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，旨在寻找订货量与再订货点的最优组合，在最大化利润的同时兼顾库存管理中的伦理考量。伦理考量包括风险评估、社会责任、环境可持续性和客户满意度。本研究采用蒙特卡洛模拟（Monte Carlo Simulation，MCS）生成库存物品的需求与提前期分布，进而估算不同库存策略下的潜在利润与风险。本文提出一种混合优化方法，融合高斯过程回归与条件函数，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，以在伦理约束下最大化利润。结果表明：（r，Q）和（p，Q）两种策略均能有效管理随机需求库存，但当需求波动较大时，（r，Q）策略表现更优（利润提升12.73%）。本研究将可量化的风险评估与敏感性分析纳入考量，重点分析需求变化及预期利润百分比偏差。研究结果为供应链分析中的伦理与负责任决策提供了实用参考，有助于提升运营效率与盈利能力。