Optimizing the first type of U-shaped assembly line balancing problems

In the literature, there are various types of assembly line balancing problems. Consequently, different types of solution approaches such as exact, heuristic, and metaheuristics have been proposed to solve such problems. In this research, we are going to propose a metaheuristic solution method based on applied grouping evolution strategies to solve the ushaped assembly line balancing problem where the aim is the minimization of the number of workstations considering a given cycle time for the assembly line. By introducing the justintime (JIT) production principle, it can be proven that the Ushaped assembly line system has a better performance than the traditional straightline system. Different test problems from the literature are solved and key indexes like the line efficiency, smoothness index, and variation, are calculated for the problems. Then the proposed method is compared to one of the recent solutions approached based on the genetic algorithm. The results show that the proposed method has the potential t be considered as one of the most efficient methods in this field.