Arc-consistency for non-binary dynamic CSPs
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Adaptive Inventory Control for Nonstationary Demand and Partial Information
Management Science
Solving Distributed Constraint Optimization Problems Using Cooperative Mediation
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Towards adjustable autonomy for the real world
Journal of Artificial Intelligence Research
Computers & Mathematics with Applications
Fuzzy decision support system for demand forecasting with a learning mechanism
Fuzzy Sets and Systems
Agent-based simulation of competitive and collaborative mechanisms for mobile service chains
Information Sciences: an International Journal
Behaviour adaptation in the multi-agent, multi-objective and multi-role supply chain
Computers in Industry
Journal of Intelligent Manufacturing
Characterizing multi-event disaster resilience
Computers and Operations Research
Hi-index | 0.01 |
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.