Stochastic Decision Optimization based on Deterministic Approximations of Processes described as Closed-form Arithmetic Simulation

Download paper here

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.

Recommended citation: M. Krishnamoorthy, A. Brodsky, and D. Menase, Stochastic Decision Optimization based on Deterministic Approximations of Processes described as Closed-form Arithmetic Simulation. In the Journal of Decision Systems. May 2018.