Collaborative robots (cobots) are increasingly used to help human workers perform assembly tasks or complete assembly tasks themselves in assembly lines. The ergonomic risks of human workers are a key factor influencing assembly line efficiency. Therefore, this study investigates the mixed-product-model assembly line balancing and sequencing problem (ALBSP) with cobots, considering ergonomic risks in cases where human workers and cobots can operate different tasks in parallel. A mixed-integer programming model is formulated to optimize the makespan and ergonomic risks; this model can solve small-scale instances optimally using the CPLEX solver. A Q-learning-based multiobjective coevolutionary algorithm (QMOCEA) is then developed to handle large-scale instances. This algorithm adopts five vectors for encoding: the task assignment vector handles the task allocation subproblem, the worker allocation vector handles the worker allocation subproblem, the cobot allocation vector handles the cobot allocation subproblem, the process alternative selection vector handles the process alternative selection subproblem, and the product model sequencing vector handles the product model sequencing subproblem. Additionally, this algorithm uses knowledge-based decoding and initialization to obtain high-quality initial solutions. A parameter self-update strategy is proposed to adjust algorithm parameters dynamically. Comparative analysis demonstrates that the proposed method outperforms the original version and exhibits promising performance in comparison with benchmark methods, achieving the highest average hypervolume (HV) ratio of 0.805 and the lowest inverted generational distance (IGD) of 0.028 across 22 instance groups.

In practical assembly enterprises, customization and rush orders lead to an uncertain demand environment. This situation requires managers and researchers to configure an assembly line that increases production efficiency and robustness. Hence, this work addresses cost-oriented mixed-model multimanned assembly line balancing under uncertain demand, and presents a new robust mixed-integer linear programming model to minimize the production and penalty costs simultaneously. In addition, a reinforcement learning-based multiobjective evolutionary algorithm (MOEA) is designed to tackle the problem. The algorithm includes a priority-based solution representation and a new task-worker-sequence decoding that considers robustness processing and idle time reductions. Five crossover and three mutation operators are proposed. The Q -learning-based strategy determines the crossover and mutation operator at each iteration to effectively obtain Pareto sets of solutions. Finally, a time-based probability-adaptive strategy is designed to effectively coordinate the crossover and mutation operators. The experimental study, based on 269 benchmark instances, demonstrates that the proposal outperforms 11 competitive MOEAs and a previous single-objective approach to the problem. The managerial insights from the results as well as the limitations of the algorithm are also highlighted.

In a realistic two-sided assembly line, a preventive maintenance (PM) activity may cause a stoppage of the whole line and a waste of capacity in most stations. To promote production continuity, multiple interchangeable task assignment schemes are required, each targeting one of the regular and PM scenarios. Yet previous studies have not solved the resulting two-sided assembly line balancing problem considering PM scenarios (TALBP-PM), and the domain knowledge deserves extraction. Hence, a multiobjective mixed-integer linear programming model is formulated to minimize cycle times and total task adjustment simultaneously, and a knowledge-assisted variable neighborhood search (KVNS) is customized. Specifically, a decoding mechanism with idle time reduction is proposed to achieve schemes with the shortest cycle times. A rule-based initialization relying on the externalization of implicit relations among unique attributes is designed to derive a high-quality initial solution. Supported by the critical station and task knowledge, objective-oriented neighborhood structures are developed to generate neighbor solutions with increasingly better objectives. Besides, a restart operator adaptive to multidomain knowledge is refined to escape from local optima. Computational results show that the knowledge assistance is effective, and KVNS is superior to other state-of-the-art meta-heuristics in achieving well-converged and -distributed Pareto fronts of TALBP-PM.

Abstract The collaborative robots (cobots) are increasingly being utilized in industries due to the advancement in the field of robotic technology and also due to the increase in labor costs. The cobots on the assembly line can be utilized to complete the tasks independently or assist the workers to complete the tasks. This study considers the U-shaped assembly line balancing problem with cobots, where several cobots with different purchasing costs are selected under the budget constraint. Three mixed-integer programming models are formulated to optimize the cycle time, and the built models are capable of solving the small-sized instances optimally. Two algorithms, artificial bee colony algorithm and migrating bird optimization algorithm, are developed and improved to tackle the large-sized instances, where new encoding scheme and decoding procedure are developed for this new problem. The computational tests demonstrate that the utilization of collaborative robots reduces the cycle time effectively in the assembly line. The comparative study on a set of instances shows that the proposed methodologies obtain competing performance in comparison with other 12 implemented algorithms.