Jan A. Van Mieghem

About

Jan A. Van Mieghem is a leading scholar in operations management, with a primary focus on supply chain dynamics, inventory theory, and the application of advanced analytics to operational decision-making. His most influential recent work investigates the transformative potential of deep reinforcement learning (DRL) in inventory management. In his highly cited 2022 paper (172 citations), Van Mieghem systematically benchmarks DRL against traditional heuristics on challenging problems, including lost sales, dual-sourcing, and multi-echelon systems. He demonstrates that while DRL can match or outperform conventional methods in complex, non-stationary environments, its success is highly dependent on problem structure and implementation. This work provides a rigorous, practical roadmap for researchers and firms exploring AI-driven supply chain solutions. His earlier 2018 study (38 citations) laid the groundwork by focusing specifically on dual-sourcing problems, showing how DRL can dynamically allocate orders between reliable and cheaper but uncertain suppliers. Beyond these contributions, Van Mieghem is known for his work on capacity investment, service operations, and the interface of operations and finance. His research is distinguished by its analytical depth and direct relevance to industry, making him a key figure in bridging cutting-edge machine learning with classic operations challenges.

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