Dynamic scheduling for multi-site companies: a decisional approach based on reinforcement multi-agent learning
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 and capacity to make 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 at different points of time will have different autonomy levels. We propose a centralized fuzzy framework for translating the changes in the environment to the changes in autonomy levels of the agents. A coalition-formation algorithm based on the autonomy levels is then applied, allowing the agents to collaborate to cope with the changes while achieving global consistency. Our proposed framework is applied 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.