A genetics algorithm for solving job-shop scheduling problems in FMS

Job shop scheduling problem (Job shop Scheduling Problem) is one of the hardest combinatorial optimization problems. Due to its nonlinear characteristic and NP hard, the traditional algorithm cannot solve this problem. This paper proposes a genetic algorithm to solve the classic job shop machine scheduling problems and emphasizes on the genetic algorithms coding so as to present a coding mode based on the relationship between machines and different working procedures. On this point, this paper aimed at designing a new crossover and mutation genetic operators to perform a global search. Furthermore, the experiments are conducted and the results show that this method has high efficiency in solving the large scale job shop problem.