AI Agent在物流优化中的实际案例：从仓配到最后一公里

class WarehouseOptimizationAgent:
    def __init__(self):
        self.inventory_agent = InventoryAgent()
        self.slotting_agent = SlottingAgent()
        self.picking_agent = PickingAgent()
        self.forecast_agent = DemandForecastAgent()
    
    async def optimize_daily(self):
        """每日优化流程"""
        # 1. 需求预测
        demand_forecast = await self.forecast_agent.predict(
            horizon=7,  # 未来7天
            granularity="SKU",
            features=["历史销量", "促销计划", "季节性", "天气"]
        )
        
        # 2. 库位重排（夜间执行）
        slotting_plan = await self.slotting_agent.optimize(
            current_layout=self.warehouse.layout,
            demand_forecast=demand_forecast,
            rules=[
                "高频SKU靠近出口",
                "关联SKU就近放置",
                "重物放低层",
                "热销品分散防拥堵"
            ]
        )
        
        # 3. 批量拣货路径优化
        picking_plan = await self.picking_agent.batch_optimize(
            orders=today_orders,
            strategy="wave_picking",  # 波次拣货
            batch_size=30,
            optimization_target="min_total_distance"
        )
        
        return slotting_plan, picking_plan

class PickingPathOptimizer:
    async def optimize_batch(self, orders, batch_size):
        """批量拣货路径优化"""
        # 1. 订单聚类——将库位相近的订单分到同一批次
        batches = self.cluster_orders(
            orders=orders,
            method="kmeans",
            features=[order.item_locations for order in orders],
            k=len(orders) // batch_size
        )
        
        # 2. 每个批次内路径优化（TSP问题）
        for batch in batches:
            # 蚂蚁算法求解近似最优路径
            optimal_path = self.ant_colony_optimization(
                locations=batch.unique_locations,
                start_point=self.warehouse.entrance,
                end_point=self.warehouse.packing_station,
                constraints=[
                    "通道单向通行",
                    "叉车避让",
                    "冷冻区时间限制"
                ]
            )
            batch.path = optimal_path
        
        return batches

class TransportOptimizationAgent:
    async def optimize_routes(self, shipments):
        """运输路由优化"""
        # 1. 货物聚合——同一方向的货物合并
        consolidated = self.consolidate_shipments(
            shipments,
            rules={
                "same_direction_angle": 30,  # 方向角差<30度
                "time_window": 6,  # 6小时内可合并
                "weight_capacity": 0.95,  # 不超过95%载重
            }
        )
        
        # 2. 车辆匹配
        for group in consolidated:
            vehicle = await self.match_vehicle(
                total_weight=group.total_weight,
                total_volume=group.total_volume,
                special_requirements=group.special_reqs,  # 冷链/危险品等
                available_vehicles=self.fleet.available()
            )
        
        # 3. 路由优化
        route = await self.optimize_route(
            origin=group.origin,
            destinations=group.destinations,
            constraints={
                "real_time_traffic": await self.get_traffic(),
                "road_restrictions": self.get_restrictions(vehicle),
                "driver_hours_limit": 8,  # 驾驶时长限制
                "delivery_windows": group.delivery_windows,
            },
            optimize_for="min_cost"  # 或 min_time / min_distance
        )
        
        return route

class DynamicRouter:
    async def monitor_and_adjust(self, active_routes):
        """实时监控并调整路由"""
        for route in active_routes:
            # 检查是否需要重新规划
            if await self.needs_rerouting(route):
                new_route = await self.replan(route)
                
                # 评估新路由是否值得切换
                time_saved = route.eta - new_route.eta
                if time_saved > 30 * 60:  # 节省>30分钟才切换
                    await self.dispatch_update(route.driver, new_route)
                    self.log_reroute(route, new_route, reason)
    
    async def needs_rerouting(self, route):
        """判断是否需要重新规划"""
        # 1. 路况变化
        current_traffic = await self.get_traffic(route.path)
        if current_traffic.congestion_level > 0.7:
            return True
        
        # 2. 新增订单
        if route.has_new_pickup:
            return True
        
        # 3. 天气变化
        weather = await self.get_weather(route.path)
        if weather.severity > 0.6:
            return True
        
        return False

class LastMileDispatchAgent:
    async def optimize(self, deliveries):
        """最后一公里配送优化"""
        
        # 1. 配送区域聚类
        clusters = self.cluster_deliveries(
            deliveries,
            method="DBSCAN",  # 基于密度的聚类
            eps=500,  # 500米半径
            min_samples=5
        )
        
        # 2. 时间窗优化
        for cluster in clusters:
            # 预测客户在家概率
            for delivery in cluster:
                delivery.home_probability = await self.predict_at_home(
                    customer_id=delivery.customer_id,
                    time_slot=delivery.requested_window,
                    history=delivery.customer_history
                )
            
            # 按在家概率排序，优化配送顺序
            cluster.optimized_order = self.optimize_with_time_windows(
                cluster.deliveries,
                vehicle_capacity=150,  # 件
                max_work_hours=8,
                traffic_factor=await self.get_traffic()
            )
        
        # 3. 配送员分配
        assignments = self.assign_couriers(
            clusters=clusters,
            couriers=self.available_couriers(),
            constraints={
                "skill_match": True,  # 大件需有搬运能力的配送员
                "area_familiarity": True,  # 优先分配熟悉区域的配送员
                "workload_balance": True  # 工作量均衡
            }
        )
        
        return assignments

class SmartAppointmentAgent:
    async def suggest_time_slots(self, customer_id, address):
        """智能推荐配送时间窗"""
        # 1. 预测客户偏好
        preference = await self.analyze_preference(customer_id)
        # e.g., 此客户历史上85%选择工作日晚18-20点
        
        # 2. 配送路线可行性
        nearby_deliveries = await self.get_nearby_deliveries(address, radius=2)
        feasible_slots = []
        for slot in self.all_time_slots:
            route_efficiency = self.assess_route(
                address, nearby_deliveries, slot
            )
            if route_efficiency > 0.7:
                feasible_slots.append((slot, route_efficiency))
        
        # 3. 综合推荐
        recommendations = []
        for slot, efficiency in feasible_slots:
            score = (
                preference.get(slot, 0) * 0.6 +  # 客户偏好权重
                efficiency * 0.4                    # 路线效率权重
            )
            recommendations.append((slot, score))
        
        recommendations.sort(key=lambda x: x[1], reverse=True)
        return recommendations[:3]  # 推荐前3个时间窗

class LogisticsMultiAgentSystem:
    def __init__(self):
        self.warehouse_agent = WarehouseOptimizationAgent()
        self.transport_agent = TransportOptimizationAgent()
        self.lastmile_agent = LastMileDispatchAgent()
        self.inventory_agent = InventoryAgent()
    
    async def daily_optimization(self):
        """全链路日优化"""
        # 1. 库存Agent预测各仓需求
        demand = await self.inventory_agent.forecast_demand()
        
        # 2. 仓储Agent根据需求优化库位
        await self.warehouse_agent.optimize(demand)
        
        # 3. 运输Agent规划仓间调拨
        transfers = await self.transport_agent.plan_transfers(demand)
        
        # 4. 最后一公里Agent优化配送
        await self.lastmile_agent.optimize(today_deliveries)
        
        # 5. 协同优化——信息共享
        # 仓库知道运输到达时间，提前准备卸货月台
        # 运输知道仓库拣货进度，动态调整到达时间
        # 配送知道运输状态，提前通知客户