## 1 Introduction  
Since the term Industry 4.0 was coined, an increasing number of major manufacturers have incorporated relevant technologies, such as using well-designed and embedded sensors to collect data from machinery or the test results of various products. Then, a server receives the signal from multiple sensors, and unifies the format of these signals, for monitoring, scheduling, and analysis [1; 2]. This makes it possible for enterprises to monitor the operational status of machinery on an ongoing basis, and in so doing enhance production efficiency, reduce maintenance costs, and optimize quality. Industry 4.0 protocols can indeed help enterprises to improve performance and reduce costs; however, the capital outlay required for the adoption of these technologies (e.g., sensors, cloud computing, big data analysis) can be prohibitively high. Furthermore, the base infrastructure differs greatly from that of conventional enterprises, thereby necessitating considerable upgrades prior to implementation. Most large-scale enterprises are able to make these changes without difficulty; however, most small- and medium-sized enterprises (SMEs) are unable to absorb the costs. Many researchers have sought to lower the adoption threshold for Industry 4.0. [PERSON] et al. [3] introduced a core-meta model to help SMEs implement Industry 4.0 in their enterprises. [PERSON] et al. [4] used the technique of mechanistic modeling to predict the micromilling cutting forces. [PERSON] et al. [5]used the internal signals of machinery to do the tool wear detection. [PERSON] and [PERSON] [6] proposed a new approach on using digital drive signals for monitoring the conditions of machine tool. [PERSON] et al. [7] discussed how to use the concept of Industry 4.0 to do modeling and process monitoring on machines. Finally, [PERSON] et al. [8] developed a risk framework for enterprises considering switching over to Industry 4.0. It should be noted that most of the methods used to monitor machinery are impractical for SMEs, due to budgetary restrictions on the purchase of updated machinery equipped with the latest sensors. Moreover, it is doubtful that they will purchase large numbers of the same machines at the same time. To save money, the machines that they buy may even come from different manufacturers and countries. Consequently, these are reasons why mutual communication among machines is difficult for SMEs. Even if their machines had sensors that could send signals to the server, the formats of these signals vary and are almost impossible to analyze. In other words, it is extremely difficult for SMEs to introduce Industry 4.0 technologies [9; 10]. Without these monitoring technologies, the gap between their yield and production costs and those of large manufacturers will gradually widen, and ultimately they will be unable to escape the fate of elimination. Some new methods are needed to address this issue. Such methods must be inexpensive so that they are affordable by SMEs with little capital. Optimizing process schedules plays a significant role in Industry 4.0 technologies. For this reason, a number of researchers have proposed scheduling-related algorithms [11; 12]. For instance, [PERSON] and [PERSON] [12] proposed a multi-stage scheduling algorithm that can process multiple machineries running in parallel. Furthermore, they demonstrated that this problem is an NP-hard problem. Next, [PERSON] et al. [13] presented an algorithm that can schedule a set of independent jobs; the algorithm enables users to set the time, break up jobs, and complete them in the shortest time. In recent years, [PERSON] et al. [11] developed a scheduling algorithm that can optimize job schedules and consider machinery statuses at the same time. Another feature of their algorithm is that it can assist enterprises in finding multi-objective results rather than the single-objective results in previous papers. As a result, their algorithm can provide enterprises with a variety of options and satisfy options under different conditions.