## 2 Literature Review  
An analysis of the state of the art should be carried out to identify the different approaches adopted in the literature to reduce the setup time, with a major focus on kitting processes. As seen in Table 1, a major part of the literature regarding the reduction of setup times is focused on job shop floor scenarios. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Author**} & \multirow{2}{*}{**Year**} & \multicolumn{4}{c}{**Industry**} & \multicolumn{4}{c}{**Objective**} & \multicolumn{4}{c}{**Model**} & \multirow{2}{*}{**Results**} \\ \cline{2-2} \cline{4-14}  & & **Job Slope** & **Assembly** & **Kitting** & **Traelines** & **Promit** & **Makepan** & **Stap Time** & **Other** & **Search Algorithm** & **CA** & **Hewritic** & **SMED** & **Other** \\ \hline  
\hline \end{tabular}
\end{table} 
Table 1: Summary of the literature review on setup reduction methods in the last decade. Indeed, the literature on kitting processes is mainly focused on comparison with other feeding policies [27], such as line-side stocking [6; 28], focusing more on product kits [4]. Moreover, the kitting processes studied are mainly manual [29], preferred due to the flexibility of the operator, whereas automatic systems are more focused on providing technical solutions [30; 31; 32], providing cost evaluating models [33] or estimating the performance [34]. However, automating the kitting process is fundamental since kitting is labor intensive; hence, it is considered a valid solution to increase productivity [16]. Despite automated storage and retrieval systems having been adopted to improve logistics since 1950s, traditional systems based on stacker cranes [35] are not suitable for kitting processes in small areas; hence, mobile robotic systems are studied in the literature [36]. One of the main problems when adopting mobile robots is optimally performing all the tasks to minimize the schedule length or duration [37] to minimize the setup time of the workcells. From our review, it is possible to distinguish two main methods. The first approach is based on the reduction in the setup time by acting on the setup operations, i.e., the single minute exchange of die (SMED) methodology. Applied since the 1950s [25], this approach is based on the identification of two types of setup operations [38]:  
* Internal setup activities, which can be performed only when the machine is stopped;
* External setup activities, which can be performed even when the machine is operating. Through this, it is possible to focus only on and to improve the internal activities, as seen in [23]. The authors developed a new clamping system; by applying the SMED approach, it was possible to reduce the changeover time from 399 min to 120 min. However, when working on a complex system, SMED may not be sufficient to obtain successful solutions [20]. Indeed, manufacturing systems are complex, as they are comprised of a set of different machines connected by automated transfer systems, and their relationship is not included in the SMED approach. As shown by the author, considering also a balancing approach improves the achievable setup time reduction, from 40% to 79%. The flexible job shop scheduling problem, which consists of scheduling a set of operations on a set of flexible machines, is known to be NP-hard [39]. Hence, to solve large-scale problems, which are more akin to real industrial scenarios, meta-heuristics are usually employed [21]. Genetic algorithms (GA) have been considered. [PERSON] and [PERSON] [21] proposed a GA based on different strategies for initial population, crossover, and mutation operations to improve its efficiency. [PERSON] [14] presented a multi-objective GA, which aims to minimize the makespan, machine load, and total load. [PERSON] et al. [26] proposed a comparison between a GA and a heuristic to solve hybrid flow shop scheduling with a setup time characteristic for each family.