrictions: List[str] = field(default_factory=list)

@dataclass
class Equipment:
    equipment_id: str
    equipment_type: str  # crane, forklift, etc.
    max_reach: float  # meters
    capacity: float  # tons
    position: Tuple[float, float] = (0, 0)
    operating_radius: float = 0

@dataclass
class DeliveryRequest:
    request_id: str
    material_type: str
    quantity: float
    unit: str
    required_date: date
    required_time_window: Tuple[int, int]  # (start_hour, end_hour)
    unload_duration_min: int
    vehicle_type: str
    destination_zone: str
    priority: int = 1
    requires_crane: bool = False

class SiteLogisticsModel:
    """Construction site logistics model"""

    def __init__(self, site_name: str):
        self.site_name = site_name
        self.zones: Dict[str, SiteZone] = {}
        self.equipment: Dict[str, Equipment] = {}
        self.deliveries: List[DeliveryRequest] = []
        self.routes: Dict[str, List[Tuple[float, float]]] = {}

    def add_zone(self, zone: SiteZone):
        """Add zone to site"""
        self.zones[zone.zone_id] = zone

    def add_equipment(self, equipment: Equipment):
        """Add equipment to site"""
        self.equipment[equipment.equipment_id] = equipment

    def add_delivery(self, delivery: DeliveryRequest):
        """Add delivery request"""
        self.deliveries.append(delivery)

    def calculate_distance(self, point1: Tuple[float, float],
                          point2: Tuple[float, float]) -> float:
        """Calculate Euclidean distance"""
        return np.sqrt((point1[0] - point2[0])**2 + (point1[1] - point2[1])**2)

    def get_zone_distances(self) -> Dict[Tuple[str, str], float]:
        """Calculate distances between all zones"""
        distances = {}
        zone_ids = list(self.zones.keys())

        for i, z1 in enumerate(zone_ids):
            for z2 in zone_ids[i+1:]:
                dist = self.calculate_distance(
                    self.zones[z1].position,
                    self.zones[z2].position
                )
                distances[(z1, z2)] = dist
                distances[(z2, z1)] = dist

        return distances

    def check_crane_coverage(self, crane_id: str, zone_id: str) -> bool:
        """Check if crane can reach zone"""
        crane = self.equipment.get(crane_id)
        zone = self.zones.get(zone_id)

        if not crane or not zone:
            return False

        distance = self.calculate_distance(crane.position, zone.position)
        return distance <= crane.max_reach

Delivery Scheduling Optimizer

from datetime import datetime, date, timedelta
from typing import List, Dict, Optional
import numpy as np

@dataclass
class ScheduledDelivery:
    delivery: DeliveryRequest
    scheduled_date: date
    scheduled_time: datetime
    assigned_bay: str
    assigned_crane: Optional[str]
    estimated_completion: datetime

class DeliveryScheduler:
    """Optimize delivery scheduling"""

    def __init__(self, site: SiteLogisticsModel):
        self.site = site
        self.schedule: Dict[date, List[ScheduledDelivery]] = {}
        self.bay_capacity = 2  # Simultaneous unloading bays
        self.working_hours = (7, 18)  # 7 AM to 6 PM

    def schedule_deliveries(self, deliveries: List[DeliveryRequest],
                           planning_horizon_days: int = 14) -> List[ScheduledDelivery]:
        """Schedule all deliveries optimally"""
        # Sort by priority and required date
        sorted_deliveries = sorted(
            deliveries,
            key=lambda d: (d.priority, d.required_date, -d.quantity)
        )

        scheduled = []
        bay_schedules = {f"bay_{i}": [] for i in range(self.bay_capacity)}

        for delivery in sorted_deliveries:
            best_slot = self._find_best_slot(delivery, bay_schedules)

            if best_slot:
                sched = ScheduledDelivery(
                    delivery=delivery,
                    scheduled_date=best_slot['date'],
                    scheduled_time=best_slot['start_time'],
                    assigned_bay=best_slot['bay'],
                    assigned_crane=best_slot.get('crane'),
                    estimated_completion=best_slot['end_time']
                )
                scheduled.append(sched)