A Review of AI Powered Production Scheduling in Smart factories

Acta Scientific Pharmaceutical Sciences, 2020

The start of a new decade brings a new wave of technological change and unprecedented opportunities that will generate massive amounts of data at the level of Big Data (BD), spawn new threats and transform the way we do business. Dealing with such enormous data structurally or unstructured-wise and process it in real-time requires a new innovative technology such as Artificial Intelligence (AI). Today, AI is capable of learning from its experience through the element of its Machine Learning (ML) in conjunction with the Deep Learning (DL) component and using them to adjust to new input and perform human-like performance, or at least to complement and enhance human abilities. Because of this capability, it encompasses every aspect of the enterprise in the years to come. That is why we believe AI, Automation, and Analytics are central to the success of the enterprise and encompass critical business areas, including data, business processes, the workforce, and risk and reputation.

Proceedings of the IEEE

The traditional production paradigm of large batch production does not offer flexibility towards satisfying the requirements of individual customers. A new generation of smart factories is expected to support new multi-variety and small-batch customized production modes. For that, Artificial Intelligence (AI) is enabling higher value-added manufacturing by accelerating the integration of manufacturing and information communication technologies, including computing, communication, and control. The characteristics of a customized smart factory are to include self-perception, operations optimization, dynamic reconfiguration, and intelligent decision-making. The AI technologies will allow manufacturing systems to perceive the environment, adapt to the external needs, and extract the process knowledge, including business models, such as intelligent production, networked collaboration, and extended service models. This paper focuses on the implementation of AI in customized manufacturing (CM). The architecture of an AI-driven customized smart factory is presented. Details of intelligent manufacturing devices, intelligent information interaction, and construction of a flexible manufacturing line are showcased. The state-of-the-art AI technologies of potential use in CM, i.e., machine learning, multi-agent systems, Internet of Things, big data, and cloud-edge computing are surveyed. The AI-enabled technologies in a customized smart factory are validated with a case study of customized packaging. The experimental results have demonstrated that the AI-assisted CM offers the possibility of higher production flexibility and efficiency. Challenges and solutions related to AI in CM are also discussed.

IJETRM JOURNAL, 2022

The manufacturing industry achieves efficiency minimizes human errors and optimizes production through Artificial Intelligence (AI). Machine Learning (ML) along with Computer Vision and Predictive Analytics guide Manufacturing Execution Systems (MES) and supply chain ordering systems with automated operations that deliver improved decision capabilities. AI offerings improve live surveillance capabilities and automated process control systems while maintaining accurate inventory records which optimizes operational efficiency (Zhang et al., 2020). Through predictive maintenance approaches steered by AI technology operators reduce the number of unexpected stoppages that create expense reductions and efficiency benefits (Kusiak, 2021). AI applications in manufacturing enable faster adjustments of production schedules which enable businesses to adjust their manufacturing operations according to rising and falling market demands. The essential nature of artificial intelligence in manufacturing systems will expand as it progresses because it generates industrial evolution combined with sustainability and operational excellence.

Metaverse, Asia Pacific Academy of Science Pte. Ltd. (APACSCI), 2024

Artificial intelligence (AI) stands as a potent catalyst for revolutionizing manufacturing, promising unprecedented efficiency, agility, and resilience. This research embarks on an investigative journey to dissect the multifaceted landscape of AI in manufacturing, aiming to unravel its current status, intrinsic challenges, and prospective pathways. This research unveils the intricate relationship between AI technologies and manufacturing processes across diverse domains. Examining various domains, including system-level analysis, human-robot collaboration, process monitoring, diagnostics, prognostics, and material-property modeling. The research also reveals AI’s transformative potential in optimizing manufacturing operations, enhancing decision-making, and fostering innovation. By dissecting each domain, the research illuminates how AI empowers manufacturers to adapt to dynamic market demands and technological advancements, ultimately driving sustainable growth and competitiveness. Moreover, it also examines the evolving dynamics of human-robot collaboration within manufacturing settings, recognizing AI’s pivotal role in facilitating seamless communication, shared understanding, and dynamic adaptation between humans and machines. Through an exploration of AI-enabled human-robot collaboration, this research underscores the transformative power of symbiotic relationships in reshaping the future of manufacturing. While highlighting opportunities, it acknowledges the myriad challenges accompanying AI integration in manufacturing, such as data quality issues, interpretability of AI models, and knowledge transfer across domains. By addressing these challenges, the research aims to pave the way for more resilient AI-driven manufacturing systems capable of navigating complex market landscapes and technological disruptions. This research sheds light on AI’s transformative potential in manufacturing, inspiring collaborative efforts and innovative solutions that will propel the industry forward into a new era of possibility and prosperity.

