Maximilian SCHIFFER

Associate Professor

Information Systems and Operations Management

Biography

Maximilian Schiffer is an Associate Professor in the Information Systems and Operations Management department at HEC Paris and holds the Hi! PARIS Chair in “Artificial Intelligence for Decision Making in Business” at HEC Paris, supported by the France 2030 AI Cluster programme. Prior to joining HEC, he was a tenured Professor at the Technical University of Munich (TUM). He received his Ph.D. in Operations Research from RWTH Aachen University in 2017 and subsequently held a visiting postdoctoral scholar position at Stanford University.

His research operates at the intersection of artificial intelligence, machine learning, and operations research. Specifically, his work focuses on Decision-Focused Learning, Structured Reinforcement Learning, and stochastic optimization, developing methodologies for high-impact business decision-making in operations, supply chains, and transportation. He regularly publishes in top-tier Operations Research and Management Science journals, including Management Science, Operations Research, Mathematical Programming, and Production and Operations Management. Additionally, his work frequently appears at leading machine learning conferences such as NeurIPS, ICML, ICLR, and AAAI.

Maximilian's contributions to the field have been recognized with numerous honors, including the INFORMS TSL Dissertation Prize , the GOR Doctoral Dissertation Prize , and the Paper of the Year 2024 Award from INFORMS Transportation Science. He is also a frequent speaker at leading international venues, having delivered among others the opening keynote at ROADEF 2025 in Paris , as well as invited keynotes at LION , the AAAI AI4Opt Workshop , and a Pierce Seminar at Massachusetts Institute of Technology

Filter

Scientific articles

Optimization-augmented machine learning for vehicle operations in emergency medical services

European Journal of Operational Research, Forthcoming, (in coll. with M. Rautenstrauß)

Analyzing the potentials of car-free zones in Munich

European Journal of Operational Research, Forthcoming, (in coll. with V. Stadnichuk, G. Walther)

Contextual Stochastic Vehicle Routing with Time Windows

INFORMS Journal on Computing, Forthcoming, (in coll. with B. Serrano, A. M. Florio, S. Minner, T. Vidal)

A decomposition-based approach for large-scale pickup and delivery problems

European Journal of Operational Research, September 2026, vol. 333, n° 3, pp 746-761, (in coll. with G. Hiermann)

Mitigating retail platform externalities via inter-supplier returns

European Journal of Operational Research, May 2026, vol. 331, n° 1, pp 92-107, (in coll. with C. J. Liepold, P. Amorim)

Dynamic capacity allocation of hybrid transportation units for cargo-hitching in urban public transportation systems

Transportation Research Part B: Methodological, April 2026, vol. 206, n° 103412, (in coll. with P. Bischoff, B. Lienkamp, T. Rambha)

A Note on Piecewise Affine Decision Rules for Robust, Stochastic, and Data-Driven Optimization

Operations Research, July - August 2026, vol. 74, n° 4, pp 2075-2087, (in coll. with S. Thomä, W. Wiesemann)

Reproducibility in the Control of Autonomous Mobility-on-Demand Systems

IEEE Transactions on Robotics, 2026, vol. 42, pp 1428-1447, (in coll. with X. Li, M. Alharbi, D. Gammelli, J. Harrison, F. Rodrigues, M. Pavone, E. Frazzoli, J. Zhao, G. Zardini)

Learning-Based Online Optimization for Autonomous Mobility-on-Demand Fleet Control

INFORMS Journal on Computing, May - June 2026, vol. 38, n° 3, pp 745-765, (in coll. with K. Jungel, A. Parmentier, T. Vidal)

Ambulance demand prediction via convolutional neural networks

Operations Research, Data Analytics and Logistics, June 2026, vol. 46, n° 200497, (in coll. with M. Rautenstrauss)

Proceedings

Structured Reinforcement Learning for Combinatorial Decision-Making

39th annual conference on Neural Information Processing Systems , 2025 (H. HOPPE, L. BATY, L. BOUVIER, A. PARMENTIER)

Enhancement of Vendor-Managed Inventory Planning Through Deep Reinforcement Learning

2024 Winter Simulation Conference (WSC) , 2024 , Orlando (M. Ratusny, J. H. Kim, H. Sekiya, H. Ehm)

Working papers

Dynamic Fee-Setting under Uncertainty in Two-Sided Marketplaces

SSRN Electronic Journal , 2025