# IoT-Driven Intelligent Scheduling Solution for Industrial Sewing Based on Real-RCPSP Model
## 1 Introduction  
The textile industry faces many challenges in improving production efficiency and optimizing management. Technology in the garment industry focuses on automated management solutions, digital sewing equipment, and sewing robots. However, the research, implementation, and application of Internet of Things (IoT) systems are becoming increasingly urgent to meet the increasing demand for quality and production efficiency. The IoT system brings many outstanding advantages in monitoring and operating production, helping textile and garment enterprises improve their competitiveness and sustainable development in international integration. This article will present the application of IoT systems in the management and operation of industrial garment production to collect real-time production data at sewing lines. Collected data will be integrated with available management data in the enterprise's MIS system, synthesizing reports to monitor real-time production progress. Typically, data gathered from IoT devices (such as Brother devices) are synchronized and stored on the device provider's cloud server. To incorporate this data into the enterprise's MIS, a method is required to manage the storage and synchronization of data from the cloud server system to the enterprise's MIS database system. This article studies and proposes a data structure used to store production data in an enterprise's MIS system. In addition,to improve production efficiency, the article proposes an automatic production planning method by applying the Real-RCPSP problem [1,2]; this problem will use data from contracts garment production contract that the enterprise signs with its partner as input data, the solution of the problem is a schedule showing the production plan based on the establishment of the enterprise's use of resources in implementation product manufacturing stages. Applying automatic production planning will help businesses save time completing contracts and improve production efficiency. Based on data collected from the IoT system, business administrators will closely monitor plan implementation progress in real-time, making appropriate and timely decisions to ensure complete contracts are on time. In order to support effective operations, businesses use collected data combined with production contract information with partners to create automatic production plans to optimize enterprise resources. The main contributions of the article are as follows:  
* Propose application plans to deploy IoT in the garment industry, methods to synchronize data between IoT devices to cloud server and between cloud server to MIS of the enterprise. * Propose a data structure to store data collected from IoT devices. This data structure will be added to the enterprise's MIS database to integrate with MIS data to make it easier to synthesize and analyze. * Propose an automatic production planning method by applying the Real-RCPSP problem. The article uses the Genetic Algorithm (GA) [3,4] and Particle Swarm Optimization (PSO) algorithm [5,6] combined with a dataset converted from enterprise garment contracts. The following content of the article includes: Section 2 presents research related to the application of IoT in the garment industry; Section 3 presents the IoT device deployment model and data synchronization method between IoT device and cloud server and between cloud server and enterprise MIS system; Section 4 presents the method of synchronizing IoT device data to the data storage system on the cloud computing system, which will then synchronize to the enterprise's MIS system; and Section 4 presents data structures for storing data collected from the IoT system. These structures will be added to the enterprise's MIS database. In addition, this part also presents data on garment contracts signed between businesses and partners, which will be converted into standard datasets for production planning. Section 5 presents the Real-RCPSP problem [1,2] and its application in automatic production planning.