## 1 Introduction  
Fierce competition in the market of the manufacturing sector and various customer preferences constantly challenge the diversity of manufacturing firms' products and their resilience to rapid technological change and to shorten the product life cycle. The flexible manufacturing systems (FMS) have, therefore, become an inevitable choice for many companies and a research hotspot [1]. Moreover, the concept of Industry 4.0, such as digitalization, Internet of Things (IoT), and cyber physical systems (CPS) has gained great importance across industries, including manufacturing recently, which require more digitized systems and network integration via smart systems [2; 3]. Therefore, FMS becomes a key topic and plays an important role in the realization of the Industry 4.0 because it can achieve the goals of Industry 4.0 that include a higher level of operational efficiency and productivity, as well as a higher level of automatization [4; 5]. An FMS is generally defined as an integrated computer-controlled system with a set of workstations, interconnected by an automated material handling system (MHS) and which can simultaneously perform several different tasks [6; 7]. It is learned from the literature that the workstations (such as CNC machine tools, industrial robots, washing and measuring machines) are processing stations of FMS, which are involved in performing operations on the part types [8]. The automated material handling system is used for transferring parts from one station to another, while the central computer is used for controlling and coordinating the performance of the workstation and material handling system. As the distinguishing attribute of an FMS, flexibility is regarded as the ability of a manufacturing system to withstand a certain level of variations in partial styles without any interruption in the production line [9]. Manufacturing system flexibility consists of volume flexibility, product flexibility, production flexibility, and delivery flexibility, etc. There is already significant research on these flexibilities features under a crucial assumption that elements of the FMS are available at all times. [PERSON] [10] combined the diagram and matrix methods to rank different FMS alternatives for the final decision making. [PERSON] and [PERSON] [11] used the six most potential preference-ranking methods for selecting the best FMS for a given manufacturing organization. [PERSON] and [PERSON] [12] proposed a decision support system to select the optimal alternative CNC machine tool to meet the desired criteria of FMS. Recently, more and more researchers use simulation methods for FMS performance analysis. [PERSON] et al. [13] presented a framework based on the Taguchi experimental design for evaluating the effects of varying levels of machine and routing flexibility on the makespan, average waiting time (AWT), and average utilization of an FMS. [PERSON] et al. [14] utilized an integrated genetic algorithm, the Monte Carlo method, to solve the stochastic flexible job shop-scheduling problem and to measure the impact of routing flexibility on shop performance. [PERSON] and [PERSON] [15] performed three different approaches to analyze the intensity of performance variables in an organization and proposed the FMS performance index to intensify the factors, which affect FMS. In a multistage production system, [PERSON] et al. [16] proposed a simulation-based optimization method that utilizes lower-level shop floor data to calculate robust production plans for final assembly lines of a flexible, multistage production system. [PERSON], [PERSON] et al. [17] demonstrated how discrete event simulation (DES) can address complexity in an FMS to optimize the production line performance. However, the reliability and availability of machines play an important role in the actual performance of FMS. Machine failures cause the greatest impact on the due date and other performance criteria even if there is the option of rerouting the parts to alternative workstations.