An Improved Genetic Algorithm with Tabu Tables for Flexible Job-Shop Scheduling Problems

Flexible job shop systems are the most common manufacturing mode in modern manufacturing industry, with the characteristics of flexible process routes and flexible processing machines. The scheduling of such system should consider both the sequence of operations and the allocation of machines for each operation. To deal with the flexible job shop scheduling problems, a mixed integer-programming model with the objective of minimizing the makespan is established. To solve the model, an improved genetic algorithm with tabu tables was put forward, in which tabu tables and adaptive designs for flexible job shop scheduling problems are skillfully applied in the crossover and mutation operations to prevent iterative regression. Finally, a program was developed with the 10 instances from literature to verify the feasibility and effectiveness of the algorithm. The example validation results demonstrate the effectiveness of the proposed algorithm in solving flexible job shop scheduling problems and its superiority over genetic algorithms.