Multi-objective optimization method research on flexible job shop scheduling problem

To solve the multi-objective optimization problem in flexible job shop scheduling, the multi-objective scheduling optimization model, namely the cost, quality and term, was constructed. While the traditional genetic algorithm which combined random weigh could not solve the multi-objective scheduling optimization problem commendably. An improved strength Pareto evolutionary algorithm was employed to optimize the multi-objective optimization model parallelly. As a result, the optimal schema of flexible job shop scheduling was presented in the form of Pareto optimal sets. At last, an instance related with the project in the air separation equip industry was given to prove that the proposed method could solve multi-objective optimization problem in flexible job shop scheduling effectively.