Application of learning automata for stochastic online scheduling

Abstract

We look at a stochastic online scheduling problem where exact joblenghts are unknown and jobs arrive over time. Heuristics exist which perform very well, but do not extend to multi-stage problems where all jobs must be processed by a sequence of machines. We apply Learning Automata (LA), a Reinforcement Learning technique, successfully to such a multi-stage scheduling setting. We use a Learning Automaton at each decision point in the production chain. Each Learning Automaton has a probability distribution over the machines it can chose. The difference with simple randomization algorithms is the update rule used by the LA. Whenever a job is finished, the LA are notified and update their probability distribution: if the job was finished faster than expected the probability for selecting the same action is increased, otherwise it is decreased. Due to this adaptation, LA can learn processing capacities of the machines, or more correctly: the entire downstream production chain. © 2010 Springer -Verlag Berlin Heidelberg.