A Revised Simplex Search Procedure for Stochastic Simulation Response Surface Optimization

Stochastic Trust-Region Response-Surface Method (STRONG) - A New Response-Surface Framework for Simulation Optimization

R surface methodology (RSM) is a widely used method for simulation optimization. Its strategy is to explore small subregions of the decision space in succession instead of attempting to explore the entire decision space in a single attempt. This method is especially suitable for complex stochastic systems where little knowledge is available. Although RSM is popular in practice, its current appl...

Hybrid Probabilistic Search Methods for Simulation Optimization

Discrete-event simulation based optimization is the process of finding the optimum design of a stochastic system when the performance measure(s) could only be estimated via simulation. Randomness in simulation outputs often challenges the correct selection of the optimum. We propose an algorithm that merges Ranking and Selection procedures with a large class of random search methods for continu...

Multi-response Simulation Optimization Using Stochastic Genetic Search within a Goal Programming Framework

This study presents a new approach to solve multi-response simulation optimization problems. This approach integrates a simulation model with a genetic algorithm heuristic and a goal programming model. The genetic algorithm technique offers a very flexible and reliable tool able to search for a solution within a global context. This method was modified to perform the search considering the mean...

A Stochastic Optimization Model for Groundwater Management

A Stochastic Optimization Model for Groundwater Management