A novel meta-heuristic approach to solve fuzzy multi-objective straight and U-shaped assembly line balancing problems

The consideration of this study is devoted to deal with the straight and U-shaped assembly line balancing problems (ALBPs). The ALBP involves allocation of required tasks to a set of workstations, so that objective functions being optimized are subjected to set of constraint. While many efforts have been dedicated in the literature to develop deterministic model of the assembly line, the attention is not considerably paid to those in uncertain circumstances. In this paper, along with proposing a novel fuzzy model for ALBP, triangular fuzzy numbers are deployed with to respect vagueness and uncertainty subjected to the task processing times. For this purpose, two conflicting objectives are considered simultaneously with regard to set of constraints, so that the efficiency of the line has to be maximized. To solve the problem, a modified NSGA-II, which utilized a new repairing mechanism, is proposed in response to the need of appropriate method treating such complicated problems. The validity of the proposed model and algorithm is evaluated and proved though a benchmark test problem. The obtained results reveal that in contrast to benchmark that applied an exact solution procedure, the proposed algorithm is capable of delivering the astonishing solutions in a more effective procedure. Along with the use of NSGA-II, in this study, three well-known meta-heuristic algorithms, namely PESA-II, NSACO and NPGA-II, are also employed for solving the problem in order to evaluate the effectiveness of the proposed algorithm, so that the results demonstrate the high performance for the NSGA-II over them. Finally, in light of the obtained results, this study offers an efficient framework enabling the decision maker to handle uncertainty in ALBPs along with the use of an efficient algorithm to solve them.