Joren Gijsbrechts

About

Joren Gijsbrechts is a leading researcher at the intersection of operations management and artificial intelligence, whose work is reshaping how supply chains leverage machine learning. His primary research areas include inventory management, deep reinforcement learning (DRL), and data-driven decision-making in complex supply chain networks. Gijsbrechts is best known for his pioneering investigation into whether DRL can outperform traditional optimization methods in inventory control. His most cited work, a 2022 paper in *Manufacturing & Service Operations Management* (172 citations), provides a rigorous benchmark of DRL against classic heuristics on lost sales, dual-sourcing, and multi-echelon problems, demonstrating both its potential and limitations. This built on his earlier 2018 study (38 citations) that specifically explored DRL’s performance on dual sourcing-mode problems. By systematically testing DRL in realistic, stochastic environments, Gijsbrechts has provided crucial guidance for both academics and practitioners seeking to implement AI in supply chains. His contributions are notable for bridging the gap between cutting-edge AI techniques and practical inventory challenges, making him a key voice in the ongoing digital transformation of operations research.

Research Focus

Key Achievements

Top Papers

1. 1
   Can Deep Reinforcement Learning Improve Inventory Management? Performance on Lost Sales, Dual-Sourcing, and Multi-Echelon Problems

   172 citations · 2022
2. 2
   Can Deep Reinforcement Learning Improve Inventory Management? Performance and Implementation of Dual Sourcing-Mode Problems

   38 citations · 2018

Key Collaborators

Contact & Links