## 1 Introduction  
Considering the tremendous competitiveness distinguishing today's production environment, the optimal design of assembly systems is a necessary requirement to consolidate and enhance the competitive advantage of most manufacturing companies [1, 2]. In particular, the assembly of large products such as cars, buses, tracks, automatic machines, and white goods requires the combination of a great number of components and parts, and it represents a relevant production process in terms of space and resource utilization [3]. In addition, assembly activities still involve a large workforce since most of the tasks require manual labor due to handling skills, acquired experience, and specific competencies difficult to be taught to autonomous robots [4, 5]. In this context, assembly system performance optimization plays a crucial role in terms of productivity growth and cost reduction; indeed, even small improvements can lead to significant monetary savings. Accordingly, the workforce, the layout, and the equipment should be properly designed and managed through efficient assembly line balancing and synchronization. Although the pressure to improve these assembly system performances is relevant in several industries, such as automotive, the literature contributions about this field of research are not abundant [6]. Traditionally, the aim of the assembly line balancing problem (ALBP) is to assign a set of tasks to the workstations composing the assembly line optimizing one or more objectives without violating any restriction imposed for the line [7]. These restrictions typically include cycle time fulfillment and other technological or organizational constraints represented by the task precedence diagram [8]. These basic limitations characterize the simple assembly line balancing problem (SALBP) [9], which considers no task assignment limitations aside from those related to precedence constraints, a single worker per workstation, and all stations equally equipped with resources. These hypotheses rarely occur in real industrial contexts; thus, several studies have recently evolved toward formulating and solving generalized assembly line balancing problems (GALBPs) with different additional characteristics [10; 11]. [PERSON] and [PERSON] [12] investigated the possibility of classifying GALBPs depending on multiple factors, including the number of workers per station. Considering this criterion, ALBPs can be categorized into three groups--simple, two-sided, and multi-manned lines. An example of such assembly line configurations is shown in Figure 1. In a two-sided assembly line arrangement, two workers per station execute different tasks on both sides of the line. In most cases, some tasks have necessarily to be processed on a specific side of the line while others may be executed on either side [13]. On the contrary, multi-manned assembly lines are characterized by several workers in each station who simultaneously perform different tasks on the same product [14]. The leading goal of multi-manned assembly lines is to minimize the total workstation number while the overall efficiency of the line, in terms of worker number and their saturation, remains optimal or near-optimal. The balancing of assembly lines with multi-manned workstations involves two decisions, namely, (1) how many workers should be allocated to each station without exceeding the maximum feasible number of workers performing tasks together on the same product and (2) which subset of tasks should be assigned to each worker and in which chronological order. The main difference between the assignment of tasks in simple and two-sided lines compared to multi-manned ones is the relevance of the temporal sequence in which the tasks are performed by the workers. For traditional lines, the sequence of the tasks within a workstation is not relevant as long as it fulfills the precedence constraints.