Title
Multi-Response Simulation Optimization Using Stochastic Genetic Search Within A Goal Programming Framework
Abstract
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 and the variance of the responses. In this way, the search is performed stochastically, and not deterministically like most of the approaches reported in the literature. The goal programming model integrated with the genetic algorithm and the stochastic search present a new approach able to lead a search towards a multi-objective solution.
Publication Date
12-1-2000
Publication Title
Winter Simulation Conference Proceedings
Volume
1
Number of Pages
788-794
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
Copyright Status
Unknown
Socpus ID
0034430158 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/0034430158
STARS Citation
Baesler, Felipe F. and Sepulveda, Jose A., "Multi-Response Simulation Optimization Using Stochastic Genetic Search Within A Goal Programming Framework" (2000). Scopus Export 2000s. 736.
https://stars.library.ucf.edu/scopus2000/736