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问题，几乎影响到地球上的每一个人。拥有新的和改进的方法来优化全球层面的供应链效率将极大地造福人类，减少商品成本、浪费和碳排放，并增加财富创造。这个问题往往过于复杂，无法在全球层面上进行优化，相反，系统的各个部分是分离的，分别建模，并分别进行优化。该项目旨在调查适应性人工智能决策代理是否可以用于就供应链中采取哪些行动做出称职的决策，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(如OpenML上提供的)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.