Adaptive AI Decision Agents for Holistic Supply Chain Optimisation: Merging Societal and Business Objectives

供应链效率是一个全球性的问题，几乎影响到地球上的每一个人。在全球范围内优化供应链效率的新方法和改进方法将对人类产生巨大的好处，降低商品成本，减少浪费和碳排放，增加财富创造。这个问题往往过于复杂，无法在全局层面上进行优化，相反，系统的各个部分被解耦，单独建模和单独优化。该项目旨在研究自适应人工智能决策代理是否可以用于做出有关在供应链中采取哪些行动的有效决策，以更接近全局最优。为了进行这项研究，我们的方法将借鉴强化学习，多目标优化和多代理系统的概念，然后使用几个开源供应链数据集（例如OpenML上可用的数据集）和Peak AI提供的客户供应链活动数据进行验证。

Supply chain efficiency is a global problem that affects almost every person on earth. Having new and improved methods to optimise supply chain efficiency at a global level will be hugely beneficial to humanity, reducing the cost of goods, wastage, and carbon emissions, and increasing wealth generation. The problem is often too complex to optimise at a global level, and instead the parts of the system are decoupled, modelled separately and optimised separately. The project aims to investigate whether adaptive AI decision agents could be used to make competent decisions about which actions to take in a supply chain, to get closer to a global optimum. To conduct this research, our methodology will draw on concepts from reinforcement learning, multi-objective optimization, and multi-agent systems, and then be validated using several open-source supply chain datasets (e.g. as available on OpenML) and customer supply chain activity data provided by Peak AI.