Real-Time Supply Chain Control via Multi-Agent Adjustable Autonomy

• Main Authors: LAU, Hoong Chuin, Agussurja, Lucas, Thangarajoo, Ramesh
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2007
• Subjects: Crisis management; Fuzzy controller; Military warehouse; Multi-agent system; Real-time control; Artificial Intelligence and Robotics; Management Information Systems; Operations and Supply Chain Management
• Online Access: https://ink.library.smu.edu.sg/sis_research/1194; https://ink.library.smu.edu.sg/context/sis_research/article/2193/viewcontent/Real_time_Supply_Chain_Control_preprint.pdf
• Institution: Singapore Management University

Real-time supply chain management in a rapidly changing environment requires reactive and dynamic collaboration among participating entities. In this work, we model supply chain as a multi-agent system where agents are subject to an adjustable autonomy. The autonomy of an agent refers to its capability to make and influence decisions within a multi-agent system. Adjustable autonomy means changing the autonomy of the agents during runtime as a response to changes in the environment. In the context of a supply chain, different entities will have different autonomy levels and objective functions as the environment changes, and the goal is to design a real-time control technique to maintain global consistency and optimality. We propose a centralized fuzzy framework for sensing and translating environmental changes to the changes in autonomy levels and objectives of the agents. In response to the changes, a coalition-formation algorithm will be executed to allow agents to negotiate and re-establish global consistency and optimality. We apply our proposed framework to two supply chain control problems with drastic changes in the environment: one in controlling a military hazardous material storage facility under peace-to-war transition, and the other in supply management during a crisis (such as bird-flu or terrorist attacks). Experimental results show that by adjusting autonomy in response to environmental changes, the behavior of the supply chain system can be controlled accordingly.