A solution of job-shop scheduling problems based on genetic algorithms

Based on the analysis of various ways in solving Job-Shop scheduling problems, the mathematical model of Job-Shop problem is introduced. A kind of genetic algorithm is used to solve it in this paper. Firstly, the chromosomes are encoded, and the population size is premised for the optimized goal of Job-Shop problem, which are the keys of the method. Secondly, the fitness function is designed. After using selection, crossover and mutation operator, and then elitist strategy to prevent the premature convergence, a best or satisfactory scheduling path can be found. The executing results using the presented algorithms in certain project case are shown in the paper.