an efficient bi-objective genetic algorithm for the single batch-processing machine scheduling problem with sequence-dependent family setup time and non-identical job sizes

This paper considers the problem of minimizing make-span and maximum tardiness simultaneously for scheduling jobs under non-identical job sizes, dynamic job arrivals, incompatible job families, and sequence-dependent family setup time on the single batch- processor, where split size of jobs is allowed between batches. at first, a new mixed integer linear programming (milp) model is proposed for this problem; then, it is solved by ε-constraint method. since this problem is np-hard, a bi-objective genetic algorithm (boga) is offered for real-sized problems. the efficiency of the proposed boga is evaluated to be compared with many test problems by ε-constraint method based on performance measures. the results show that the proposed boga is found to be more efficient and faster than the ε-constraint method in generating pareto fronts in most cases.