An efficient migrating birds optimization algorithm with idle time reduction for Type-I multi-manned assembly line balancing problem

Multi-manned assembly line, which is broadly utilized to assemble high volume products such as automobiles and trucks, allows a group of workers to assemble different tasks simultaneously in a multi-manned workstation. This additional characteristic of parallel operators increases the complexity of the traditional NP-hard assembly line balancing problem. Hence, this paper formulates the Type-I multi-manned assembly line balancing problem to minimize the total number of workstations and operators, and develops an efficient migrating birds optimi-zation algorithm embedded into an idle time reduction method. In this algorithm, a new decoding mechanism is proposed which reduces the sequence-dependent idle time by some task assign-ment rules; three effective neighborhoods are developed to make refinement of existing solutions in the bird improvement phases; and temperature acceptance and competitive mecha-nism are employed to avoid being trapped in the local optimum. Comparison experiments suggest that the new decoding and improvements are effective and the proposed algorithm outper-forms the compared algorithms.