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 optimization 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 assignment rules; three effective neighborhoods are developed to make refinement of existing solutions in the bird improvement phases; and temperature acceptance and competitive mechanism 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 outperforms the compared algorithms.