Solving a multi-objective mixed-model assembly line balancing and sequencing problem

This research addresses the mixed-model assembly line (MMAL) by considering various constraints. In MMALs, several types of products which their similarity is so high are made on an assembly line. As a consequence, it is possible to assemble and make several types of products simultaneously without spending any additional time. The proposed multi-objective model considers the balancing and sequencing problems, simultaneously. Based on the assembly problem, the various tasks of models are assigned to the workstations, while in the sequencing problem, a sequence of models for production is determined. The two meta-heuristic algorithms, namely MOPSO and NSGA-II are used to solve the developed model and different comparison metrics are applied to compare these two proposed meta-heuristics. Several test problems based on empirical data is used to illustrate the performance of our proposed model. The results show that NSGA-II outperforms the MOPSO algorithm in most metrics used in this paper. Moreover, the results indicate that our proposed model is more effective and efficient to assignment of tasks and sequencing models than manual strategy. Finally, conclusion remarks and future research are provided.