Genetic algorithm based multi-objective scheduling in a flow shop with batch processing machines

In this paper, the problem of minimizing makespan and the total tardiness in a flow shop with batch processing machines (BPM) is considered and an efficient genetic algorithm (GA) is presented, in which job permutation is the only optimization object and the solution of problem can be directly obtained using the permutation. To obtain a set of non-dominated solutions, a rank and the weighted objective based binary tournament selection and an external archive updating strategy are also adopted. The proposed GA is finally tested and the computational results show its promising performance on multi-objective scheduling of flow shop with BPM.