供应链多目标约束感知行动规划 — MILP+LLM的Pareto最优决策方案自动生成

"""
供应链多目标约束感知行动规划
功能：MILP建模 / Pareto前沿计算 / LLM约束解析 / Palantir Action生成
输入：SKU列表 + 供应商数据 + 业务约束（自然语言）
输出：Pareto最优方案 + 权衡分析 + Palantir Action推荐
"""
import numpy as np
from dataclasses import dataclass, field
from typing import Optional
import warnings
warnings.filterwarnings('ignore')


@dataclass
class SupplyPlanSolution:
    """供应规划解——直接映射到Palantir Action参数"""
    solution_id: str
    weights: dict           # 目标权重
    allocations: dict       # {(sku_id, supplier_id): quantity}
    total_cost: float
    service_level: float
    risk_score: float
    objective_value: float
    
    def to_palantir_actions(self) -> list:
        """转换为Palantir Action列表"""
        actions = []
        for (sku_id, supplier_id), qty in self.allocations.items():
            if qty > 0:
                actions.append({
                    "action_type": "CreatePurchaseOrder",
                    "parameters": {
                        "sku_id": sku_id,
                        "supplier_id": supplier_id,
                        "quantity": round(qty, 0),
                        "solution_id": self.solution_id,
                        "expected_service_level": self.service_level,
                    }
                })
        return actions


class SupplyChainMOOPlanner:
    """
    供应链多目标优化规划器
    使用加权和法探索Pareto前沿
    """
    
    def __init__(self, skus: list, suppliers: list):
        self.skus = skus          # [{id, demand, moq}]
        self.suppliers = suppliers # [{id, capacity, risk_score, price_by_sku}]
    
    def _compute_plan_metrics(self, allocation: dict,
                               demand_dict: dict, budget: float) -> dict:
        """计算规划方案的各项指标"""
        total_cost = 0
        total_demand = sum(demand_dict.values())
        total_fulfilled = 0
        total_risk = 0
        active_suppliers = set()