## 4 Literature Review  
In this section, we aim to gain a comprehensive understanding of the field and identify research gaps and opportunities by providing a literature review on the synergies between data-driven and ML methods in remanufacturing. We systematically sorted the literature by matching identified remanufacturing topics in Table 1 with advanced computational methods, as summarized in Table 2. First, we examined the application and associated impacts of the IIoT on life cycle management and closed-loop supply models, emphasizing how these technologies facilitate data-driven decision-making in support of circular economy initiatives (Topics 0-4 in Figure 2). Next, we discuss the potential of optimization and ML techniques to enhance dynamic scheduling and HRC within remanufacturing processes (Topic 6), enabling optimized decision-making amidst uncertainties. Finally, we present our review work on utilizing ML models to understand and manage the quality of remanufacturing processes and products, addressing Topics 6-7. Figure 4 provides a comprehensive overview of the interactions between machine learning (ML) and data-driven models within remanufacturing tasks, highlighting their connections to life cycle management, scheduling and planning, quality control, and HRC. These areas are supported by a variety of ML and data-driven models, ranging from neural networks to probabilistic approaches. Each model is associated with specific tasks in remanufacturing research, as identified in our literature review. Figure 4: Machine learning and data-driven models and their role in remanufacturing. Abbreviations: CNN—Convolutional Neural Network, LSTM—Long Short-Term Memory, DQN—Deep Q-Network, PPO—Proximal Policy Optimization, LP—Linear Programming, NLIP—Nonlinear Integer Programming, GA—Genetic Algorithms, BA—Bees Algorithm. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Topics** & **Applications** & **Algorithms** & **Key Findings** \\ \hline \multirow{7}{*}{Life cycle management} & \multirow{7}{*}{Cylinder heads [44], Energy labeling [46], Aerospace blades [74], E-waste [75], Battery [76, 77], Automated vehicle [78], Engine [79]} & \multirow{7}{*}{HeiT [74], Life cycle assessment (LCA) [80], Cobot [77], Digital Twin [78, 79], Digital Passport [44, 45, 46, 75, 76]} & \multirow{7}{*}{Real-time monitoring systems with IIoT and DT enhance the precision, efficiency and success rates of recovering high-value components and materials, thereby contributing to extending product life cycles. Digital twins and predictive analytics can help forecast EoL scenarios, support proactive maintenance, and ensure that products are effectively remanufactured or recycled. \\ \hline \multirow{7}{*}{Reverse logistics} & \multirow{7}{*}{Manufacturers [45], Engine [81], Tactical decisions [82], Solid waste [83], Laptop [84]} & \multirow{7}{*}{Digital passport [45], Digital Twin [81, 82], IIoT [81, 83, 84]} & \multirow{7}{*}{Digital passport [45], Digital Twin [81, 82], IIoT [81, 83, 84]} & \multirow{7}{*}{Digital passport [45], Digital Twin [81, 82], IIoT [81, 83, 84]} & \multirow{7}{*}{Digital passport [45], Digital Twin [81, 82], IIoT [81, 83, 84]} & \multirow{7}{*}{Digital passport [45], Digital Twin [81, 82], IIoT [81, 83, 84]} & \multirow{7}{*}{Digital passport [45], Digital Twin [81, 82], IIoT [81, 83, 84]} & \multirow{7}{*}{\(\bullet\) The integration of advanced technologies supports dynamic management of stochastic demand and material flows, leading to more efficient reverse logistics.} \\ \hline \multirow{7}{*}{Carbon emission} & \multirow{7}{*}{Food supply chain [85, 86], Wind turbine [87]} & \multirow{7}{*}{IIoT [85, 86, 87], LCA [80, 86], Blockchain [80]} & \multirow{7}{*}{Integrating LCA with emerging technologies such as IIoT and Blockchain enables accurate real-time monitoring.}