CPS and IoT applications for Sustainable Production Logistics

Background

Cyber-Physical System (CPS) and Internet of Things (IoT) are essential for achieving the vision of Sustainable Production Logistics and enhancing manufacturing competitiveness. Digital twin (DT) and IoT based services provide real-time location of materials and optimization of resources for addressing mass customization and fluctuating market demand. However, literature applying IoT and achieving DT-based services in production logistics is scarce.

Aims and objectives

The purpose of the research is to analyze the combined use of digital services and IoT for sustainable production logistics. In this project a framework combining DT-based services and IoT for the real-time location and optimization of material handling is proposed.

Project plan

The study draws results from the smart production logistics demonstrator based on a case in the automotive industry applying the proposed framework. The results show improvement in the delivery, makespan, and distance travelled during material handling. This study provides critical insight for managers responsible for improving the delivery of materials and information inside a factory.

Applied interdisciplinarity

In production logistics environment, various stakeholders are involved such as material handling operators, logistics planners, and decision-making managers as well. They have different interests and that makes us provide different DT-based services based on their interests and requirements. Therefore, we need to collaborate with other research groups with different strengths. For example, in this project, our research group suggested CPS and IoT framework for sustainable production logistics and the research group from IIP developed a core algorithm to optimize logistics schedule. I believe inter disciplinary collaboration work helps to maximise each of our research strengths.

Papers

Jeong, Y., Flores-García, E., & Wiktorsson, M. (2020, Dec). A design of digital twins for supporting decision-making in production logistics. In 2020 Winter Simulation Conference (WSC).

Flores-García, E., Jeong, Y., & Wiktorsson, M. (2021, Sep). Applying Machine Learning for Adaptive Scheduling and Execution of Material Handling in Smart Production Logistics. In IFIP International Conference on Advances in Production Management Systems (APMS).

KTH Collaborations

Sustainable Production Development (HPU)
Production Engineering (IIP)

Duration

May 2019 – April 2022

Project participants