A Two-Stage Genetic Algorithm for Large-Size Scheduling Problem

The majority of large-size job shop scheduling problems are non-polynomial-hard (NP-hard). In the past decades, Genetic algorithms have demonstrated considerable success in providing efficient solutions to many NP-hard optimization problems. But there is no literature considering the optimal parameters when designing genetic algorithms. Unsuitable parameters may cause terrible solution for a specific scheduling problem. In this paper, we proposed a two-stage genetic algorithm, which attempts to firstly find the fittest control parameters, namely, number of population, probability of crossover, probability of mutation, for a given job shop problem with a fraction of time; and then the fittest parameters are used in the genetic algorithm for further more search operation to find optimal solution. For large-size problem, the two-stage genetic algorithm can get optimal solution effectively and efficiently. The method is validated based on some hard benchmark problems of job shop scheduling.