The convergence of robotic process automation (RPA) and generative AI (GAI) within the context of Internet of Everything (IoE) services represents a profound paradigm shift. This fusion of technologies not only streamlines routine tasks but also catalyzes innovation while harnessing the potential of interconnected devices. Such integration empowers organizations to achieve remarkable gains in efficiency and sustainability. This paper embarks on an exploration of these transformative services, designed to elevate productivity, and curtail wasteful practices in contemporary industries. By closely examining intricate case studies, we illuminate the multifaceted advantages of this integrated approach. Our investigation demonstrates how RPA accelerates the execution of repetitive processes, substantially diminishing the margin for human error and amplifying operational efficiency. In contrast, generative AI introduces a disruptive force, generating fresh ideas, designs, and solutions, thereby elevating the quality of products and services. The infusion of these cutting-edge technologies into the fabric of IoE services paves the way for organizations to attain unprecedented levels of automation, intelligence, and connectivity. Furthermore, this paper comprehensively addresses the intricate challenges and considerations associated with the proposed implementation. We delve into ethical concerns, security implications, and the necessary workforce adaptation to offer a balanced perspective on the adoption of these technologies. Additionally, we navigate through potential limitations and constraints, underscoring the imperative need for strategic planning and robust governance.

When all of the one-sided specification indices of each quality characteristic reach the requirements of the process quality level, they can ensure that the process capability of the product meets the requirements of the process quality level. This study constructs a fuzzy membership function based on the upper confidence limit of the index, derives the fuzzy critical value, and then labels the fuzzy critical value on the axis of the visualized radar chart as well as connects adjacent critical points to shape a regular polygonal critical region. Next, this study calculates the observed value of the index to estimate and mark it on the axis for forming a visualized fuzzy radar evaluation chart. Obviously, this fuzzy evaluation model not only reduces the testing cost but also makes the quality level quickly meet the requirements of the specifications. Further, the radar chart can reduce the risk of misjudgment attributable to sampling errors and help improve the accuracy of evaluation by a confidence-upper-limit-based fuzzy evaluation model. Therefore, this easy-to-use visualized fuzzy radar evaluation chart is used as an evaluation interface, which has good and convenient management performance to identify and improve critical-to-quality quickly. Improving the quality of the process before the product is completed will also have the advantage of reducing social losses and environmental damage costs.

The manufacturing systems face growing demands due to the instability of the market, the demanding sustainability policies, and the high rate of old equipment, but traditional planning structures are mostly fixed and deterministic, leading to the inefficiency of joint optimization of operational stability and environmental sustainability in unpredictable situations. This research proposed and empirically tested an artificial-intelligence-based adaptive planning platform, which combines a physics-based Digital Twin (DT) and a Pareto-conditioned Multi-Objective Proximal Policy Optimization (MO-PPO) algorithm to be able to co-optimize reliability and sustainability indicators in real-time. The platform reinvents manufacturing planning as a Constrained Multi-Objective Markov Decision Process (CMDP), optimizing an Overall Equipment Effectiveness (OEE) and energy carbon intensity as well as material waste, and strongly adhering to operational restrictions. The study utilizes a four-layer cyber–physical architecture, which includes an edge-based data acquisition layer, a high-fidelity stochastic simulation engine that is calibrated via Bayesian inference, a graph attention network-based state-encoding layer, and a closed-loop execution loop that runs with 60 s long planning cycles. In this study, a statistically significant enhancement was shown in 10,000 stochastic simulation experiments and a 12-week industrial pilot deployment: 96.8% schedule performance, 84.7% OEE, 16.5% cut in specific energy usage (2.38 kWh/kg), 17.1% reduction in material-waste rate (6.8%), and 21.4% enhancement in carbon effectiveness, outperforming all baseline strategies (p = 0.001). The analysis showed that there was a surprising synergistic correlation between waste minimization and OEE enhancement (r = −0.73), and 34.1% of overall OEE improvement could be explained by sustainability strategies. This study provides a robust framework for adaptive, resilient, and eco-friendly manufacturing processes in line with Industry 5.0 ideologies.

Climate change threatens human survival and development. Cities, as the main gathering places for human production and life, serve as the focal points for the implementation of the policies related to energy efficiency, energy transition, and environmental protection. This study constructs an index system for the evaluation of carbon-neutral cities from the perspectives of carbon sources and carbon sinks. The system includes thirteen indicators under six dimensions. It combines objective and subjective data (i.e., statistical data and expert evaluations) by integrating two approaches: the fuzzy analytic hierarchy process (fuzzy AHP) and vise kriterijumska optimizacija i kompromisno resenje with hesitant fuzzy sets (HFS-VIKOR). We verify the efficacy of the proposed approach through a case study of thirteen low-carbon pilot cities in China. The results indicate that among these cities, Shenzhen performs the best, followed by Guangzhou and Hangzhou, while Kunming, Baoding, and Tianjin show poor performance in terms of carbon neutrality. Kunming and Baoding exhibit shortcomings mainly in carbon sources, while Tianjin faces deficiencies in both carbon sources and carbon sinks. Sensitivity analysis and comparative analysis show the availability and effectiveness of the proposed method. The proposed radar chart further highlights the improvement directions for each city to achieve carbon neutrality.