# Deep Reinforcement Learning for Selection of Dispatch Rules for Scheduling of Production Systems
## 2 Literature Review  
In manufacturing, scheduling refers to the process of arranging, controlling and optimizing the work and workloads in a production system [11]. The fundamental objective is to arrange the manufacturing activities in such a way that the production costs and lead times are minimized, and quality standards are achieved. In industrial practice, dispatch rules represent one of the approaches used for scheduling, as they are simple to implement and they can deliver acceptable solutions quickly In the literature, it has been demonstrated how dynamic modifications to dispatching rules and the incorporation of real-time data can increase the overall efficiency, decrease lead times and improve responsiveness [12; 13; 14]. Although the first two cited studies focus on customizing rule selection for particular manufacturing contexts, such as general systems or unique production, the latter offers a comprehensive overview that situates these strategies within the changing Industry 4.0 environment, emphasizing the value of informed, data-driven decision-making. In the past few years, more sophisticated scheduling methods have been investigated, such as smart search [15; 16; 17] or genetic algorithms [6; 18]. A resource planning methodology specifically tailored to the installation phase of industrial product-service systems has been proposed, integrating advanced planning and forecasting techniques to ensure the efficient allocation of personnel and equipment, ultimately reducing delays and improving coordination among stakeholders [15]. In [16], an integrated, simulation-based approach is introduced for refinery short-term scheduling that concurrently manages tank farms, inventories and distillation processes. Through the holistic modeling of interconnected operations, it achieves optimized production sequencing, minimized downtimes and improved overall refinery performance. [PERSON] proposed a real-time scheduling and simulation optimization framework for job shops operating in a production-logistics collaborative environment. By continuously adjusting the schedules based on live data and accommodating both production and material flow constraints, it enhances the responsiveness, shortens lead times and aligns operational execution with the evolving shop floor conditions [17]. A dynamic scheduling approach was proposed for manufacturing job shops by employing genetic algorithms. Improved adaptability and efficiency were demonstrated as task sequencing and resource allocation were optimized in rapidly changing conditions [6]. A unified multi-objective genetic algorithm was introduced to achieve energy-efficient job shop scheduling. Reduced energy consumption and shortened production times were obtained by simultaneously optimizing multiple criteria, thereby enhancing the overall operational sustainability [18]. In practice, most of the time, there are deviations from the initial schedule due to unexpected events such as new jobs or machine breakdowns. In such cases, a mechanism to reschedule is needed. Much research has been carried out around the general concept of dynamic scheduling. A heuristic was developed for adaptive production scheduling and control within flow shop environments. Improved responsiveness and reduced lead times were achieved by continuously adjusting schedules based on the real-time conditions [19]. A two-layer dynamic scheduling method was proposed to minimize earliness and tardiness in re-entrant production lines. Enhanced schedule quality was obtained by layering decision-making processes to better handle complex production flows [20]. A dynamic scheduling method was introduced for integrated process planning and scheduling tasks affected by machine faults. More robust operational performance was ensured by rapidly adapting schedules to disruptions and maintaining the continuity of production [21]. An intelligent scheduling approach based on deep reinforcement learning was presented for discrete automated production lines.