The application of the Variance Targeting Estimator (VTE) is considered in GJR-GARCH(1,1) model, under three
misspecification scenarios, which are, model misspecification, initial parameters misspecification and innovation
distribution assumption misspecification. A simulation study has been performed to evaluate the performance of VTE
compared to commonly used, which is the Quasi Maximum Likelihood Estimator (QMLE). The data has been simulated
under GJR-GARCH(1,1) process with initial parameters ω = 0.1, α = 0.05, β = 0.85, γ = 0.1 and an innovation with a
true normal distribution. Three misspecification innovation assumptions, which are normal distribution, Student-t
distribution and the GED distribution have been used. Meanwhile, for the misspecified initial parameters, the first initial
parameters have been setup as ω = 1, α = 0, β = 0 and γ = 0. Furthermore, the application of VTE as an estimator has
also been evaluated under real data sets and three selected indices, which are the FTSE Bursa Malaysia Kuala Lumpur
Index (FBMKLCI), the Singapore Straits Time Index (STI) and the Jakarta Composite Index (JCI). Based on the results, VTE
has performed very well compared to QMLE under both simulation and the applications of real data sets, which can be
considered as an alternative estimator when performing GARCH model, especially the GJR-GARCH.
Preventive maintenance (PM) planning becomes a crucial issue in the real world of the manufacturing process. It is important in the manufacturing industry to maintain the optimum level of production and minimize its investments. Thus, this paper focuses on multiple jobs with a single production line by considering stochastic machine breakdown time. The aim of this paper is to propose a good integration of production and PM schedule that will minimize total completion time. In this study, a hybrid method, which is a genetic algorithm (GA), is used with the Monte Carlo simulation (MCS) technique to deal with the uncertain behavior of machine breakdown time. A deterministic model is adopted and tested under different levels of complexity. Its performance is evaluated based on the value of average completion time. The result clearly shows that the proposed integrated production with PM schedule can reduce the average completion time by 11.68% compared to the production scheduling with machine breakdown time.