Two-sided assembly lines are typically use both sides (left and right) this type of assembly line are found in production of learg-sized products, such as automobile and trucks. Solving mixed model two-side assembly lines under a learning effect problem to achieve multi-objective functions aims at highest effectiveness of assembly lines. This type of problem is known to be NP-hard. A Biogeography-based optimization (BBO) is applied as a method for solving this problem. Four objectives were considered including minimum number of Mated-station, minimum number of Workstations, minimum Work Relatedness and minimum Workload Balance between Workstation. The performance of BBO was compared with the well-known algorithms, namely Non-dominated Sorting Genetic Algorithm II (NSGA-II), Discrete Particle Swarm Optimization (DPSO), and the Particle Swarm Optimization with Negative Knowledge (PSONK). The result shows that BBO is good performance and acceptable.
