Simultaneous solving of balancing and sequencing problems in mixed-model assembly line systems

Recently, the mixed-model assembly line (MMAL) has been widely studied by many researchers. In fact, there are two basic problems, namely balancing and sequencing problems, which have been investigated in a lot of studies separately, but few researchers have solved both problems simultaneously. Regarding this, the best results in minimising total utility work have been gained by developing a co-evolutionary genetic algorithm (Co-GA) so far. This paper provides a mixed-integer linear programming (MILP) model to jointly solve the problems. Because of NP-hardness, an evolution strategies (ES) algorithm is presented and evaluated by the same test problems in the literature. Two main hypotheses, namely simultaneous search and feasible search, are tested in the proposed algorithm to improve the quality of solutions. To calibrate the algorithm, a Taguchi design of experiments is employed. The proposed ES is compared with the modified version of Co-GA and the MILP model results. According to numerical experiments and statistical proving, the proposed ES outperformed the modified Co-GA from two points of view: the objective function and the computational time. Additionally, the meta-heuristic algorithms are examined in terms of other well-known criteria in MMAL. Finally, the contribution of each hypothesis in accounting for this superiority is analysed.