Abstract With the introduction of Industry 4.0, automation and robotics have made great strides, enabling enterprises to improve their manufacturing processes for increased productivity and efficiency. This project introduces a novel method for implementing Industry 4.0 concepts through color-based object sorting employing a robot arm with real-time object identification capabilities. Creating a reliable and effective system that can automatically categorize items based on their color properties is the main goal of this project. To enable seamless object recognition and manipulation in real time, the suggested system integrates robotic manipulation with computer vision algorithms. The system makes use of a convolutional neural network (CNN) for precise object detection, using recent advancements in deep learning and image processing, allowing the robot arm to interact with a variety of items effectively. The training phase and the sorting phase are the two key phases of the approach. The CNN model is trained on a sizable dataset of labeled objects during the training phase to recognize various colors and forms. In order for the robotic arm to recognize things as they go along the conveyor belt and sort them into predetermined bins according to their respective colors, the trained model must be integrated with the robotic arm during the sorting phase. Several experiments are carried out with various lighting setups and object arrangements to evaluate the performance of the suggested system. The outcomes show how well the system performs in terms of exact object detection and reliable sorting. The system's capacity to effectively handle a variety of objects and adapt to changing environmental conditions further emphasizes its suitability for use in actual industrial scenarios. This project has important ramifications for the manufacturing sector, enabling improved automation capabilities and cost-efficiency. An important step towards implementing Industry 4.0 principles is the seamless integration of color-based object sorting and real-time object detection using a robotic arm. This will allow industries to optimize their production processes, minimize human intervention, and increase overall productivity. Further developments in robotics and computer vision are anticipated to push the limits of automation and open the door for more advanced and intelligent industrial systems as technology develops.

Abstract Last years, the implementation of reverse loops in logistics and the deployment of industrial symbiosis becomes more important. However, these new activities management isn’t easy and it’s necessary to propose methodologies to facilitate the actors work. However, the existing studies are more at strategic level, aiming with implementation or cooperation. At the tactical/operational level, solutions are poorly effective and/or expensive. Our work targets an optimal production policy definition based on the base stock strategy adapted for an industrial symbiosis. The system is composed of two kinds of warehouses and three types of enterprises. The recovering depends on the kind of collected used finished products. All demands are uncertain such as the number of collected used finished products and generated waste. Then, the main objective is to minimize the sum of all economic, environmental and social costs by identifying the level of the base stocks. To do this, an infinitesimal perturbation analysis study is conducted to evaluate the gradient estimators of the objective function subject to an echelon base stock production policy. This result is then used in a simulation based-optimization algorithm to determine these stock levels and highlight our theoretical results by comparing with other replenishment strategy and mathematical programming.

Abstract The development of data science in civil engineering has benefited from the rapid advances in sensor technology as well as data acquisition and storage. In contrast to traditional analysis and evaluation based on periodic inspection and full-scale test, the structural safety analysis and pre-warning can be achieved directly through the analysis of the design data of the newly built bridge and the monitoring data & test data of the bridge in service. Specifically, structural geometric data (length and cross-sectional area etc.), physical response data like displacement and stress, and vibration response data, such as acceleration and frequency, as well as the influence of the environment, e.g., temperature and humidity, must all be taken into account. Furthermore, the different sensitivity of different response data, which in turn affects structural safety analysis and pre-warning accuracy, is one of the current frontier sciences, i.e., the problem of multi-source (different response) data. It is expected that the development of data science will have very important theoretical research value and engineering practice significance for safety analysis and pre-warning in civil engineering, and is expected to bring new prospects for academia and industry.

Abstract Despite the worldwide investment of over a decade and billions of dollars in Industry 4.0 technologies, only a small number of researchers have entered digital supply chain management and modeling research. Thus, what hindered supply chain development? Recent internal discussions have focused on how to answer these concerns and quicken the development of supply chain management. We’ll collaborate with industry to learn from each other’s mistakes and see how other regions are faring in supply chain management.