A multi-objective genetic-algorithm for mixed-model assembly line rebalancing problems

In this paper we consider the mixed model assembly line rebalancing problem in the context of seasonal production which is characterized by remarkable changes of products portfolio over seasons. Starting from a given line balancing strategy the goals are to minimize: (1) the total processing time of reassigned tasks (TTRT) to measure rebalancing cost, combining the reassigned tasks quantity and difficulty of these tasks, which is a refinement of minimizing number of reassigned tasks for rebalancing cost measurement proposed by Gamberini et al.; (2) both the sum of differences between the real station time and cycle time, and total differences of models´ station time, which have been known as vertical balancing and horizontal balancing for mixed model assembly line balancing problem. A Multi-objective Genetic-algorithm (MOGA) is used to deal with this mixed model rebalancing problem. To test the MOGA, a small instance is tested.