This study addresses the difficulties associated with inventory management of products with stochastic demand. The objective is to find the optimal combination of order quantity and reorder point that maximizes profit while considering ethical considerations in inventory management. The ethical considerations are risk assessment, social responsibility, environmental sustainability, and customer satisfaction. Monte Carlo simulation (MCS) is used in this study to generate a distribution of demand and lead times for the inventory items, which is then used to estimate the potential profit and risk associated with different inventory policies. This work proposes a hybrid optimization approach combining Gaussian process regression and conditioning function to efficiently search the high-dimensional space of potential continuous review (r, Q) and periodic review (p, Q) values to find the optimal combination that maximizes profit while considering ethical considerations. The findings show that both the (r, Q) and (p, Q) approaches can effectively manage inventory with stochastic demand, but the (r, Q) approach performs better (profits up by 12.73%) when demand is more volatile. The study adds quantifiable risk assessment and sensitivity analysis to these considerations, considering the variation in demand and expected output in profit percentage. The results provide useful information for making ethical and responsible choices in supply chain analytics, boosting efficiency and profits.

翻译：本研究针对随机需求产品的库存管理难题，旨在确定订单数量与再订货点的最优组合，在实现利润最大化的同时兼顾库存管理中的伦理考量。伦理考量涵盖风险评估、社会责任、环境可持续性及客户满意度四个维度。采用蒙特卡洛模拟生成库存品需求与前置时间的概率分布，进而评估不同库存策略的潜在收益与风险。本文提出一种融合高斯过程回归与条件函数的混合优化方法，可高效搜索连续盘点(r, Q)与定期盘点(p, Q)策略的高维参数空间，通过寻找最优组合实现利润最大化并满足伦理约束。研究表明，(r, Q)与(p, Q)两种策略均能有效管理随机需求库存，但当需求波动性增强时，(r, Q)策略表现更优（利润提升12.73%）。本研究将可量化风险评估与灵敏度分析纳入考量体系，系统分析了需求变动与预期产出对利润率的影响。研究结果可为供应链分析中的伦理决策与责任选择提供数据支撑，有效提升运营效率与企业效益。