The Job Shop Scheduling Problem (JSSP) is a combinatorial optimization problem that is NP-hard and highly complex, particularly in modern manufacturing environments associated with industry. Conventional metaheuristic methods such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are capable of generating solutions in a relatively short time; however, they often face limitations in solution quality due to premature convergence and limited adaptability to dynamic problem conditions. In contrast, deep learning approaches such as Sequence-to-Sequence (Seq2Seq) offer strong representational capabilities for modeling operation sequences, although they still encounter challenges related to training stability and generalization. This study proposes a hybrid approach that integrates a Seq2Seq encoder–decoder architecture with an adaptive metaheuristic mechanism to enhance scheduling optimization performance. The Seq2Seq model is utilized to learn underlying patterns in operation sequences, while the adaptive mechanism dynamically adjusts search parameters based on makespan evaluation. The experiments are conducted using datasets from the OR-Library, specifically the 10×10 and 15×15 scenarios, to evaluate the performance and scalability of the proposed method. The experimental results demonstrate that the Seq2Seq + adaptive metaheuristic approach consistently produces lower makespan values compared to GA and PSO. For the 10×10 dataset, the proposed method achieves a makespan of 932, outperforming GA (1095) and PSO (1047). Similarly, for the 15×15 dataset, it attains a makespan of 1050, which is better than GA (1250) and PSO (1200). Although the proposed approach requires slightly longer computational time, the improvement in solution quality indicates that it effectively balances exploration and exploitation.
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