Evolutionary algorithms for multi-objective flexible job shop cell scheduling

The multi-objective flexible job shop scheduling in a cellular manufacturing environment is challenging real-world problem. This recently introduced problem variant considers exceptional parts, intercellular moves, transportation times, sequence-dependent family setup and recirculation requiring minimization of makespan total tardiness, simultaneously. A previous study shows that the exact solver based on mixed-integer nonlinear programming model fails to find an optimal solution each ‘medium’ ‘large’ size instances considering even simplified version In this study, we present evolutionary algorithms for solving bi-objective apply genetic memetic use three different scalarization methods, including weighted sum, conic, tchebycheff. performance all with various configurations investigated across forty-three benchmark from ‘small’ large instance. experimental results show transgenerational algorithm using sum outperforms rest producing best-known almost cell instances, overall. • variety novel components proposed. It has via methods. We have tailored one-point crossover provide feasible solution. used hill-climbing method neighborhood structures.