An iterative genetic algorithm for the assembly line worker assignment and balancing problem of type‐II

In this study, we consider the assembly line worker assignment and balancing problem of type‐II (ALWABP‐2). ALWABP‐2 arises when task times differ depending on operator skills and concerns with the assignment of tasks and operators to stations in order to minimize the cycle time. We developed an iterative genetic algorithm (IGA) to solve this problem. In the IGA, three search approaches are adopted in order to obtain search diversity and efficiency: modified bisection search, genetic algorithm and iterated local search. When designing the IGA, all the parameters such as construction heuristics, genetic operators and local search operators are adapted specifically to the ALWABP‐2. The performance of the proposed IGA is compared with heuristic and metaheuristic approaches on benchmark problem instances. Experimental results show that the proposed IGA is very effective and robust for a large set of benchmark problems.