IoT-Driven Intelligent Scheduling Solution for Industrial Sewing Based on Real-RCPSP Model

Huu Dang Quoc, Loc Nguyen The (), Truong Bui Quang and Phuong Han Minh
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Huu Dang Quoc: Faculty of Economic Information System and E-commerce, Thuongmai University, 79 Ho Tung Mau, Cau Giay, Ha Noi City 100000, Vietnam
Loc Nguyen The: Faculty of Information Technology, Hanoi National University of Education, 136 Xuan Thuy, Cau Giay, Ha Noi City 100000, Vietnam
Truong Bui Quang: Faculty of Economic Information System and E-commerce, Thuongmai University, 79 Ho Tung Mau, Cau Giay, Ha Noi City 100000, Vietnam
Phuong Han Minh: Faculty of Economic Information System and E-commerce, Thuongmai University, 79 Ho Tung Mau, Cau Giay, Ha Noi City 100000, Vietnam

Future Internet, 2025, vol. 17, issue 2, 1-23

Abstract: Applying IoT systems in industrial production allows data collection directly from production lines and factories. These data are aggregated, analyzed, and converted into reports to support manufacturers. Business managers can quickly and easily grasp the situation, making timely and effective management decisions. In industrial sewing, IoT applications collect production data from sewing lines, especially from industrial sewing machines, and transmit that data to cloud-based systems. This allows businesses to analyze production situations, thereby improving management capacity. This article explores the implementation of IoT applications at industrial sewing enterprises, focusing on data collection during the production process and proposing a data structure to integrate this information into the company’s MIS system enterprise. In addition, the research also considers applying the Real-RCPSP problem to support businesses in planning automatic production operations.

Keywords: IoT application; IoT in sewing production; scheduling problem; evolution algorithms; intelligent planning; production planning; Real-RCPSP problem (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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