## 3 Materials and Methods  
\"What\" decision should be made in real-time production scheduling? Traditional planning systems are not effective at providing a solution to the continuous variations that occur in real-time production scheduling. As a result, various dynamic planning strategies have emerged that use new systems and technologies to propose the best possible solution based on the data obtained. Each strategy focuses on specific variables, objectives and methods. These variables are obtained through various technologies previously referred to as sensors, business systems and big data. For instance, sensors can capture production data to calculate production costs and evaluate production quality in real time. The fundamental strategy for decision making during planning defines the objectives. Some strategies prioritise the objective of energy cost reductions, while others prioritise process time reductions. Some strategies are multi-objective. \"How\" to make real-time scheduling decisions? To determine how to schedule, certain methods are defined and applied, typically based on heuristic and metaheuristic algorithms. These methods aim to find the best possible solution for the given variables, problems and objectives. Production planning involves planning and sequencing the various operations of production orders across different work centres. When manufacturing a product, all operations may take place in a single work centre or involve several work centres. There are methods available for considering the operations of a single work centre in production scheduling. Some of the scheduling methods that can be applied with the previous methods and variables are the SPT (shortest process time), the R&M algorithm (Rachamadugu and Morton), Hogdson, and method weighted methods [2]. At the same time, there are methods that consider the operations of multiple work centres in production planning. These methods take into account the impact of other production operations that must be completed in other work centres. They evaluate different combinations based on the number of orders, quantity, time and characteristics. Depending on the complexity of the combinations, some planning problems can be solved polynomially, and others cannot be solved and are classified as NP, NP-complete or NP-hard problems. In the NP cases, the search can be completed with the optimal solution, while in other cases, the search can be completed with a heuristic solution or a good possible solution [3; 4; 5]. The main methods or strategies used by the authors can be classified as shown in Table 1.  
* Multi-period hierarchical mechanism: [PERSON] et al. [6] proposed a multi-period hierarchical mechanism to optimise production planning and real-time planning for a hybrid workshop flow. The multi-period hierarchical scheduling (MPHS) mechanism was developed based on the decision procedure, in which the proposed scheduling mechanism contains two levels (shop floor decision and work stage decision) in order to control the workload balance. * Multi-agent architecture: [PERSON] et al. [7] developed a multi-agent architecture for a workshop flow. An agent-based architecture was developed that enables the integration of production planning and scheduling, facilitating real-time production scheduling to implement the concept of MAS. * Genetic algorithms ([PERSON], [PERSON]; _Journal of Intelligent Algorithms_) [23]: the grid method and genetic algorithm ([PERSON], [PERSON], [PERSON]) [24], the genetic algorithm with particle swarm optimisation ([PERSON] et al.) [25], and the multi-objective optimisation algorithm ([PERSON] et al.) [8]. A genetic algorithm (GA) is a method commonly used in the real-time scheduling solutions. In this type of metaheuristic algorithm, the optimisation of search problems is inspired by the process of natural selection, which mimics biological evolution. It belongs to the group of evolutionary algorithms. * Game theory: [PERSON] et al. [9] proposed an algorithm based on game theory that considers energy consumption.