A simulation-optimization approach using genetic algorithm for dynamic Job Shop scheduling with stochastic demand

Abstract

The Job Shop scheduling problem has been extensively studied in the last decades due to its importance
and computational complexity. However, a large part of the papers address the static and deterministic
version of the problem. This work presents an integration of a simulation model with an optimization
method in order to solve the dynamic job shop scheduling problem. The proposed model integration is
accomplished using out-of-process components, through the ActiveX Automation technology and the
Visual Basic for Application, in which a simple Genetic Algorithm runs as a free-standing application.
Results of the proposed method were compared with some common dispatching rules and showed that
the proposed approach the scheduling problem efficiently. Moreover, the solutions generated by GA are
less sensitive to variations in demand, which is quite significant in such environments.