## 2 A Division of Manual Labor, Assembly and Production Lines
### Changing Customer Demand and Optimization of Operation Sequences
A hybrid search approach may be used to find the optimum assembly sequence, which will be integrated into an interactive assembly virtual environment. The user can adjust the assembly sequences with obvious good objectives via interaction to improve the performance of the search algorithm. A human-machine cooperation algorithm was proposed, by which a human operator can play a pivotal role instead of pure computing. Numerical experiments were performed to validate the performance of the physical approach to generate an assembly sequence, which showed the efficiency and operability to guide the assembly work. In the article [64], eight multi-objective ant colony optimization algorithms have been developed and compared for solving ten benchmark instances of the SALBP. Experiments on the computer showed that the commonly used heuristic functions deteriorate the performance of the developed algorithms in a limited CPU time scenario. Even neglecting such costs, the developed algorithms achieved a better performance without heuristics. The developed algorithms were ranked according to three multi-objective indicators and the calculated differences between the top four of them were reviewed using statistical tests. The four best-performing algorithms were favorably compared with the other algorithms designed for industrial optimizations. A supplier selection problem is a strategic decision-making activity for building a competitive advantage in assembly production. Quality suppliers can understand a firm's operational goals and provide high-quality components. Achieving efficient production requires a good plan. A superior competitive strategy should consider the suppliers'availability and the plant's ability. In the article [65], production line planning was applied to address specific problems associated with a supplier selection by constructing a multi-objective optimization model. The proposed model includes both assembly sequence planning and assembly line balancing. A hybrid algorithm was proposed to heuristically solve the above problems. The proposed algorithm combines a guided search and a multi-objective particle swarm optimization, as well as a particle swarm optimization. A real case of a computer assembly plant was used to verify the algorithm's performance. The computational results showed that the proposed algorithm identifies non-dominated solutions and obtains high Pareto-optimal solution ratios. Mixed-model assembly lines are widely used in industries where a high variety of products are required in addition to low cost and high responsiveness. When a certain product mix is demanded and different variants require different assembly times on the available workstations, the sequence of variants on the line highly affects the assembly line performance. Although assembly operations are often manual, most sequencing algorithms assume deterministic times for these operations, rendering the obtained results unreliable. The max-plus algebra is a mathematical tool that can model discrete event systems in linear equations analogous to traditional state space dynamic equations. Modeling mixed-model assembly lines with max-plus equations would enable comparing sequences over ranges of values of assembly times, thus increasing the robustness, stability and reliability of the obtained results. In the article [66], mixed-model assembly lines with both closed and open workstations were modeled using the max-plus algebra. The produced models were used to compare possible sequences and to analyze various performance measures of the assembly lines while varying some system parameters. Two examples were presented to demonstrate analyses that can be performed using the proposed model. In the first example, three possible assembly operation sequences were compared and regions of optimality for each sequence were determined. In the second example, the effect of changing the launching rate of work units on the assembly line performance was studied.