## 4 Literature Review
### Scheduling and Planning  
Effective scheduling and planning are essential for remanufacturing management as they ensure optimal resource allocation, minimize downtime, and enhance overall operational efficiency. To address the challenges of process complexity and demand uncertainties, data-driven optimization and RL models have been developed. Various types of uncertainties impact the scheduling and planning of the remanufacturing process. The most studied uncertainty is the quality of returned cores [55; 61; 62; 93; 94; 95; 97; 98; 103], which significantly impacts the remanufacturing process requirements, resource needed, and cost estimates. Another crucial uncertainty is the disassembly time [70; 99; 101; 102], which affects process planning and resource management. Additionally, uncertainties related to the cores and demands [93; 94; 96] affect production planning and inventory management, with variations arising from differences in core return timing and quality [61; 63]. Furthermore, remanufacturing failure rates [55; 101; 102] are crucial for robust process planning and quality assurance. While these uncertainties are well documented, others like the resources needed for remanufacturing [63], are less discussed. ML and data-driven models are powerful tools to address the challenges in scheduling and planning, particularly through meta-heuristic, mathematical optimization, and RL techniques. Mathematical optimization techniques are utilized to maximize profit and minimize costs in scheduling and planning in remanufacturing. These methods typically target optimizing acquisition qualities and quantities, remanufacturing decisions, and resource allocation. For instance, [PERSON] et al. formulated a convex optimization, an extended multi-product Newsvendor Problem, to maximize overall profit [62]. Similarly, a nonlinear integer programming (NLIP) model was developed to minimize the total cost of acquisition, remanufacturing, and scrapping of cores [63]. Other data-driven approaches integrate with linear programming to maximize total profit [62], applying nonlinear programming models considering carbon emissions [64]. The objectives of these models are often costs, revenue, and environmental benefits. Meta-heuristic techniques address complex, multi-objective optimization problems in scheduling and planning. Examples include a modified discrete BA for disassembly sequence planning [100], and an improved discrete BA for workstation optimization [28]. [PERSON] et al. proposed a GA combined with an improved random forest classifier to intelligently select the optimal rescheduling method based on system status, as shown in Figure 9. The system status is characterized by factors such as machine utilization, job processing times, and the total time required for reworked operations [101]. RL has proven to be effective in handling uncertainties in remanufacturing, such as the quality of returned products, machine failures, and varying initial states. [PERSON] et al. used Q-learning and DQN algorithms to minimize total production time, continuously adapting to dynamic conditions [55]. [PERSON] et al. dynamically controlled a hybrid disassembly system, consisting of various types of stations, using DQN to minimize labor costs, idling costs, makespan, and failures [57]. [PERSON] et al. dealt with the control of job release in a hybrid disassembly line, minimizing work in progress and maximizing throughput using PPO [56]. [PERSON] et al. employed PPO to optimize disassembly scheduling and minimize makespan, utilizing the strength of RL to adapt to various uncertainties and improve decision-making over time [103]. Hybrid approaches that combine multiple algorithms have also demonstrated effectiveness. For example, one study integrated fuzzy dynamic modeling and Monte Carlo simulations with RL for robotic disassembly optimization [70].