Evolution Balancing of Mixed Model-Assembly Line Considering Task Reassignment

Abstract

Market demand and technological progress drive continuous product evolution, upgrade and innovation, which necessitate readjustment and evolution balancing of the mixed model assembly line (MMAL) for improving production efficiency. In the evolution process of MMAL, the rational matching between difficulty of assembly tasks and operating level are mainly considered, the mathematical model of evolution balancing optimization for MMAL is established, and an improved particle swarm optimization algorithm (IPSO) based on leapfrog algorithm is designed. In the process of optimization, the single population is divided into several subgroups for searching, information exchange between species is executed to get better particles and the strategy of returning to the beginning is introduced, in which particle diversity and global search capability are increased and improved, respectively. Finally, the effectiveness and feasibility of the method were validated by evolution balancing planning of MMAL.