## 2 A Division of Manual Labor, Assembly and Production Lines
### Preventing Maintenance and Worker Assignment Problems  
The article [56] addresses the mixed-model ALBP, considering preventive maintenance scenarios. A mixed-integer mathematical programming was developed in order to optimize a cycle time and assembly operation alteration. A cooperative algorithm was proposed to simplify a large-sized ALBP due to the divide-and-conquer procedure. An archive was generated to save the obtained complete solutions with better performances, evaluating the fitness of solutions. A mixed-model variable decoding procedure was designed to speed-up the decoding process of the proposed algorithm. An inter-population crossover operator was designed. Four objective-oriented neighbor search operators were proposed to promote the convergence performance of the proposed algorithm. Experimental computational results demonstrated that the algorithm allows obtaining the Pareto solutions for small-sized instances of the mixed-model ALBP. This algorithm outperformed other ones. The obtained Pareto front was close to the true Pareto front. In the article [57], paced and un-paced assembly lines were compared via simulation on the computer. Human operators can speed-up their processing times when it is needed either to feed other workers downstream or to unblock upstream workers. In the study [57], it was found that un-paced assembly lines were superior to paced assembly lines for some real-world settings, e.g., in the mixed-model production environments with a long assembly line length. The benefit of such assembly lines has been overestimated in previously published studies because of simplifying assumptions, such as disregarding the state-dependent behavior or worker fatigue. With an inhomogeneous workforce, the assembly line efficiency was more sensitive to worker placement. In the un-paced assembly lines, an inexperienced human operator should be placed in the middle of the assembly line; while in paced assembly lines, an inexperienced worker should be placed at the first workstation of the assembly line. Assembly operators capable of speeding up should be placed in the middle of the assembly line in both tested types of assembly lines. In the article [58], the preventive maintenance in the assembly line balancing problem is investigated for improving the production efficiency and smoothness. For such a two-objective problem, a heuristic rule based on the tacit knowledge and gene expression programming was developed to obtain a good heuristic solution rather quickly. A grey -wolf optimizer was developed in order to achieve a Pareto front solution. The neighbor operators prevent the developed algorithm from trapping into local optima. The conducted computational experiments demonstrated that the heuristic rule used outperformed other published rules. A real-world case study was conducted in order to validate the proposed heuristic rule and the developed meta-heuristic rule. In the assembly lines, it is reasonable to assume that assembly operation durations are the same for each human operator. In sheltered work centers for disabled workers, this assumption is not valid. Some human operators may execute some assembly operations considerably slower than others capable of executing them. Worker heterogeneity leads to problems, called an assembly line worker assignment and line balancing problem. For a fixed set of workers, this problem is to maximize the production rate of an assembly conveyer by assigning human operators to available workstations and assembly operations to workers, while satisfying precedence constraints between the given assembly operations. In the article [59], a heuristic algorithm and an exact algorithm to solve this problem are introduced. A mixed-integer programming formulation for this problem was also presented. The proposed heuristic algorithm is based on a beam search. The exact algorithm was a branch-and-bound method, which used reduction rules and obtained lower bounds for exact solutions.