Bi-criteria improved genetic algorithm for scheduling in flowshops to minimise makespan and total flowtime of jobs

Bi-criteria improved genetic algorithm (IGA) for flowshop scheduling problem is proposed in this paper. The primary concern of flowshop scheduling problem considered in this work is to obtain the best sequence, which minimises the makespan and total flow time of all jobs. The initial population of the genetic algorithm is created using popular NEH constructive heuristic (Nawaz et al. 1983). In IGA, multi-crossover operators and multi-mutation operators are applied randomly to subpopulations divided from the original population to enhance the exploring potential and to enrich the diversity of the crossover templates. The performance of the proposed algorithm is demonstrated by applying it to benchmark problems available in the OR-Library. Computation results based on some permutation flowshop scheduling benchmark problems (OR-Library) show that the IGA gives better solution when compared with the earlier reported results.