SACM - United States of America
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9668
Browse
2 results
Search Results
Item Restricted Production Planning in the Context of Industry 4.0 with Focus on Efficient Job Allocation & Workers’ Real-Time Status(Saudi Digital Library, 2023-08-15) Albassam, Abdullah Mohammed; Niknam, Seyed AIndustry 4.0 (I4.0) has emerged a distinct impact on industrial workforce and created demand for diverse set of workforce skills and domain knowledge. Accordingly, I4.0 production systems are in need for developing and utilizing an appropriate workforce planning that considers workers with different type of skills to cope with the production requirements and keep up an efficient production. The I4.0 philosophy advocates the usage of advanced wearable technologies. Such wearable devices are able to monitor workers’ status and record vital signs and physiological data. It is well known in literature that workers’ performance in production systems is linked to their job satisfaction level as well as psychological well-being. There is much active research in the area of advanced physiology measurement technologies and incorporating the workers’ health data into industrial applications in real time. In essence, it is expected that smart wearable health devices provide the ability to boost job satisfaction, reduce human errors, and affect performance by helping managers for more efficient task matching and scheduling. This research is focused on developing job assignment models in the context of I4.0 and has considered both workers’ physiological status and the skills required to achieve the production goals. The ultimate goal of the proposed models is to maximize productivity by matching operations tasks to workers with different required skills and various skill levels. This study also considers workers' performance indicator which is predicted by machine learning models using workers’ physiology measurement. The assignment model could provide promising results in moving toward real-time application of workers’ physiological status in order to better assign production tasks and maximize production value.13 0Item Restricted Applications Of Artificial Intelligence In Supply Chain Management In The Era Of Industry 4.0(2023) Ali, Arishi; Krishna, KrishnanNowadays, an emerging trend in Supply Chain Management (SCM) is a focus shift from classical Supply Chain (SC) to digital SC. However, decisions in the digital SC context require new tools and methodologies that consider the digitalization environment. Artificial Intelligence (AI) methodologies can provide learning, predictive, and automated decision-making capabilities in the digital environment. Among a wide range of problems in the SCM field, risk management, logistics, and transportation have received less attention from an AI perspective. The work presented in this dissertation proposes three AI-based approaches to help SCs manage their operations more effectively using creative risk monitoring and logistics/transportation solutions in the era of Industry 4.0. In the first study, a Digital Twin (DT) framework for analyzing and predicting the impact of COVID-19 disruptions on the manufacturing SC is developed to support the decision-making process in disrupted SC. The proposed Digital SC Twin (DSCT) model is aimed to work as an online controlling tower to monitor the behavior of physical SC in the digital environment and guide SCM managers to make the necessary adjustments to minimize risks and maintain SC stability during disruptions. In the second study, a contactless truck-drone delivery model for last-mile problems in the SC is introduced to support logistics and transportation operations during pandemics. A hybrid AI approach is developed to provide quality real-time solutions for the introduced truck-drone delivery system. In the third study, a collaborative Multi-Agent Deep Reinforcement Learning (MADRL) approach for vehicle routing in the SCM is designed to facilitate collaboration and communication among multiple vehicles in the SC distribution networks. Overall, the methods and models presented in this dissertation can enable SCs to transform their traditional practices, provide cost savings, support real-time decision-making, and enable self-optimization and self-healing capabilities in the age of Industry 4.056 0