## 5 Discussion and Conclusions  
This study offers a comprehensive and integrative view of how Artificial Intelligence (AI) and metaheuristic techniques can be leveraged within Advanced Planning and Scheduling (APS) systems to address the growing complexities of production scheduling in manufacturing--particularly in dynamic sectors like fashion. By combining the strengths of AI models such as DRL and ANNs with metaheuristics approaches like ACO and GAs, it becomes possible to manage complex, multi-objective scheduling problems such as Job Shop Scheduling Problems (JSSPs) by exploiting greater efficiency, adaptability, and sustainability perspectives. Therefore, metaheuristics approaches like ACO show strong  
Figure 3: Proposed framework for AI-based APS systems supported by metaheuristics [3; 18; 23; 25; 28; 30; 32; 33; 37; 38; 39; 41; 42; 43; 44; 45; 51; 53; 54; 55; 62; 63; 64; 67; 68; 69; 71; 72; 73; 79; 81]. performance in solving large-scale combinatorial scheduling problems, offering fast convergence and effective use of problem-specific heuristics. Enhanced ACO variants, including elitist strategies and hybrid models like GA-ACO, further improve solution quality by balancing exploration and exploitation. These methods also reduce the need for detailed system modeling and extensive configuration, making them well-suited to high-variability contexts such as small-batch or custom-order production. Similarly, DRL aligns with I4.0 and I5.0 visions by enabling self-optimizing production systems, while ANNs have proven useful in tasks like cycle time prediction. From an academic standpoint, this study contributes to the literature by highlighting the potential of hybrid optimization models and self-adaptive algorithms. It demonstrates how AI not only improves the configuration and performance of metaheuristics but also facilitates autonomous learning and decision refinement in production environments. Techniques such as Simulated Annealing and Tabu Search, while effective, depend heavily on manual parameter tuning and often lack the flexibility to adapt to rapidly changing conditions. In contrast, AI can support automatic parameter optimization and leverage historical production data to enable adaptive learning and schedule refinement with minimal human intervention. Theoretically, the primary value of this study lies in providing a unified framework that consolidates and contextualizes diverse approaches to AI-driven scheduling optimization. Unlike the fragmented literature, which tends to focus either on specific algorithms or narrow application domains, this work maps the intersection between AI and metaheuristics specifically in the context of production planning and control (PPC) within manufacturing. While existing studies have examined AI in broader manufacturing environments or assessed individual techniques in isolation, this study goes further by highlighting how hybrid, self-adaptive models can be tailored to the unique constraints of fast-moving, demand-volatile sectors such as the fashion sector. This not only fills a gap in the literature but also establishes a novel contribution by focusing not only on algorithmic adaptability but also on responsiveness and ethical considerations, reflecting the priorities of modern, digitally transformed production systems in real-world and high-variability contexts. From a practical perspective, the findings have direct implications for companies aiming to modernize their production control systems without heavy infrastructure investment. Production scheduling faces several core challenges: (i) ensuring stable and cost-effective operations; (ii) managing the inherent complexity of job shop environments, where machines, time, and human resources must be optimally allocated; (iii) adapting to dynamic market conditions that introduce instability and uncertainty into production workflows.