Decentralized asynchronous optimization of stochastic discrete event systems

We propose and analyze decentralized asynchronous control structures for the parametric optimization of stochastic discrete event systems (DES) consisting of K distributed components. We use a stochastic approximation type of optimization scheme driven by gradient estimates of a global performance measure with respect to local control parameters. The estimates are obtained in distributed and asynchronous fashion at the K components based on local state information only. We identify two verifiable conditions for the estimators and show that if they, and some additional technical conditions, are satisfied, the decentralized asynchronous scheme that we propose converges to a global optimum in a weak sense.