To determine the effectiveness of metaheuristic Differential Evolution
optimization strategy for inventory management (IM) in the context of
stochastic demand, this empirical study undertakes a thorough investigation.
The primary objective is to discern the most effective strategy for minimizing
inventory costs within the context of uncertain demand patterns. Inventory
costs refer to the expenses associated with holding and managing inventory
within a business. The approach combines a continuous review of IM policies
with a Monte Carlo Simulation (MCS). To find the optimal solution, the study
focuses on meta-heuristic approaches and compares multiple algorithms. The
outcomes reveal that the Differential Evolution (DE) algorithm outperforms its
counterparts in optimizing IM. To fine-tune the parameters, the study employs
the Latin Hypercube Sampling (LHS) statistical method. To determine the final
solution, a method is employed in this study which combines the outcomes of
multiple independent DE optimizations, each initiated with different random
initial conditions. This approach introduces a novel and promising dimension to
the field of inventory management, offering potential enhancements in
performance and cost efficiency, especially in the presence of stochastic
demand patterns.

本实证研究旨在确定元启发式差分进化优化策略在随机需求背景下的库存管理领域的有效性，并通过深入调查揭示最有效的策略以最小化不确定需求模式下的库存成本。结果显示，差分进化算法在优化库存管理方面优于其他算法，同时探讨了拉丁超立方抽样等参数调优方法。该研究引入了一种新颖且有前景的方法，为库存管理领域带来了潜在的性能和成本效益的提升，特别是在存在随机需求模式的情况下。

应对库存管理中需求的不确定性和变异性的多个独立差分进化优化

Multiple Independent DE Optimizations to Tackle Uncertainty and  Variability in Demand in Inventory Management

Most existing literature on supply chain and inventory management consider
stochastic demand processes with zero or constant lead times. While it is true
that in certain niche scenarios, uncertainty in lead times can be ignored, most
real-world scenarios exhibit stochasticity in lead times. These random
fluctuations can be caused due to uncertainty in arrival of raw materials at
the manufacturer's end, delay in transportation, an unforeseen surge in
demands, and switching to a different vendor, to name a few. Stochasticity in
lead times is known to severely degrade the performance in an inventory
management system, and it is only fair to abridge this gap in supply chain
system through a principled approach. Motivated by the recently introduced
delay-resolved deep Q-learning (DRDQN) algorithm, this paper develops a
reinforcement learning based paradigm for handling uncertainty in lead times
(\emph{action delay}). Through empirical evaluations, it is further shown that
the inventory management with uncertain lead times is not only equivalent to
that of delay in information sharing across multiple echelons
(\emph{observation delay}), a model trained to handle one kind of delay is
capable to handle delays of another kind without requiring to be retrained.
Finally, we apply the delay-resolved framework to scenarios comprising of
multiple products subjected to stochasticity in lead times, and elucidate how
the delay-resolved framework negates the effect of any delay to achieve
near-optimal performance.

本文考虑供应链中存在的交货期不确定性和因此导致的库存管理性能下降问题，提出了一种基于强化学习的延迟解决方法，并在多种不确定性的情景下进行了实证评估，证明该方法可以实现接近最优的性能。