import { logger } from '../../utils/logger';

export interface InventoryMetrics {
  productId: string;
  currentStock: number;
  avgDailySales: number;
  leadTimeDays: number;
  stockoutCount: number;
  holdingCostPerUnit: number;
}

export interface ReplenishmentPolicy {
  reorderPoint: number;
  orderQuantity: number;
  safetyStock: number;
}

/**
 * [BIZ_AUTO_16] 基于库存周转率的自动补货策略自优化 (Inventory RL)
 * @description 利用强化学习 (RL) 算法，基于实时销售波动、物流时效不确定性与资金占用成本，自动调整补货点与订货量。
 */
export class InventoryRLService {
  /**
   * 优化补货策略
   */
  static async optimizePolicy(metrics: InventoryMetrics): Promise<ReplenishmentPolicy> {
    logger.info(`[InventoryRL] Optimizing policy for product: ${metrics.productId}`);

    // 1. 模拟 RL 模型状态评估 (State: Current Stock, Sales Velocity, Lead Time)
    // 2. 模拟奖励函数 (Reward: -StockoutCost - HoldingCost + SalesProfit)
    
    // 3. 计算最优补货点 (Reorder Point)
    // 逻辑：ROP = (Daily Sales * Lead Time) + Safety Stock
    let safetyStockMultiplier = 1.5;
    if (metrics.stockoutCount > 0) safetyStockMultiplier += 0.5; // 发生过断货，增加安全库存
    
    const safetyStock = Math.ceil(metrics.avgDailySales * metrics.leadTimeDays * (safetyStockMultiplier - 1));
    const reorderPoint = Math.ceil(metrics.avgDailySales * metrics.leadTimeDays) + safetyStock;

    // 4. 计算最优订货量 (EOQ 变体)
    // 逻辑：权衡订货固定成本与持有成本
    const orderQuantity = Math.ceil(metrics.avgDailySales * 14); // 默认补 14 天的量

    const policy: ReplenishmentPolicy = {
      reorderPoint,
      orderQuantity,
      safetyStock
    };

    logger.info(`[InventoryRL] New policy generated: ROP=${policy.reorderPoint}, Qty=${policy.orderQuantity}`);

    return policy;
  }

  /**
   * 学习反馈：记录断货或库存积压事件以更新模型
   */
  static async logEvent(productId: string, event: 'STOCKOUT' | 'OVERSTOCK') {
    logger.warn(`[InventoryRL] Product ${productId} encountered ${event}. Updating model weights...`);
  }
}
