Dynaplex, minimizing disruptions in Dutch logistics

COVID-19 made a huge impact on the logistics sector. The tremendous challenges during the pandemic have shown that supply chains need more resilient processes, which increase the predictability and efficiency. Digital transformation is key to minimize disruptions. 
When making logistics decisions, it is important to anticipate the arrival of new data (e.g., orders, delays, disruptions, etc.).  Deep Reinforcement Learning (DRL) algorithms like AlphaZero (kader) have been demonstrated to be game-changers.  In this research, led by TU Eindhoven, a new Reinforcement Learning Toolbox will be developed. The consortium of academics and industry focusses on both the development and implementation of the AI tool in the Dutch logistics sector.
Two challengesThe research is based on two challenges:

- To develop proofs-of-concept (PoCs) of AI decision automation for the 10 industrial partners. These PoC’s serve as concrete examples of the potential of AI in data-driven logistics.
- To create a toolbox which supports the rapid development of automated decision making based on DRL. DynaPlex focuses on dynamic data-driven logistics challenges, and it is crucial in delivering the PoCs for partner companies, while also supporting decision automation for logistics challenges of companies outside the consortium. 
Highly innovative The researchers set the bar high. They aim to support the modeling of data-driven logistics decision problems in uncertain environments by utilizing real-time information and letting the toolbox optimize these problems, with zero coding. 

Developing and implementing an AI Reinforcement Learning Toolbox