## 1 Introduction  
Until now, production has focused on boosting productivity by enhancing sales volume, which has led to overproduction. With the transition towards more sustainable and environmentally responsible production, it is essential to extend product lifecycles by refurbishing components and products [1]. Interest in Artificial Intelligence (AI) has surged recently due to its wide-ranging applications in areas such as modeling, classification, optimization, and prediction. AI systems are designed to stimulate human cognitive functions by learning, adapting, processing language, and performing intelligent tasks rationally [2]. As big data and analytics continue to evolve, market demands for product manufacturing have escalated, needing greater personalization, quicker delivery times, and reduced costs. To support and enhance decision-making, a larger range of data fusion is essential for scheduling, allowing the integration of information from heterogenous sources to encompass nearly all aspects of manufacturing, whether derived from expert insights, Internet of Things (IoT) systems, or external data [3]. Traditional techniques such as infinite capacity scheduling present the great limitation of not providing a realistic production plan according to the real availability of resources, materials, or equipment for production systems. As a results, they are rather ineffective for complex optimization problems with vast solution spaces, such as assembly line balancing, production planning, and fashion sales forecasting. Applying AI in supply chain operations, such as in production scheduling and control, can overcome the limitations provided by traditional scheduling techniques. Moving towards finite-capacity scheduling adds value by identifying future critical issues in advance and providing corrective actions that can be implemented on time. Moreover, finite-capacity scheduling aims to identify an effective and feasible production plan making the best use of available resources, minimizing production time in compliance with the internal and external deadlines. This work highlights how introducing AI into Advanced Planning and Scheduling (APS) systems can resolve optimization challenges where traditional techniques often fail, especially in decision-making based on more informed resource management. AI-based APS systems not only enhance the precision and flexibility of scheduling processes but also support human intervention in executing tasks more efficiently, reducing errors and improving outcomes. The primary objective of this research is to explore how AI and metaheuristic techniques can be leveraged within APS systems to manage the growing complexity of production scheduling--particularly in dynamic sectors like fashion. By integrating AI models such as Deep Reinforcement Learning and Artificial Neural Networks with metaheuristics like Ant Colony Optimization and Genetic Algorithms, the study demonstrates how hybrid, self-adaptive models can tackle complex scheduling problems such as Job Shop Scheduling Problems (JSSPs). These solutions improve adaptability, sustainability, and operational efficiency in high-variability contexts such as small-batch or custom-order production. Furthermore, they offer scalable and low-configuration alternatives suitable for real-time rescheduling and enhanced decision-making, in alignment with Industry 4.0 and Industry 5.0 principles. Therefore, this research aims to bridge a significant gap in the scientific literature by consolidating fragmented insights into a unified framework. It contributes by contextualizing AI-enhanced APS approaches within production planning and control (PPC) while also embedding human-centric and ethical considerations. Practically, the findings offer companies actionable insights to modernize production systems without large infrastructure investments, improving responsiveness, energy efficiency, and alignment with volatile demand patterns--especially in sectors like fashion manufacturing.