An Effective Hybrid Local Search Method for Flexible Job-Shop Scheduling Problem in Smart Manufacturing Systems

The Flexible Job-shop Scheduling Problem (FJSP) plays an important role in production and processing in Smart Manufacturing Systems. Unlike the traditional Job-shop Scheduling Problem (JSP), the additional flexibility in machine selection enlarges the search space and increases scheduling difficulty, particularly for large-scale instances. Existing algorithms improve either convergence speed or solution quality, but maintaining both remains difficult as problem size grows. This paper presents a Hybrid Local Search Algorithm (HLS-FJSP), integrating Greedy Search, Genetic Algorithm, and Tabu Search into a two-phase optimization framework. Control parameters and a process monitoring mechanism are used to adjust the search behavior during different optimization stages. Computational experiments on benchmark instances show that the proposed method obtains competitive makespan results compared with several existing algorithms. The results also show stable improvement capability when used for further optimization of existing schedules.