Stochastic Reactive Production Scheduling by Multi-agent Based Asynchronous Approximate Dynamic Programming

The paper investigates a stochastic production scheduling problem with unrelated parallel machines. A closed-loop scheduling technique is presented that on-line controls the production process. To achieve this, the scheduling problem is reformulated as a special Markov Decision Process. A near-optimal control policy of the resulted MDP is calculated in a homogeneous multi-agent system. Each agent applies a trial-based approximate dynamic programming method. Different cooperation techniques to distribute the value function computation among the agents are described. Finally, some benchmark experimental results