Wayan F. Mahmudy, Romeo M. Marian, Lee H.S. Luong
This paper addresses optimization of the flexible job-shop problem (FJSP) by using realcoded genetic algorithms (RCGA) that use an array of real numbers as chromosome representation. The first part of the papers has detailed the modelling of the problems and showed how the novel chromosome representation can be decoded into solution. This second part discusses the effectiveness of each genetic operator and how to determine proper values of the RCGA's parameters. These parameters are used by the RCGA to solve several test bed problems. The experimental results show that by using only simple genetic operators and random initial population, the proposed RCGA can produce promising results comparable to those achieved by other bestknown approaches in the literatures. These results demonstrate the robustness of the RCGA. © (2013) Trans Tech Publications, Switzerland.
School of Advanced Manufacturing and Mechanical Engineering, University of South Australia, Australia; Department of Computer Science, Brawijaya University (UB), Indonesia
