# Stochastic Optimization Algorithm based on Deterministic Approximations

### Mohan Krishnamoorthy, Alexander Brodsky, Daniel MenascÃ©

#### Abstract

We consider steady-state production processes that have feasibility constraints and metrics of cost and throughput that are stochastic functions of process controls. We propose an efficient stochastic optimization algorithm for the problem of finding process controls that minimize the expectation of cost while satisfying deterministic feasibility constraints and stochastic steady state demand for the output product with a given high probability. The proposed algorithm is based on (1) a series of deterministic approximations to produce a candidate set of near-optimal control settings for the production process, and (2) stochastic simulations on the candidate set using optimal simulation budget allocation methods. We demonstrate the proposed algorithm on a use case of a real-world heat-sink production process that involves contract suppliers and manufacturers as well as unit manufacturing processes of shearing, milling, drilling, and machining, and conduct an experimental study that shows that the proposed algorithm significantly outperforms four popular simulation-based stochastic optimization algorithms.

Download#### Paper Citation

#### in Harvard Style

Krishnamoorthy M., Brodsky A. and MenascÃ© D. (2021). **Stochastic Optimization Algorithm based on Deterministic Approximations**.In *Proceedings of the 10th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,* ISBN 978-989-758-485-5, pages 287-294. DOI: 10.5220/0010343802870294

#### in Bibtex Style

@conference{icores21,

author={Mohan Krishnamoorthy and Alexander Brodsky and Daniel MenascÃ©},

title={Stochastic Optimization Algorithm based on Deterministic Approximations},

booktitle={Proceedings of the 10th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},

year={2021},

pages={287-294},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0010343802870294},

isbn={978-989-758-485-5},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,

TI - Stochastic Optimization Algorithm based on Deterministic Approximations

SN - 978-989-758-485-5

AU - Krishnamoorthy M.

AU - Brodsky A.

AU - MenascÃ© D.

PY - 2021

SP - 287

EP - 294

DO - 10.5220/0010343802870294