An expanded genetic algorithm for stochastic job-shop scheduling problems

This paper proposes a method for solving stochastic job-shop scheduling problems using a genetic algorithm in uncertain environments. The genetic algorithm in uncertain environments is applied to stochastic job-shop scheduling problems where the processing times are treated as stochastic variables. The Roulette strategy according to fitness function values is adopted for selecting the optimum solution having the minimum expected value for makespan and controlling the evolution direction of the generation. Applying crossover algorithm results in offspring inheriting the parents' eximious gene segment. Crossover/mutation gene recombining algorithms are introduced to ensure the new individual meet technics constraints. Individuals having very high frequency through all generations are selected as the good solutions. In addition, an instance shows that the algorithm possesses validity and good prospects of application in stochastic job-shop scheduling problems.