## 2 Literature Review
### Content Analysis
Traditional routing, assignment and scheduling models and solutions must be transformed to cyber-physical models, like the transformation of conventional scheduling to 3D printing service scheduling demonstrates [28] in the case of cloud manufacturing, where distributed manufacturing resources are encapsulated into services and aggregated [29]. The additive manufacturing can lead to decentralized, flexible production facilities, where the customer's demands can be produced with low financial risk; they are flexible and can respond rapidly to changes in demand [30]. In hybrid production systems, the manufacturing of new and remanufactured products is integrated. In hybrid production systems, special constraints caused by uncertainties in recycling processes must be taken into consideration [31; 32; 33]. The literature introduces a wide range of methods used to solve design problems of logistics processes in manufacturing, like integer programming, decision-making methods, heuristic and metaheuristic algorithms, Petri Net simulation, statistical approaches, simulation and simulation-based optimization, fuzzy modelling, and hybrid optimization approach. Linear programming, integer linear programming, and mixed integer linear programming can also be used for the optimization of logistics processes in manufacturing systems. Researchers developed a multi-objective mixed integer linear programming model to generate efficient solutions minimizing cost and assigning more reliable manufacturers in a dynamic manufacturing network [34]. Clustering algorithms, like K-mean, mean-shift, density-based spatial clustering, or agglomerative hierarchical clustering are widely used in the design of complex in-plant supply systems; they can be combined for multi-stage optimization with heuristic and metaheuristic algorithms. The combination of prioritized K-mean and genetic algorithm was used to optimize manufacturing related transportation processes [35]. Heuristic optimization methods are used in the case of NP-hard optimization problems: genetic algorithm was used to increase machine utilization, reduce throughput time and delivery delays [36; 37], while discrete particle swarm optimization (PSO) was applied to solve the dynamic travelling salesman problem in chip manufacturing, where machine failure can force changes to the problem specification [38]. A typical application field of PSO is flow shop and job shop manufacturing [39] or the machine loading problem in flexible manufacturing systems, where the feeding process is generally robotized or automatized [40]. Heuristic methods can be used not only for the optimization of processes but also for the allocation of IT structure in the manufacturing process: a fruit fly algorithm was used to find the optimal location of the wireless sensor network in the intelligent workshop [41]. The in-plant supply of manufacturing processes is based on complex material handling systems, which were optimized with a hybrid multi-objective artificial immune systems-based algorithm [42]. Manufacturing processes are typical uncertain environments, where fuzzy modelling and fuzzy optimization offers suitable tools [43; 44] and the fuzzy approach can easily integrate with other analytical or heuristic algorithms [45]. Researchers used an integrated data-driven stochastic degradation model to find the optimal maintenance strategy in chemical and manufacturing processes, where unit failures are caused due to equipment degradation [46]. Different types of simulation methods and tools can be used to optimize in-plant and external manufacturing related logistics processes, like discrete event simulation [47], timed Petri net simulation [48], and hybrid simulation integrating discrete and continuous time event simulation [49]. The Petri net modelling, the timed, colored, and fuzzy Petri net approaches are widely spread in the field of simulation of manufacturing related logistics systems [50; 51].