Occupational safety and health is among the most challenging issues in many industrial workplaces, in that various factors can cause occupational illness and injury. Robotics, automation, and other state-of-the-art technologies represent risks that can cause further injuries and accidents. However, the tools currently used to assess risks in workplaces require manual work and are highly subjective. These tools include checklists and work assessments conducted by experts. Modern Industry 4.0 technologies such as a digital twin, a computerized representation in the digital world of a physical asset in the real world, can be used to provide a safe and healthy work environment to human workers and can reduce occupational injuries and accidents. These digital twins should be designed to collect, process, and analyze data about human workers. The problem is that building a human-focused digital twin is quite challenging and requires the integration of various modern hardware and software components. This paper aims to provide a brief survey of recent research papers on digital twins, focusing on occupational safety and health applications, which is considered an emerging research area. The authors focus on enabling technologies for human data acquisition and human representation in a virtual environment, on data processing procedures, and on the objectives of such applications. Additionally, this paper discusses the limitations of existing studies and proposes future research directions.

The primary sources of injuries in the workplace are improper manual material handling (MMH) motions, forklift collisions, slip, and fall. This research presents a Digital Twin (DT) framework to analyze fatigue in humans while performing lifting MMH activity in a laboratory environment for the purpose of reducing these types of injuries. The proposed methodology analyzes the worker’s body joints to detect biomechanical fatigue as a factor of change in back, elbow, and knee joint angles. Using the dynamic time warping (DTW) algorithm, the change in joint angles with time was analyzed. The variation in DTW parameters was evaluated using exponentially weighted moving average (EWMA) control charts for further analysis. A preliminary study considering two healthy male subjects performing seven experiments, each under an optical motion capture system was performed to test the model’s efficiency. Our contributions are twofold. First, we propose a model to detect biomechanical fatigue in the subjects performing MMH lifting activity as a change in joint angles. Secondly, the research also shows evidence that different individuals show signs of body fatigue via different body joints and showcases the need for a true personalized DT for an operator for fatigue assessment in an MMH environment.

Driven by wind and solar photovoltaics technology, the power industry is shifting towards a distributed generation (DG) paradigm. A key challenge in deploying a renewable DG system is the power volatility. This study proposes a visionary energy concept and further presents a mathematical model that could help the large industry consumers adopt this new energy technology. The study seeks to design a grid-connected DG system that is capable of providing the necessary electricity for wafer fabs. Simulation-based optimization algorithm was applied to determine the equipment type and capacity aiming to minimize the DG lifecycle cost. The proposed method was demonstrated on fab facilitates located in three different regions in the US.

This paper studies an unrelated parallel machine photolithography scheduling problem with dual resource constraints (DRC). This is an emerging problem in the semiconductor industry, where the masks can be transferred from one working station to another, increasing the flexibility of production through sharing the auxiliary resource. It gives rise to a complex DRC scheduling problem involving jobs, machines, and masks, restricted by sequence-dependent setup time and station-dependent mask transfer time. This study proposes two mixed-integer programming (MIP) models (Model 1 and Model 2) and an improved naked mole-rat algorithm that hybrids with a genetic algorithm (GA) and variable neighborhood search algorithm (VNS). Numerical experiments show that Model 2 performs better compared with Model 1, and the improved naked mole-rat algorithm brings a significant improvement over GA and VNS.