## 4 Literature Review
### Hrc  
HRC in remanufacturing enhances productivity and precision by merging human dexterity and decision-making capabilities with the consistency and strength of robots. This synergy not only boosts efficiency and adaptability to uncertainties but also holds promise for effectively managing process complexities and uncertainties in product disassembly, component inspection, and reassembly through the strategic allocation of tasks between humans and robots/cobots. This synergy forms effective teams with unique capabilities in operational tasks, information perception, and learning [144]. HRC enhances precision and adaptability in assembly [110], integrates human expertise with robotic sensing in inspection [58], and addresses the unpredictable challenges of disassembly. While full automation is often impractical, HRC enables efficient task distribution between humans and robots [114]. This approach addresses the unpredictable nature of returned products while balancing workload and economic outcomes. However, implementing HRC in remanufacturing systems faces challenges ranging from technological integration to worker adaptation and process changes [114]. In HRC for remanufacturing, process planning objectives include human-related factors alongside traditional profit-oriented goals like minimizing disassembly time, cost, and workstation numbers [28; 59; 70; 95; 115; 116; 117; 118; 122]. While efficiency remains crucial, its definition shifts in HRC scenarios. Instead of focusing solely on throughput and resource utilization, efficiency in HRC emphasizes optimal task allocation between humans and robots [28; 59; 115; 116; 117; 118; 122]. This addresses the challenges of workload distribution in manual and automated operations, as noted by [114]. Unique to HRC are objectives related to worker well-being, including human fatigue, safety, and workload [59; 115; 116; 117; 122]. These human-centric considerations are crucial in HRC scenarios, recognizing the importance of worker well-being and safety in the remanufacturing process. Environmental factors such as energy consumption are sometimes considered [59], further expanding the multifaceted nature of HRC in remanufacturing planning and scheduling. Balancing these diverse objectives makes optimal task allocation between humans and robots a central research question in HRC for remanufacturing. ML and data-driven methods drive the utilization of HRC to enhance the efficiency and quality of remanufacturing processes. For example, [PERSON] et al. conducted an extensive product analysis based on a CAD file to customize suitable operations for each returned core [119]. This has been extended to access the properties, complexity of parts and tool requirements [120]. Connecting elements are often of particular interest because their detachment affects the complexity and forces required for a remanufacturing operation, which, in turn, influences whether a task is best performed by a human or a robot [121]. To effectively allocate tasks in inspection, [PERSON] et al. propose an AND/OR graph-based approach, improving efficiency by enabling parallel operations like simultaneous retrieval and inspection, allowing human intervention for issue management [58]. Another study implemented a voice-controlled collaborative inspection system where robots performed AI-powered visual inspections of predefined areas while humans provided oversight and performed parallel tasks, reducing the cycle time by 33.4% compared to manual inspection [113]. While the remanufacturing literature rarely focuses specifically on reassembly, research on general assembly has identified various ML and data-driven approaches for task allocation in HRC. Traditional methods such as Markov chains [71] and Petri nets [60] have been successfully applied, showing significant improvements in efficiency and cycle times. Figure 11 shows an exemplary workflow of HRC for disassembly designed to flexibly and efficiently complete the disassembly process in remanufacturing [100].