Solving job-shop scheduling problems by genetic algorithm

Job-shop scheduling problem (JSP) is one of extremely hard problems because it requires very large combinatorial search space and the precedence constraint between machines. The traditional algorithm used to solve the problem is the branch-and-bound method, which takes considerable computing time when the size of problem is large. We propose a new method for solving JSP using genetic algorithm (GA) and demonstrate its efficiency by the standard benchmark of job-shop scheduling problems. Some important points of GA are how to represent the schedules as an individuals and to design the genetic operators for the representation in order to produce better results