## 3 Materials and Methods
For example, cyber-physical systems can be combined with additional software that applies specific algorithms. These values can also be incorporated into business systems such as MESs and ERP, where they can be complemented with a greater number of data variables outside of production. This enables real-time planning by applying strategies with a wider range of objectives. \"When\" to make decisions in real-time scheduling? Real-time scheduling is defined as dynamic planning by definition. It is the result of a continuous interaction between real-time variable data and the application of methods and algorithms to achieve the desired objectives. Therefore, a continuous analysis of the results and a continuous reevaluation of the most appropriate plan to maintain these objectives are necessary. AMRs and AGVs are used for material handling in the manufacturing process and are responsible for connecting production processes. The control of these robots is essential for real-time production scheduling to connect the tasks in different work centres. Therefore, finding the optimal transport time and equipment for each task with respect to the production specifications is crucial in production planning. AGVs operate with minimal integrated intelligence and move in a guided manner through a closed circuit. In contrast, AMRs rely on software that provides them with real-time maps and do not require a circuit to guide their movement. Optimising the transportation time and allocating both AGVs and AMRs to production logistics tasks are important objectives to be incorporated into real-time programming [28]. Therefore, it is important to consider the most commonly used rules for dispatching AGVs in the real-time scheduling problem, as suggested by [PERSON] and [PERSON] [29]. With this consideration, transport robots as AGVs have been incorporated into the application of heuristic algorithms using different strategies. [PERSON] et al. [30] proposed a genetic algorithm approach to solve the problem of simultaneous scheduling of machines, and proposed using a number of identical AGVs in an FMS to minimise the makespan. [PERSON] et al. [31] proposed an allocation strategy and dynamic optimisation method for shop floor material handling based on multi-source manufacturing data. [PERSON] et al. [32] proposed a mixed-integer linear programming approach and applied a two-stage ant colony algorithm (ACA) to solve a model of job shop scheduling problems and a conflict-free routing problem for AGVs. [PERSON] et al. [33] proposed a multi-objective scheduling approach for AGVs in a flexible manufacturing system (FMS) using a hybrid of the genetic algorithm and the particle swarm algorithm. This approach considers multiple objectives, including minimising the makespan and the number of AGVs. [PERSON] et al. [34] proposed a real-time scheduling approach using deep reinforcement learning to reduce the makespan and delay ratio of AGVs. [PERSON] et al. [35] proposed a multi-population co-evolutionary algorithm to solve a green multi-objective IPPS problem considering a logistics system. This algorithm aims to minimise the energy consumption and maximise the completion time.