Green Flexible Job Shop Scheduling Based on Improved Golden Jackal Optimization
DOI:
https://doi.org/10.23055/ijietap.2025.32.5.10881Abstract
To tackle the flexible job-shop scheduling problem (FJSP) in green manufacturing, the multi-objective optimization problem is transformed into a single-objective algorithm through normalization. A model is developed to optimize the objectives of minimizing makespan, machining energy consumption, and completion cost. The Golden Jackal Optimization (GJO) algorithm is enhanced to derive the optimal scheduling scheme. A real-number encoding mechanism is applied to simplify decoding, crossover, and mutation processes. A chaotic sequence is introduced to initialize the population, increasing population diversity and accelerating convergence. Furthermore, crossover and variable neighborhood search mechanisms are integrated to effectively enhance the algorithm's search capability. Comparative analysis based on designed test cases confirms the feasibility and effectiveness of the Improved Golden Jackal Optimization (IGJO) algorithm in addressing the green FJSP. Furthermore, a practical case and statistical testing illustrate the superiority of the IGJO algorithm.
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