AI for Supply Chains | Artificial Intelligence Institute for Advances in Optimization

Supply-chain management used to be an arcane topic, discussed by a few and invisible to the general public. This has changed after the pandemic: the public is now aware of a topic that has become top-of-mind in many corporate boards. Supply chains have become larger, and e-commerce has proliferated, imposing significant environmental costs to meet new customer expectations. At the same time, many customers and suppliers, especially in rural regions, face increasing difficulties in procuring or delivering specific products. What is needed is a paradigm change, a new vision for supply chains that complements efficiency with resilience, sustainability, and equity goals. Research in supply chains at AI4OPT is centered around end-to-end supply chains, with scalability, resilience, sustainability, and equity as core challenges. AI4OPT has assembled a consortium of partners that cover (almost) all aspects of supply chains. It leverages novel forecasting methods, optimization proxies, decision making under uncertainty, and automation to meet these challenges.

AI4OPT works with partners on all aspects of end-to-end supply chains, from forecasting to optimization and risk management.

Publications

Funded by the Institute

Constraint Programming to Improve Hub Utilization in Autonomous Transfer Hub Networks. Chungjae Lee, Wirattawut Boonbandansook, Vahid Eghbal Akhlaghi, Kevin Dalmeijer, and Pascal Van Hentenryck. The 29th International Conference on Principles and Practice of Constraint Programming (CP-2023), Toronto, Canada, August 27 - 31, 2023.
Optimization-based Learning for Dynamic Load Planning in Trucking Service Networks. Ritesh Ojha, Wenbo Chen, Hanyu Zhang, Reem Khir, Alan Erera, Pascal Van Hentenryck. ArXiv:2307.04050v1, July 8, 2023.

Related work

Reinforcement Learning from Optimization Proxy for Ride-Hailing Vehicle Relocation. Enpeng Yuan, Wenbo Chen, and Pascal Van Hentenryck. Journal of Artificial Intelligence Research (JAIR), 75, 985-1002, November, 2022.
The Impact of Autonomous Trucking: A Case-Study of Ryder’s Dedicated Transportation Network. Ryder System, Inc. and the Socially Aware Mobility Lab (Georgia Tech)
State Entropy Maximization with Random Encoders for Efficient Exploration,
Younggyo Seo, Lili Chen, Jinwoo Shin, Honglak Lee, Pieter Abbeel, Kimin Lee.
In the proceedings of the International Conference on Machine Learning (ICML), Virtual, July 2021.
arXiv 2102.09430
Spatio-Temporal Point Processes with Attention for Traffic Congestion Event Modeling. Shixiang Zhu, Ruyi Ding, Minghe Zhang, Pascal Van Hentenryck, and Yao Xie. IEEE Transactions on Intelligent Transportation Systems, 2021.
Self-Supervised Policy Adaptation during Deployment,
Nicklas Hansen, Yu Sun, Pieter Abbeel, Alexei A. Efros, Lerrel Pinto, Xiaolong Wang.
In the proceedings of the 7th International Conference on Learning Representations (ICLR), Virtual, April 2021.
arXiv 2007.04309
See the Future through the Void: Active Pre-Training with Successor Features,
Hao Liu, Pieter Abbeel.
In the proceedings of the International Conference on Machine Learning (ICML), Virtual, July 2021.
Mutual Information-based State-Control for Intrinsically Motivated Reinforcement Learning,
Rui Zhao, Yang Gao, Pieter Abbeel, Volker Tresp, Wei Xu. In the proceedings of the 7th International Conference on Learning Representations (ICLR), Virtual, April 2021. arXiv 2103.08107
coupling Representation Learning from Reinforcement Learning,
Adam Stooke, Kimin Lee, Pieter Abbeel, Michael Laskin.
In the proceedings of the International Conference on Machine Learning (ICML), Virtual, July 2021.
arXiv 2009.08319
Constraint Programming Models for Integrated Port Container Terminal Operations. Damla Kizilay, Pascal Van Hentenryck, and Deniz T. Eliiyi. European Journal on Operations Research. Volume 286, Issue 3, Pages 945-962, November 2020.
Real-time dispatching of large-scale ride-sharing systems: Integrating optimization, machine learning, and model predictive control. Connor Riley, Pascal Van Hentenryck, and Enpeng YuanIn Christian Bessiere, editor, Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, pages 4417–4423. ijcai.org, 2020.
Real-time dispatching of large-scale ride-sharing systems: Integrating optimization, machine learning, and model predictive control. Connor Riley, Pascal Van Hentenryck, and Enpeng Yuan. In Christian Bessiere, editor, Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, pages 4417–4423.
Lifelong multi-agent path finding in large-scale warehouses. J. Li, A. Tinka, S. Kiesel, J. Durham, S. Kumar, and S. Koenig. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), pages 1898–1900, 2020.
On-time last mile delivery: Order assignment with travel time predictors. S. Liu, L. He, and Z.J.M. Shen. Management Science, Accepted(1):9–20, 2020.
Task and path planning for multi-agent pickup and delivery. M. Liu, H. Ma, J. Li, and S. Koenig. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), pages 1152–1160, 2019.