Stochastic Decision Optimization based on Deterministic Approximations of Processes described as Closed-form Arithmetic Simulation
Published in IFIP WG 8.3 on Decision Support Systems, 2018
Recommended citation: M. Krishnamoorthy, A. Brodsky, and D. Menasce, Stochastic Decision Optimization based on Deterministic Approximations of Processes described as Closed-form Arithmetic Simulation. In Proceedings of the IFIP WG 8.3 on Decision Support Systems, June 2018. Best Paper Award. https://doi.org/10.1080/12460125.2018.1468174
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. Menasce, Stochastic Decision Optimization based on Deterministic Approximations of Processes described as Closed-form Arithmetic Simulation. In Proceedings of the IFIP WG 8.3 on Decision Support Systems, June 2018. Best Paper Award.