a bi-objective bi-level mathematical model for cellular manufacturing system applying evolutionary algorithms

The present study aims to design a bi-objective bi-level model for a multidimensional cellular manufacturing system (cms). minimization of the total number of voids and balancing of the workloads assigned to cells are regarded as two objectives at the upper level of the model. however, at the lower level, attempts are made to maximize the workers' interest to work together in a particular cell. to this end, two nested bi-level metaheuristics, including particle swarm optimization (nbl-pso) and a population-based simulated annealing algorithm (nbl-pbsa), were implemented to solve the model. in addition, the goal programming approach was utilized at the upper level of these algorithms. further, nine numerical examples were applied to verify the suggested framework, and the topsis method was used to find a better algorithm. furthermore, the best weights for upper-level objectives were tuned by using a weight sensitivity analysis. based on computational results of all of the three objectives, when decisions about inter-and intra-cell layouts as well as cell formation were simultaneously made in order to balance the assigned workloads by considering voids and workers' interest, making the problem closer to the real world, the outcomes were found different from their ideal. finally, nbl-pbsa could perform better than nbl-pso, which confirmed the efficiency of the proposed framework.