Logistics Expert

from dataclasses import dataclassfrom typing import List, Optionalfrom datetime import datetimefrom enum import Enum
class StorageType(Enum):    PALLET = "pallet"    SHELF = "shelf"    BULK = "bulk"    COLD = "cold_storage"
@dataclassclass Location:    location_id: str    zone: str    aisle: str    rack: str    level: int    storage_type: StorageType    capacity: float    current_load: float
@dataclassclass Product:    sku: str    name: str    category: str    weight: float    volume: float    storage_requirements: str
@dataclassclass InventoryItem:    item_id: str    sku: str    quantity: int    location_id: str    received_date: datetime    expiry_date: Optional[datetime]    batch_number: str
class WMS:    """Warehouse Management System"""
    def __init__(self, db):        self.db = db
    def receive_shipment(self, shipment):        """Process incoming shipment"""        items_received = []
        for item in shipment.items:            # Find optimal storage location            location = self.find_optimal_location(item)
            # Create inventory record            inventory_item = InventoryItem(                item_id=generate_id(),                sku=item.sku,                quantity=item.quantity,                location_id=location.location_id,                received_date=datetime.now(),                expiry_date=item.expiry_date,                batch_number=item.batch_number            )
            self.db.save_inventory(inventory_item)            self.update_location_capacity(location, item)
            items_received.append(inventory_item)
        return {            'shipment_id': shipment.shipment_id,            'items_received': len(items_received),            'status': 'completed'        }
    def find_optimal_location(self, item):        """Find best storage location for item"""        product = self.db.get_product(item.sku)        available_locations = self.db.get_available_locations(            storage_type=product.storage_requirements,            min_capacity=product.volume * item.quantity        )
        # Prioritize locations        # 1. Same SKU for efficient picking        # 2. Closest to shipping area for fast-moving items        # 3. Maximize space utilization
        same_sku_locations = [            loc for loc in available_locations            if self.has_same_sku(loc, item.sku)        ]
        if same_sku_locations:            return same_sku_locations[0]
        # Select closest to shipping for fast-moving items        if product.category == 'fast-moving':            return min(available_locations, key=lambda l: l.distance_to_shipping)
        # Otherwise, optimize space utilization        return max(available_locations, key=lambda l: l.utilization_score)
    def pick_order(self, order_id):        """Generate picking list and route"""        order = self.db.get_order(order_id)        picking_list = []
        for line_item in order.line_items:            inventory = self.db.find_inventory(                sku=line_item.sku,                quantity=line_item.quantity            )
            picking_list.append({                'sku': line_item.sku,                'quantity': line_item.quantity,                'location': inventory.location_id,                'batch': inventory.batch_number            })
        # Optimize picking route        optimized_route = self.optimize_picking_route(picking_list)
        return {            'order_id': order_id,            'picking_list': optimized_route,            'estimated_time': self.estimate_picking_time(optimized_route)        }
    def optimize_picking_route(self, picking_list):        """Optimize warehouse picking route"""        # Sort by zone, aisle, rack for efficient walking path        sorted_picks = sorted(            picking_list,            key=lambda x: (                self.get_location_zone(x['location']),                self.get_location_aisle(x['location']),                self.get_location_rack(x['location'])            )        )
        return sorted_picks
    def check_stock_level(self, sku):        """Check current stock level"""        total_quantity = self.db.sum_quantity_by_sku(sku)        product = self.db.get_product(sku)
        status = 'normal'        if total_quantity <= product.reorder_point:            status = 'reorder'        elif total_quantity <= product.safety_stock:            status = 'critical'
        return {            'sku': sku,            'quantity': total_quantity,            'status': status,            'reorder_point': product.reorder_point        }

import numpy as npfrom scipy.spatial.distance import cdistfrom ortools.constraint_solver import routing_enums_pb2from ortools.constraint_solver import pywrapcp
class RouteOptimizer:    """Vehicle routing optimization"""
    def optimize_delivery_routes(self, depot, deliveries, vehicles):        """Optimize delivery routes using OR-Tools"""        # Create distance matrix        locations = [depot] + deliveries        distance_matrix = self.create_distance_matrix(locations)
        # Create routing model        manager = pywrapcp.RoutingIndexManager(            len(distance_matrix),            len(vehicles),            0  # depot index        )
        routing = pywrapcp.RoutingModel(manager)
        # Distance callback        def distance_callback(from_index, to_index):            from_node = manager.IndexToNode(from_index)            to_node = manager.IndexToNode(to_index)            return distance_matrix[from_node][to_node]
        transit_callback_index = routing.RegisterTransitCallback(distance_callback)        routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)
        # Add capacity constraints        def demand_callback(from_index):            from_node = manager.IndexToNode(from_index)            return deliveries[from_node - 1].weight if from_node > 0 else 0
        demand_callback_index = routing.RegisterUnaryTransitCallback(demand_callback)
        routing.AddDimensionWithVehicleCapacity(            demand_callback_index,            0,  # null capacity slack            [v.capacity for v in vehicles],            True,  # start cumul to zero            'Capacity'        )
        # Solve        search_parameters = pywrapcp.DefaultRoutingSearchParameters()        search_parameters.first_solution_strategy = (            routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC        )
        solution = routing.SolveWithParameters(search_parameters)
        if solution:            return self.extract_routes(manager, routing, solution, locations)
        return None
    def create_distance_matrix(self, locations):        """Create distance matrix from coordinates"""        coords = np.array([(loc.lat, loc.lon) for loc in locations])        return cdist(coords, coords, metric='euclidean')
    def extract_routes(self, manager, routing, solution, locations):        """Extract optimized routes from solution"""        routes = []
        for vehicle_id in range(routing.vehicles()):            route = []            index = routing.Start(vehicle_id)
            while not routing.IsEnd(index):                node = manager.IndexToNode(index)                route.append(locations[node])                index = solution.Value(routing.NextVar(index))
            routes.append({                'vehicle_id': vehicle_id,                'stops': route,                'total_distance': solution.ObjectiveValue()            })
        return routes

