Port wine stocks ameliorate during storage, facilitating product differentiation according to age. This induces a trade-off between immediate revenues and further maturation. Varying climate conditions in the limited supply region lead to stochastic purchase prices for wine grapes. Decision makers must integrate recurring purchasing, production, and issuance decisions. Because stocks from different age classes can be blended to create final products, the solution space increases exponentially in the number of age classes. We model the problem of managing port wine inventory as a Markov decision process, considering decay as an additional source of uncertainty. For small problems, we derive general management strategies from the long-run behavior of the optimal policy. Our solution approach for otherwise intractable large problems, therefore, first aggregates age classes to create a tractable problem representation. We then use machine learning to train tree-based decision rules that reproduce the optimal aggregated policy and the enclosed management strategies. The derived rules are scaled back to solve the original problem. Learning from the aggregated optimum outperforms benchmark rules by 21.4% in annual profits (while leaving a 2.8%-gap to an upper bound). For an industry case, we obtain a 17.4%-improvement over current practices. Our research provides distinct strategies for how producers can mitigate climate risks. The purchasing policy dynamically adapts to climate-dependent price fluctuations. Uncertainties are met with lower production of younger products, whereas strategic surpluses of older stocks ensure high production of older products. Moreover, a wide spread in the age classes used for blending reduces decay risk exposure.

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从汇总的最优值中学习：面对气候风险管理波特酒库存


波特酒库存在储存过程中得到改善，有助于根据年龄区分产品。这会导致即时收入和进一步成熟之间的权衡。在有限供应地区，气候条件的变化导致酿酒葡萄的收购价格随机波动。决策者必须整合重复的采购、生产和发行决策。由于可以将来自不同年龄段的库存混合以创建最终产品，因此解决方案空间在年龄段的数量上呈指数级增长。我们将管理波特酒库存的问题建模为马尔可夫决策过程，将衰变视为不确定性的另一个来源。对于小问题，我们从最优策略的长期行为中得出一般的管理策略。因此，我们针对其他棘手的大型问题的解决方案首先聚合年龄类别以创建易于处理的问题表示。然后，我们使用机器学习来训练基于树的决策规则，这些规则可以重现最佳聚合策略和封闭式管理策略。派生规则将按比例缩小以解决原始问题。从聚合的最优值中学习，年利润比基准规则高出 21.4%（同时在上限上留下 2.8% 的差距）。对于行业案例，我们比当前做法提高了 17.4%。我们的研究为生产商如何减轻气候风险提供了独特的策略。采购政策动态适应气候相关的价格波动。年轻产品的产量下降带来了不确定性，而老产品的战略盈余确保了老产品的高产量。此外，用于混合的年龄等级的广泛分布降低了腐烂风险。