## 3 Analysis of Literature Resources
### AI Techniques to Support APS Systems  
AI is emerging as a powerful tool for gaining advantages in dynamic markets [81] where customers' increasing demand and product variety challenge manufacturers' capacity to innovate and remain competitive [37]. The I4.0 paradigm integrates technologies, particularly AI and ML, to support production planning and control by generating optimal or near-optimal production plans within a feasible timeframe [16]. I4.0 technologies enable cost reductions in production and management through machine-to-machine communication, data-driven decision-making, and automation. However, the growing complexity and dynamism within production systems, driven by horizontal and vertical integration and automation, have introduced new challenges [16]. Before automated data collection, variables in production planning were limited due to manual handling [38]. Nowadays, the smart factory concept--based on real-time data sharing and seamless connectivity--allows machines to autonomously process large datasets, extract insights, and optimize operations [37].  
Indeed, ML algorithms are central to this shift as they can be trained on available datasets, relying on sufficient high-quality input data to identify patterns and predict continuous values [30; 32; 39]. Typically, ML algorithms can be classified according to the type of PPC task and the input and output data given [30], improving with experience, rather than being specially programmed manually by humans [32; 39].  
ML techniques are commonly categorized into three categories [20] (supervised learning, unsupervised learning, and RL) and are applied in PPC for forecasting, fault detection, production optimization, shop floor control, and quality improvement [32]. Supervised and unsupervised ML techniques can predict supply disruptions by exploiting historical data, while RL has been explored to support real-time adaptive and decentralized control scheduling in response to unpredictable events [18; 23; 33]. ML helps manage uncertainty in medium-term planning, enhancing inbound logistics and overall supply chain performance. Inbound logistics, involving internal material flows, benefits from continuous tracking and integration enabled by ML [1; 21; 33].  
This paper investigates the potential of ML, particularly RL, to improve lead time planning in job shop manufacturing [23]. Continuous training and re-training of models are often required depending on the approach used [21; 39]. However, implementing AI-driven APS systems remains a major industry challenge. Effective APS requires managing job orders and production flows to avoid bottlenecks and ensure timely deliveries [23; 30; 31]. Traditional systems such as ERP handle large volumes of production planning data, but lack operational effectiveness [23]. While a traditional production system makes difficult or even prevents human production scheduling, APS aims to assist decision-makers by delivering optimized production schedules in real-time, and therefore must be flexible, robust, and responsive to changing solutions [16; 32].  
To pursue this, metaheuristics and AI techniques come into play and the synergism between AI and APS is introduced. This study focuses first on RL (discussed in Section 3.2.1), while metaheuristics and hybrid techniques are addressed in Sections 3.3 and 3.4.