F.R. Akkerman (Fabian)

DynaPlex

Deep Reinforcement Learning for Data-Driven Logistics

In December 2018, a team of researchers of DeepMind (owned by Google) published a paper in the journal Science, demonstrating the ability of their newly developed AlphaZero algorithm to beat the best game engines in Chess, Go, and Shogi. What is more, instead of relying on hand-crafted evaluation functions of board states, the AlphaZero algorithm contains no expert information on any of the games played: it autonomously learns to play each game only by playing the game many times against itself. This AI breakthrough is exciting because Go and Chess are games where it is crucial to anticipate unknown moves of the opponent. When making logistics decisions, it is equally important to anticipate the arrival of new data (e.g., orders, delays, disruptions, etc.). For various dynamic data-driven decision problems, Deep Reinforcement Learning (DRL) algorithms like AlphaZero have been demonstrated to be game-changers. The logistics sector recognizes the opportunities and is eager to adopt AI for decision automation. However, companies struggle to translate the abstract possibilities of AI into the tangible project plans, and employing DRL-based decision making requires expert algorithmic knowledge that is difficult to source. To overcome these challenges, we started to develop the DynaPlex toolbox. In a similar fashion as AlphaZero was designed as a generic tool to solve various games, we created the DynaPlex toolbox to support the rapid development of automated decision making based on DRL. DynaPlex focuses on dynamic data-driven logistics challenges, with a focus on planning, scheduling, allocation and routing decisions, for example in transportation management, inventory management, and warehouse management.