Single-objective flexible job-shop scheduling problem based on improved dung beetle optimization

Given the characteristics of the flexible job-shop scheduling problem and the practical production of a given enterprise, a flexible job-shop scheduling model was proposed to minimize the maximum completion time. A novel algorithm was proposed to solve the model by integrating the dung beetle optimization algorithm and the simulated annealing algorithm. Algorithmic improvements include the design of a single-layer process encoding scheme with machine selection during decoding to ensure a higher level of the initial population. During population update, the dung beetle optimization algorithm was applied for optimization, followed by simulated annealing operations to enhance the convergence speed and solution quality of the algorithm. Through simulation experiments and comparisons with other algorithms, the effectiveness and superiority of the proposed algorithm were validated. In addition, the feasibility of the algorithm was tested through a real-world factory production case. In conclusion, the improvements made in this paper to the algorithms and scheduling models offer valuable insights into the educational aspects of job-shop scheduling. For instance, the single-layer encoding proposed herein simplifies the coding process, making it more accessible for beginners. Additionally, the accompanying decoding strategy yields relatively higher-quality initial solutions, facilitating subsequent optimization processes by accelerating convergence without compromising solution quality. Students were able to gain a better understanding of real workshop conditions through this project, going beyond the sole goal of minimizing completion time. They began to consider more complex situations in the machining process, such as machine breakdowns, changes in machining schedules, and the load on bottleneck machines and total machine load. This allowed students to have a holistic view of a complex production workshop. In terms of education, the project improved students' ability to consider practical aspects when solving problems and provided them with a way to solve problems.