@dataclassclass Vehicle:    vehicle_id: str    type: str    capacity_weight: float    capacity_volume: float    fuel_efficiency: float    status: str    current_location: dict    maintenance_due: datetime
class FleetManagement:    """Fleet tracking and management"""
    def __init__(self, db):        self.db = db
    def assign_vehicle(self, delivery):        """Assign optimal vehicle for delivery"""        available_vehicles = self.db.get_available_vehicles(            location=delivery.origin,            min_capacity=delivery.total_weight        )
        # Score vehicles based on:        # - Distance from origin        # - Capacity utilization        # - Fuel efficiency        # - Maintenance status
        best_vehicle = min(            available_vehicles,            key=lambda v: self.calculate_vehicle_score(v, delivery)        )
        return best_vehicle
    def track_vehicle(self, vehicle_id):        """Real-time vehicle tracking"""        vehicle = self.db.get_vehicle(vehicle_id)        current_route = self.db.get_current_route(vehicle_id)
        return {            'vehicle_id': vehicle_id,            'location': vehicle.current_location,            'status': vehicle.status,            'current_delivery': current_route.delivery_id if current_route else None,            'eta': self.calculate_eta(vehicle, current_route) if current_route else None,            'fuel_level': vehicle.fuel_level,            'distance_traveled_today': vehicle.daily_distance        }
    def schedule_maintenance(self, vehicle_id):        """Schedule vehicle maintenance"""        vehicle = self.db.get_vehicle(vehicle_id)        maintenance_history = self.db.get_maintenance_history(vehicle_id)
        # Predictive maintenance based on:        # - Mileage        # - Hours of operation        # - Previous issues        # - Manufacturer schedule
        next_maintenance = self.predict_maintenance_date(            vehicle,            maintenance_history        )
        return {            'vehicle_id': vehicle_id,            'next_maintenance': next_maintenance,            'type': 'preventive',            'estimated_cost': self.estimate_maintenance_cost(vehicle)        }

from sklearn.ensemble import RandomForestRegressorimport pandas as pd
class DemandForecasting:    """Demand forecasting and inventory optimization"""
    def forecast_demand(self, sku, forecast_period_days=30):        """Forecast product demand"""        # Get historical sales data        historical_data = self.db.get_sales_history(sku, days=365)
        # Features: day of week, month, seasonality, promotions        df = pd.DataFrame(historical_data)        df['day_of_week'] = pd.to_datetime(df['date']).dt.dayofweek        df['month'] = pd.to_datetime(df['date']).dt.month        df['is_promotion'] = df['promotion_id'].notna().astype(int)
        X = df[['day_of_week', 'month', 'is_promotion']]        y = df['quantity_sold']
        # Train model        model = RandomForestRegressor(n_estimators=100)        model.fit(X, y)
        # Generate forecast        future_dates = pd.date_range(            start=pd.Timestamp.now(),            periods=forecast_period_days,            freq='D'        )
        forecast_features = pd.DataFrame({            'day_of_week': future_dates.dayofweek,            'month': future_dates.month,            'is_promotion': 0  # Assume no promotions unless specified        })
        predicted_demand = model.predict(forecast_features)
        return {            'sku': sku,            'forecast_period': forecast_period_days,            'predicted_demand': predicted_demand.tolist(),            'total_demand': sum(predicted_demand),            'recommended_order_quantity': self.calculate_order_quantity(                predicted_demand,                sku            )        }
    def calculate_order_quantity(self, forecast, sku):        """Calculate economic order quantity"""        product = self.db.get_product(sku)        total_demand = sum(forecast)
        # EOQ formula        eoq = np.sqrt(            (2 * total_demand * product.order_cost) /            product.holding_cost        )
        return int(eoq)