Assembly sequence planning based on improved pollination algorithm

Assembly sequence planning is a critical facet of product design and manufacturing, influencing assembly effectiveness and product tolerances. Ineffectual sequences can hamper product quality and development efficiency. This study introduces a matrix-based evaluation function and an enhanced pollination algorithm for optimal assembly sequence determination. This approach accounts for various assembly factors and links the evaluation matrix to fuzzy mathematics through expert decision-making methods. The algorithm is tailored to assembly sequences, with defined rules for judgment, operation, assignment and repair. It incorporates Levy flights for broader search exploration and an adaptive mutation factor for refined local search. The hybrid algorithm, combining genetic algorithm principles, is employed for sequence planning and analysis. Comparative simulations highlight the hybrid algorithm’s robust global search and convergence capabilities. This research offers practical significance in enhancing assembly performance and overall product reliability within the product development cycle.