AAAI 2024technical0 citations
Coordination of Emergent Demand Changes via Value-Based Negotiation for Supply Chain Management (Student Abstract)
Takumu Shimizu, Ryota Higa, Katsuhide Fujita, Shinji Nakadai
Abstract
We propose an automated negotiation for a reinforcement learning agent to adapt the agent to unexpected situations such as demand changes in supply chain management (SCM). Existing studies that consider reinforcement learning and SCM assume a centralized environment where the coordination of chain components is hierarchical rather than through negotiations between agents. This study focused on a negotiation agent that considered the value function of reinforcement learning for SCM as its utility function in automated negotiation. We demonstrated that the proposed approach could avoid inventory shortages under increased demand requests from the terminal customer.
BibTeX
@article{Shimizu_Higa_Fujita_Nakadai_2024, title={Coordination of Emergent Demand Changes via Value-Based Negotiation for Supply Chain Management (Student Abstract)}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/30510}, DOI={10.1609/aaai.v38i21.30510}, abstractNote={We propose an automated negotiation for a reinforcement learning agent to adapt the agent to unexpected situations such as demand changes in supply chain management (SCM).
Existing studies that consider reinforcement learning and SCM assume a centralized environment where the coordination of chain components is hierarchical rather than through negotiations between agents.
This study focused on a negotiation agent that considered the value function of reinforcement learning for SCM as its utility function in automated negotiation.
We demonstrated that the proposed approach could avoid inventory shortages under increased demand requests from the terminal customer.}, number={21}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Shimizu, Takumu and Higa, Ryota and Fujita, Katsuhide and Nakadai, Shinji}, year={2024}, month={Mar.}, pages={23649-23650} }