## 2 Literature Review
An integrated optimization model was developed in [11] to minimize the manufacturing time of recycled parts. The model was integrated with an intelligent hybrid algorithm, a GA, neural network and simulation technique to optimize re-manufacturing planning. The model was tested on a case study and gave positive results. Another hybrid model was given in [12] that used hybrid computer simulation Artificial Neural Network (ANN) to minimize the makespan for a job-shop manufacturing system. For its ability to deal with complex, stochastic and nonlinear problems, DES was used to provide the training data for the ANN to estimate the makespan value for the dispatching rules available to find the optimal solution of the job-shop scheduling problem. Since the scheduling problem is very challenging, many special purpose simulation tools were designed by researchers to solve this problem. Example for this approach, is the research done in [13], where the researcher developed their own simulation tool using Java script to solve the scheduling optimization problem with their proposed evolutionary computation method. Other work used preexisting software simulation tools as in [14; 15]. These tools use dispatching rules and heuristic methods to generate the production planning schedule. The researchers used these tools to verify their proposed mathematical models for the scheduling optimization problem. Although previous works investigated various approaches and techniques in solving the production scheduling problem as an optimization problem, they did not include maintenance slots in their work. Nevertheless, including maintenance slots in the production scheduling would improve the overall efficiency and reduce unplanned downtime. There are three main maintenance techniques in the literature: corrective maintenance, preventive maintenance, and predictive maintenance [16]. * Corrective Maintenance (CM) or Run-to-Failure (R2F) or Reactive Maintenance (RM) happens only when an equipment stops working. This is the simplest maintenance strategy adding a direct cost to the process, since it is necessary both the stop on the production and the repair of the parts to be replaced. * Preventive Maintenance (PvM) or predetermined Time-Based Maintenance (TBM) is a maintenance technique performed periodically with a planned schedule in time or process iterations to avoid process/equipment failures. It is generally an effective approach to avoid failures. However, unnecessary corrective actions are taken, leading to an increase in the operating costs. * Predictive Maintenance (PdM) uses predictive tools to determine when maintenance actions are necessary. It is based on continuous monitoring of a machine or a process integrity, allowing maintenance to be performed only when it is needed. Moreover, it allows the early detection of failures thanks to predictive tools, health monitoring data (e.g., visual aspects, wear, coloration different from original, among others), statistical inference methods and engineering approaches. The total maintenance cost is the lowest when PdM is used [17]. Through the integration of AI and real-time Data Acquisition (DAQ), PdM time slots can be computed. Machine Learning (ML) with real-time DAQ can easily predict causes of disruptions in the manufacturing environments which allows a quick response to changes in shop floor in a timely and cost-effective manner. Recently, researchers tried to integrate PdM in the process of scheduling using various approaches. One of the early work done with this concept is the work presented in in [18]. It proposed a new problem formulation for the scheduling optimization problem to include maintenance calculations. The method used in [18] is very complex as it needs continuous change and skilled researcher to map it with the real-life problem.