GOALI: Machine Learning Approaches for Supply Chain Decision-Making

供应链是指将商品从生产地运送到消费地的系统。这个系统包括制造、装配、仓储、运输和零售过程。该项目将研究如何通过使用新颖的机器学习技术来自动控制供应链，从而提高供应链的效率和鲁棒性。该项目特别侧重于制定决策策略，以应对供应链系统中的不确定性和纠正中断。该项目将与工业伙伴西门子合作，重点关注与放射性药物（健康诊断的重要组成部分）的生产和分销有关的真正供应链应用。该项目带来的供应链和机器学习方面的进步有可能使广泛的行业受益。本项目研究了一种使用机器学习（ML）作为优化、分析和控制供应链的工具的新方法。当前的供应链操作方法对供应链中不确定因素的统计分布进行假设，并使用预测技术或预测来估计表征这些分布所需的少数参数。然后将这些估计的分布用于供应链的分析和控制。该项目采用了一种新颖的方法，将数据分析和供应链优化阶段结合到单个ML算法中。该项目侧重于两类主要的供应链问题：放射性药物的生产和分销（工业合作伙伴西门子的核心问题），以及随机供应链的早期预警和纠正行动。此外，该项目将为机器学习算法的理论和实现做出方法学上的贡献，包括新的损失函数、使用深度学习作为强化学习预处理步骤的技术、处理非平稳数据的自适应策略，以及改进的机器学习初始化方法。待开发的工具和概念将在供应链内外得到更广泛的推广。

Supply chain refers to the system that moves goods from where they are produced to where they are consumed. This system includes manufacturing, assembly, warehousing, transportation, and retailing processes. This project will study how to improve efficiency and robustness of the supply chain by using novel machine learning techniques to control the supply chain automatically. The project specifically focuses on the development of decision-making strategies to deal with uncertainties and correct disruptions in the supply chain system. In collaboration with an industrial partner, Siemens, the project will focus on a real supply chain application related to the production and distribution of radiopharmaceuticals (an important component in health diagnostics). The advancements in supply chain and machine learning resulting from this project have potential to benefit a wide range of industries. This project studies a new approach for using machine learning (ML) as a tool for optimizing, analyzing and controlling supply chains. Current approach to supply chain operations makes assumptions about statistical distributions of uncertain factors in supply chain and uses predictive techniques or forecasts to estimate the few parameters required to characterize these distributions. These estimated distributions are then used in the analysis and control of the supply chain. This project has a novel approach of combining the data-analysis and supply-chain optimization stages into a single ML algorithm. The project focuses on two main classes of supply chain problems: production and distribution of radiopharmaceuticals (a core problem for the industrial partner, Siemens), and early warnings and corrective actions for stochastic supply chains. In addition, the project will make methodological contributions to the theory and implementation of ML algorithms, including new loss functions, techniques for using deep learning as a preprocessing step for reinforcement learning, adaptive strategies to handle non-stationary data, and improved initialization methods for ML. The tools and concepts to be develop will be generalizable more broadly, both within and beyond supply chain.