Stochastic decision optimisation based on deterministic approximations of processes described as closed-form arithmetic simulation

We propose an efficient one-stage stochastic optimisation algorithm for the problem of finding process controls that minimise the expectation of cost while satisfying multiple deterministic and stochastic feasibility constraints with a given high probability. The proposed algorithm is based on a series of deterministic approximations to produce a candidate solution set and on a refinement step using stochastic simulations with optimal simulation budget allocation. We conduct an experimental study for a real-world manufacturing service network, which shows that the proposed algorithm significantly outperforms four popular simulation-based stochastic optimisation algorithms.