A hybrid genetic algorithm based on Pareto was proposed and applied to the flexible job shop scheduling problem (FJSP) with bi-objective, and the bi-objective FJSP optimization model was built, where the make-span and the production cost were concerned. The algorithm embeds Pareto ranking strategy into Pareto competition method, and the niche technology and four kinds of crossover operations are used in order to promote solution diversity. Pareto filter saves the optimum individual occurring in the course of evolution, which avoids losing the optimum solutions. This hybrid genetic algorithm reasonably assigns the resources of machines and workers to jobs and achieves optimum on some performance. In this paper, the influence of the proportion of workers and machines on the scheduling result is researched on the basis of the hybrid genetic algorithm and the result is in accord with other researchers. In conclusion, the algorithm proposed in this paper is available and efficient.
Recent Research Trends in Genetic Algorithm Based Flexible Job Shop Scheduling Problems