Satyajith Amaran

TL;DR: Simulation optimization (SO) refers to the optimization of an objective function subject to constraints, both of which can be evaluated through a stochastic simulation as discussed by the authors, where discrete or continuous decisions, expensive or cheap simulations, single or multiple outputs, homogeneous or heterogeneous noise, etc.

TL;DR: This document emphasizes the difficulties in simulation optimization as compared to algebraic model-based mathematical programming, makes reference to state-of-the-art algorithms in the field, examines and contrasts the different approaches used, reviews some of the diverse applications that have been tackled by these methods, and speculates on future directions in the fields.

TL;DR: The difficulties in SO as compared to algebraic model-based mathematical programming are emphasized, the different approaches used are examined, some of the diverse applications that have been tackled by these methods are reviewed, and future directions in the field are speculates.

TL;DR: This work addresses the scheduling of a set of plant turnarounds over a medium-term of several months using integer programming formulations and proposes combined robust optimization and stochastic programming formulations to address the problem.

TL;DR: This work investigates a discrete-time mixed-integer linear programming (MILP) model to perform turnaround optimization and proposes general formulations to tackle the problem of choice of sequence and timing of planned periodic turnarounds.