BAYESIAN JOINT CHANCE CONSTRAINED OPTIMIZATION: APPROXIMATIONS AND STATISTICAL CONSISTENCY

This paper considers data-driven chance-constrained stochastic optimization problems in a Bayesian framework. Bayesian posteriors afford a principled mechanism to incorporate data and prior knowledge into stochastic optimization problems. However, the computation of Bayesian posteriors is typically an intractable problem and has spawned a large literature on approximate Bayesian computation. Here, in the context of chance-constrained optimization, we focus on the question of statistical consistency (in an appropriate sense) of the optimal value, computed using an approximate posterior distribution. To this end, we rigorously prove a frequentist consistency result demonstrating the convergence of the optimal value to the optimal value of a fixed, parameterized constrained optimization problem. We augment this by also establishing a probabilistic rate of convergence of the optimal value. We also prove the convex feasibility of the approximate Bayesian stochastic optimization problem. Finally, we demonstrate the utility of our approach on an optimal staffing problem for an M/M/c queueing model.

Automated summary

This paper investigates data-driven chance-constrained stochastic optimization problems using a Bayesian framework. The study focuses on the statistical consistency and probabilistic rate of convergence of the optimal value obtained using an approximate posterior distribution. The research also establishes the convex feasibility of the approximate Bayesian stochastic optimization problem and demonstrates its utility through a staffing problem for an M/M/c queueing model.