Research on Assembly Sequence Planning of Hybrid Power Transmission Device Based on Improved Genetic Algorithm

Abstract

Aiming at the complex optimisation problem involved in the assembly process of DM-i hybrid system, an improved genetic algorithm based on the assembly sequence planning problem is proposed to be investigated. Two matrices, assembly priority and assembly space interference, are used to constrain the assembly relationship of parts, and the feasibility, optimality and flexibility of assembly are considered; the initial population of the genetic algorithm is optimised in terms of algorithmic improvement, and inverse learning is used to generate the initial population and an elite inverse learning mechanism is introduced to avoid the algorithm from falling into a local optimal solution; the search strategy of the algorithm is improved, which consists of four main steps, i.e., binary tournament selection, partial matching crossover, exchange mutation and elite individual retention strategy. The feasibility and superiority of the proposed improved genetic algorithm in solving the assembly sequence planning problem of DM-i hybrid system are verified by example algorithms and on-site assembly verification.