## 1 Introduction
Nevertheless, it is important to understand that although the method above solves the problem of scheduling optimization in Industry 4.0, it is too complex for SMEs. This is because most of these enterprises, which may produce products such as screws, springs, molds, or plastic injections, need only one step to complete the manufacturing of their products. A multi-stage algorithm would be too time-consuming, and these SMEs rarely have the budget to purchase relevant software and hardware to perform these calculations. Thus, some simple approaches are needed to solve this problem. In response to these machinery monitoring and scheduling issues, this study proposed two solutions. First, in terms of monitoring, we present anduino-based add-on sensors to monitor machinery status. Then a feature extraction algorithm and a dimensionality reduction algorithm are utilized on these collected signals. Finally, a neural network is used to determine whether machinery statuses are normal. The advantage of this method is that the arduino-based add-on sensors are extremely inexpensive, so even if an enterprise requires a large quantity of these sensors, their cost will not be a great burden. Of course, the precision of the data collected by the arduino-based add-on sensors is not as high as that of data collected by expensive sensors. However, the products of SMEs, such as screws, springs, molds, or plastic products, do not require very high precision, so these add-on sensors are adequate for monitoring. Second, in terms of scheduling, multi-objective scheduling is the current trend [11]. Therefore, we developed an algorithm for scheduling with multiple objectives. To achieve this goal, we incorporated the recently well-known skyline query [14; 15; 16], which can consider situations with different conditions and send back the optimal solutions for various combinations of conditions. With Table 1 and Figure 1 as an example, Table 1 presents the total work time and costs of different schedules. For instance, the total work time of Schedule 1 is 560 min, and its total cost is USD 2,500. Figure 1 displays the total work time and cost of each schedule in Table 1 using coordinates; each point represents a schedule. The points in the very lower left corner of Figure 1 (i.e., Schedules 1,3,and 4) are the skyline schedule results. Schedule 1 has the shortest total work time, Schedule 4 has the lowest total cost, and the total work time and cost of Schedule 3 falls between those of Schedules 1 and 4. Schedule 2 is not a skyline schedule result because its total work time and cost are both higher than those of Schedule 1. Enterprise workers will certainly choose Schedule 1 over Schedule 2. With the concept of skyline schedules, we can help enterprises identify schedules that are superior in all aspects. However, skyline queries are known for requiring longer computation time; to resolve this issue, we developed an innovative algorithm. This paper is arranged as follows. We first introduce some related work in Section 2. Section 3 explains the approaches for monitoring the machinery. Section 4 studies the methods for scheduling. Section 5 presents the experiment and discussion, and Section 6 contains the conclusion.