## 2 A Division of Manual Labor, Assembly and Production Lines
### Generalizations of the SALBP and Complexity of These Problems
The robotic ALBP is determined for a robotic assembly line, where a set of similar or different assembly robots may be included in the assembly conveyer. An assembly robot may need different assembly times to perform an assembly operation, because of different specializations. The solution to such an ALBP includes attempts for optimally assigning the robots to the ordered workstations and balancing the distribution of work between the workstations. It is necessary to maximize the production rate of the assembly conveyer. In the paper [51], a genetic algorithm was developed in order to find a heuristic solution to the considered problem. Different heuristic procedures were used for adapting the genetic algorithm to the ALBP. The assigning robots with different capabilities to the workstations were investigated based on a recursive assignment procedure and a consecutive assignment one. The genetic algorithm was improved by a local optimization (hill climbing) of the workpiece. The conducted computational tests on randomly generated instances showed that the assignment procedure achieves a better solution quality (an average cycle time), while other computational tests determine a better combination of parameters for the genetic algorithm. A comparison of the genetic algorithm with a truncated branch-and-bound method for the ALBP demonstrated that the genetic algorithm provided closed results faster than the exact branch-and-bound method. Workload smoothing on assembly conveyers that aims to evenly assign assembly operations to different workstations may support workforce planning and resource optimization. In the paper [52], the authors studied smoothing conveyers and developed an algorithm to heuristically solve a large-sized problem. To find a good heuristic solution, this algorithm uses a set of known rules for assigning assembly operations based on a probabilistic procedure for closing workstations. A computational experiment was conducted for selecting the best-performing priority rules and for tuning the probabilistic procedure. The efficiency of the proposed algorithm was experimentally tested on the computer. Since the industrial robots are utilized in U-shaped assembly lines to replace human operators, the focus of such assembly lines is not only on productivity, but also on the carbon and noise emissions. In the paper [53], a multi-objective mixed-integer non-linear programming is proposed to minimize carbon emissions, noise emissions and cycle time, concurrently. In the proposed approach, quantifying a carbon emission and a noise emission was achieved via presentations connected with processing times of assembly operations and industrial robots. Existing constraints of the precedence relations were readjusted into an integrated formula to remove worthless equations and to improve the computational efficiency. A hybrid Pareto grey wolf optimization was used to heuristically solve these multi-objective problems. The algorithm included a code to initialize the wolves and designed two searching procedures to update the position of the wolves. Two crossover operators were designed to enhance the communication between the low-grade wolves. The algorithm was compared with five other multi-objective algorithms and the computational results indicated that the proposed algorithm outperforms the compared algorithms in the evaluation metrics of the convergence, maximum spread and hyper-volume ratio. The developed algorithm can achieve the trade-off in reducing carbon and noise emissions and minimizing the cycle time. In the paper [54], a learning effect was studied in the ALBP. In many realistic settings, the produced workers (or machines) continuously develop by repeating the same or similar activities. The production time of the product shortens if it is processed later. It was shown that polynomial solutions can be obtained for both SALBP and U-shaped ALBP with a learning effect. In a mass manufacturing, neither human operators nor industrial robots alone can efficiently perform all assembly operations.