## 1 Introduction  
Nowadays, it is fundamental for many manufacturing companies to provide a wide range of products with minimum lead time. To reach this goal, a production system must be flexible, i.e., manage changes in product and volumes [1]. Despite manual assembly systems (MAS) achieving a high degree of flexibility, several drawbacks limit their usage: the accuracy and repeatability is inferior to that achievable with automatic systems, and ergonomic problems affects the achievable throughput [2]. Therefore, automation is a viable option, thanks to their improved throughput and product quality with reduced production costs in comparison to MAS. Kitting is one of the most critical in terms of throughput and flexibility. Kitting is the process where all the parts required to assemble a product are collected in a kit container, instead of storing the parts along the line-side storage locations, as in line-side stocking [3]. However, this work focuses on kits as the end product of the production line, i.e., the set of the assembled product packed together with its accessories and supplied to the customers as a single product, and which are called sales kits [4]. Different from other assembly technologies, kitting is based on operations with minimum added value to the end product [5], even if from the logistic point of view it creates added value in the form of improved quality and reduced search time [6]; thus, it is critical to increase throughput. The required flexibility often leads to the adoption of manual systems, which creates a range of problems as seen with MAS [7], but also, it is labor intensive. Thus, the adoption of automatic kitting systems is fundamental, but to reach this goal, it is necessary to have flexible systems to minimize the lead times. For an automatic system to be competitive, it should [8] be able to adapt to part and volume changes, be capable of processing multiple parts and models simultaneously, and carry out model changeovers quickly and easily. To improve the flexibility of automatic systems, flexible assembly systems (FAS) have been developed. Flexibility during the fixing phase is achieved by using a programmable robotic manipulator [9; 10]. However, for a FAS to be truly flexible, it is important to ensure that the feeder system provides multiple and different parts quickly. Indeed, the fundamental tasks of partitioning the parts, i.e., the separation and presentation in a certain amount and orientation at a picking location, are carried out by the feeders [11]. This process is critical for the flexibility of the assembly system [12] and should be accordingly studied. Indeed, traditional automatic feeding systems [11] are mainly passive orientating systems, and therefore, they present limited flexibility, i.e., they can provide a limited number of component types at the same time. However, their low cost is appealing for kitting companies. Hence, the workcell may require as many feeders as the number of parts; changing between products requires the set up of usually more than one feeder, thus increasing the setup time. Therefore, it is important to reduce the number of feeder reconfigurations between changeovers to reduce the overall setup time. This work aims to provide a model that minimizes the setup times of automatic kitting systems by adopting a suitable schedule that minimizes the number of retoolings of its feeders. Indeed, the productivity of a logistics system depends on the efficiency of the schedule: if different products are to be made on a single line, it is important to reduce the setup time between each changeover [11]. To achieve this goal, this work adopts a traveling salesman problem (TSP) approach, which aims to minimize the reconfiguration times. TSP is already adopted in the literature to minimize the cost of setup. However, when the number of parts is significant, which is reasonable for sales kitting since it is characterized by high product variety, the computational time may be too large to adopt this method in flexible systems.