Assembly line balancing is an important and well recognised operations research problem. The current line balance may not stay optimal, even feasible, due to the disruptions in one or more workstations. In this study, after the disruption, we aim to rebalance the assembly line by considering the trade-off between workload balancing (fairness measure) and total replacement distance for the tasks assigned to the different workstations (stability measure). We try to generate all non-dominated objective function vectors for the defined fairness and stability measures. Two algorithms are developed: exact algorithm (classical approach) and tabu search algorithm. The results of the experiments have shown that the classical approach returns exact non-dominated objective vectors with up to 40 tasks and 7 workstations in one hour, and the tabu search algorithm returns approximate non-dominated objective vectors that are very close to their exact counterparts and can solve large sized instances with up to 94 tasks and 7 workstations in less than 10 s.