需求信息部分已知下生鲜品的低碳订购策略研究

Study of Low-carbon Ordering Strategies of Fresh Products with Partial Demand Information

为了研究需求信息缺失和低碳减排下的生鲜品企业运营决策问题,基于单周期随机库存系统,分别建立受碳限额与交易政策规制和无碳约束的分布式鲁棒优化模型,通过极大极小期望利润准则和最优化方法求解出两种情形下生鲜品的最优订购量,并运用数值算例检验需求随机因子的标准差系数和碳排放权交易价格对生鲜品订购量、利润和碳排放的影响。结果表明,存在唯一的最优库存因子使得生鲜品企业在最坏分布下的期望利润取得最大;与不受碳政策规制情形相比,生鲜品企业在碳限额与交易政策下能够实现高利润和低排放;需求信息缺失对生鲜品企业在碳限额与交易政策下期望利润的影响小于不受碳政策规制情形。

In order to study the operational strategies of fresh products in the stochastic demand environment considering incomplete information and carbon emission reduction factors, two distributionally robust optimization models for the case under cap-and-trade regulation and that without considering carbon emission were formulated, respectively, based on the single period stochastic inventory system. The optimal ordering quantities were solved for two models through max-min expected profit criterion and optimization method. The impacts of the standard deviation coefficient and the trading price of unit carbon permit on the order quantity of fresh products, profit and carbon emissions were verified by conducting numerical analysis. The results show that the optimal storage level is determined uniquely to maximize the expected profit of fresh products in the worst distribution case. When compared with the case without considering carbon regulation, cap-andtrade regulation can lead the retailer to achieve higher profit and lower carbon emissions. Moreover, the partial information of the demand has higher effects on the profit under cap-and-trade regulation than that without considering carbon emission.