Sustainability

Adaptation and innovation are extremely important to the manufacturing industry. This development should lead to sustainable manufacturing using new technologies. To promote sustainability, smart production requires global perspectives of smart production application technology. In this regard, thanks to intensive research efforts in the field of artificial intelligence (AI), a number of AI-based techniques, such as machine learning, have already been established in the industry to achieve sustainable manufacturing. Thus, the aim of the present research was to analyze, systematically, the scientific literature relating to the application of artificial intelligence and machine learning (ML) in industry. In fact, with the introduction of the Industry 4.0, artificial intelligence and machine learning are considered the driving force of smart factory revolution. The purpose of this review was to classify the literature, including publication year, authors, scientific sector, country, in...

AI Magazine, 1990

There is a great disparity between the number of papers which have been published about AI-based manufacturing scheduling tools and the number of systems which are in daily use by manufacturing engineers. It is argued that this is not a reflection of inadequate AI ...

Applied Sciences

Artificial intelligence (AI) has been successfully applied in industry for decades, ranging from the emergence of expert systems in the 1960s to the wide popularity of deep learning today. In particular, inexpensive computing and storage infrastructures have moved data-driven AI methods into the spotlight to aid the increasingly complex manufacturing processes. Despite the recent proverbial hype, however, there still exist non-negligible challenges when applying AI to smart manufacturing applications. As far as we know, there exists no work in the literature that summarizes and reviews the related works for these challenges. This paper provides an executive summary on AI techniques for non-experts with a focus on deep learning and then discusses the open issues around data quality, data secrecy, and AI safety that are significant for fully automated industrial AI systems. For each challenge, we present the state-of-the-art techniques that provide promising building blocks for holist...

International Journal of Research Publication and Reviews, 2023

New industrial revolution in smart manufacturing is about to occur, propelled by unparalleled accessibility to cutting-edge technology. The Fourth Industrial Revolution, often referred to as Industry 4.0, has ushered in a new era of manufacturing known as smart manufacturing. At its core are technologies like Artificial Intelligence (AI) and Machine Learning (ML) that have revolutionized traditional manufacturing processes. This research article explores the integral role played by AI and ML in transforming conventional manufacturing into smart manufacturing. It delves into their applications, from data-driven decision-making to predictive maintenance, and their integration with the Internet of Things (IoT). The article also examines real-world examples to illustrate the impact of these technologies while addressing challenges and ethical considerations. Furthermore, it envisions future trends and implications for the manufacturing industry in the era of AI and ML. AI guarantees quality control in the manufacturing sector. Intelligent AI programmes are able to track performance, keep an eye on machine output, and identify flaws. They also contribute to lower maintenance expenses. Nowadays, the majority of industrial businesses automate their manufacturing processes with AI.

The current COVID-19 pandemic has caused severe disruptions in economies. It is likely to cause supply chain disorder and eventually force companies and entire industries to rethink and adapt to the global supply chain model. Many manufacturing companies have halted their production, which has collaterally damaged the supply chain and the industry. The industries have started to restructure their business model for 2020, and many SMEs and large manufacturing plants have halted/postponed any new technology upgrade in their factories in order to recover from the losses caused by the lockdown and economic slowdown. The growth in the adoption of AI solutions is completely dependent on the growth of manufacturing units. AI refers to multiple technologies, working in tandem to allow the machines to sense, learn, understand, and act to augment human capabilities. AI technology can learn and handle vast amounts of information that will enhance and transform operations in different fields effectively. Over a certain period of learning and comprehending, AI technology can anticipate needs and make informed and relevant decisions. AI machines are efficient at quick data processing to generate relevant answers to any question arising in the business. They offer accurate predictions, and customers' needs based on what they learn. Due to the growing importance of AI in business, now the present study focuses on the role of Artificial Intelligence in manufacturing sector in India.

2023

The huge transformation brought by the fourth industrial revolution into the manufacturing world has forced any company to take on the digitalization journey, regardless of its size, sector, or location. In this context, Artificial Intelligence (AI) technologies are ready to take off as a new approach to solve business issues, and, recently, AI tools are proliferating [1]. Forward-thinking results can be obtained by analyzing huge amounts of data from a wide range of sources in the production system and by identifying deviations and trends in real time for making decisions [2]. The greater intelligence brought by AI embedded in production systems can not only bring advantages for large companies but also support Small-Medium Enterprises (SMEs) and mid-caps in achieving better operational performance. Yet