Abstract: In the current manual assembly workshops, worker assignment heavily relies on the experience of managers, often resulting in low scheduling efficiency and uneven workloads among workers. To address this issue, studying the multi-stage hybrid flow shop worker assignment problem, considering various human factors such as skill types, skill levels, and age, is crucial. For this problem, a scheduling optimization model is established with the objectives of minimizing order completion time and balancing worker loads. An improved NSGA-Ⅱ algorithm is proposed. A multi-layer chromosome structure coding method is developed to encode workers for different stages. A chromosome crossover and mutation method is introduced to account for the impact of worker skill types. To prevent the algorithm from falling into local optima, a local search strategy based on a mutation method is proposed. Taking a soldier equipment production workshop as an example, 32 test cases are designed for algorithm comparison experiments. The results demonstrate that the proposed algorithm outperforms six other algorithms in terms of stability, convergence, and solution quality. Providing a novel method for solving the multi-stage hybrid flow shop worker assignment problem.

Key words: assembly workshop, worker assignment, human factors, improved NSGA-Ⅱ algorithm