No-Regret Learning in Two-Echelon Supply Chain with Unknown Demand Distribution

Mengxiao Zhang, Shi Chen, Haipeng Luo, Yingfei Wang
Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, PMLR 206:3270-3298, 2023.

Abstract

Supply chain management (SCM) has been recognized as an important discipline with applications to many industries, where the two-echelon stochastic inventory model, involving one downstream retailer and one upstream supplier, plays a fundamental role for developing firms’ SCM strategies. In this work, we aim at designing online learning algorithms for this problem with an unknown demand distribution, which brings distinct features as compared to classic online convex optimization problems. Specifically, we consider the two-echelon supply chain model introduced in [Cachon and Zipkin, 1999] under two different settings: the centralized setting, where a planner decides both agents’ strategy simultaneously, and the decentralized setting, where two agents decide their strategy independently and selfishly. We design algorithms that achieve favorable guarantees for both regret and convergence to the optimal inventory decision in both settings, and additionally for individual regret in the decentralized setting. Our algorithms are based on Online Gradient Descent and Online Newton Step, together with several new ingredients specifically designed for our problem. We also implement our algorithms and show their empirical effectiveness.

Cite this Paper

BibTeX
@InProceedings{pmlr-v206-zhang23e, title = {No-Regret Learning in Two-Echelon Supply Chain with Unknown Demand Distribution}, author = {Zhang, Mengxiao and Chen, Shi and Luo, Haipeng and Wang, Yingfei}, booktitle = {Proceedings of The 26th International Conference on Artificial Intelligence and Statistics}, pages = {3270--3298}, year = {2023}, editor = {Ruiz, Francisco and Dy, Jennifer and van de Meent, Jan-Willem}, volume = {206}, series = {Proceedings of Machine Learning Research}, month = {25--27 Apr}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v206/zhang23e/zhang23e.pdf}, url = {https://proceedings.mlr.press/v206/zhang23e.html}, abstract = {Supply chain management (SCM) has been recognized as an important discipline with applications to many industries, where the two-echelon stochastic inventory model, involving one downstream retailer and one upstream supplier, plays a fundamental role for developing firms’ SCM strategies. In this work, we aim at designing online learning algorithms for this problem with an unknown demand distribution, which brings distinct features as compared to classic online convex optimization problems. Specifically, we consider the two-echelon supply chain model introduced in [Cachon and Zipkin, 1999] under two different settings: the centralized setting, where a planner decides both agents’ strategy simultaneously, and the decentralized setting, where two agents decide their strategy independently and selfishly. We design algorithms that achieve favorable guarantees for both regret and convergence to the optimal inventory decision in both settings, and additionally for individual regret in the decentralized setting. Our algorithms are based on Online Gradient Descent and Online Newton Step, together with several new ingredients specifically designed for our problem. We also implement our algorithms and show their empirical effectiveness.} }
Endnote
%0 Conference Paper %T No-Regret Learning in Two-Echelon Supply Chain with Unknown Demand Distribution %A Mengxiao Zhang %A Shi Chen %A Haipeng Luo %A Yingfei Wang %B Proceedings of The 26th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2023 %E Francisco Ruiz %E Jennifer Dy %E Jan-Willem van de Meent %F pmlr-v206-zhang23e %I PMLR %P 3270--3298 %U https://proceedings.mlr.press/v206/zhang23e.html %V 206 %X Supply chain management (SCM) has been recognized as an important discipline with applications to many industries, where the two-echelon stochastic inventory model, involving one downstream retailer and one upstream supplier, plays a fundamental role for developing firms’ SCM strategies. In this work, we aim at designing online learning algorithms for this problem with an unknown demand distribution, which brings distinct features as compared to classic online convex optimization problems. Specifically, we consider the two-echelon supply chain model introduced in [Cachon and Zipkin, 1999] under two different settings: the centralized setting, where a planner decides both agents’ strategy simultaneously, and the decentralized setting, where two agents decide their strategy independently and selfishly. We design algorithms that achieve favorable guarantees for both regret and convergence to the optimal inventory decision in both settings, and additionally for individual regret in the decentralized setting. Our algorithms are based on Online Gradient Descent and Online Newton Step, together with several new ingredients specifically designed for our problem. We also implement our algorithms and show their empirical effectiveness.
APA
Zhang, M., Chen, S., Luo, H. & Wang, Y.. (2023). No-Regret Learning in Two-Echelon Supply Chain with Unknown Demand Distribution. Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 206:3270-3298 Available from https://proceedings.mlr.press/v206/zhang23e.html.

Related Material