An Improved Genetic Algorithm for Solving the Multi-Objective Flexible Job Shop Scheduling Problem - Chen, Lei; Lazaro, Joan P. (International Journal of Computing Sciences Research; Vol 9 (2025): Volume 9, 2025)
Purpose – Aiming at the flexible job shop scheduling problem (FJSP), a multi-objective scheduling model with the maximum completion time and the minimum total processing energy consumption was constructed as the optimization objectives. Method – To optimize the performance of the genetic algorithm, a diversified population strategy was adopted, combining elite retention and a random roulette wheel selection mechanism to screen and retain offspring with excellent performance carefully At the operational level of the algorithm, an efficient single-point crossover strategy was adopted for the process coding part to promote the combination and transmission of excellent characteristics. For the machine allocation part, a flexible random crossover method was introduced to increase the diversity of solutions. To explore new solution spaces and avoid premature convergence, a single-point mutation mutation strategy based on minimum machine energy consumption was designed. Results – Simulated the workshop manufacturing of six machines and six job positions in a company. Compared to the original scheme, the optimized one reduced the production interval by 19.67%, proving its effectiveness. Conclusion – There are other important objectives in actual production, and dual objective optimization may not fully reflect the complexity and diversity of the production system. In addition to dual objective optimization, considering multi-objective optimization methods may better capture the authenticity of the production system comprehensively. Recommendations – By optimizing production scheduling, overall production efficiency can be improved and profit margins can be increased. Practical Implications – This study provides a solution to improve production efficiency and reduce energy consumption in manufacturing environments. The proposed method has been validated through enterprise cases, demonstrating its effectiveness in optimizing maximum completion time and total energy consumption. Implementing this algorithm can improve profit margins and promote more sustainable production practices, aligning with the goals of modern manufacturing.