Convergence of Hybrid Grey Wolf Optimization with Heuristic Approaches for Enhanced Job Shop Scheduling
Keywords:
Combinatorial Optimization, Hybrid Grey Wolf Optimization Problem, Job Shop Scheduling Problem, Meta-heuristic Algorithm.Abstract
This scholarly inquiry examines the utilization of the Hybrid Grey Wolf Optimization Algorithm (HGWOA) in addressing the Job Shop Scheduling Problem (JSSP), a combinatorial optimization problem commonly encountered within production management. The central aim is to minimize makespan, defined as the cumulative duration necessary to finalize all tasks on a designated set of machines while observing precedence constraints. Conventional Optimization methodologies frequently encounter difficulties with intricate instances of JSSP owing to its NP-hard classification. We introduce a ground-breaking method the Grey Wolf Optimization Algorithm (GWOA) with various meta-heuristic strategies to augment its fruitfulness in resolving JSSP. The multiple findings underscore the usefulness of HGWOA, highlighting its prospective applicability in real-world contexts of production scheduling and management.
References
T. Jiang, “A hybrid grey wolf optimization for job shop scheduling problem,” International Journal of Computational Intelligence Applications, vol. 17, no. 1850016, pp. 1–12, 2018. Available From: https://doi.org/10.1142/S1469026818500165
X. Kong, Y. Yao, W. Yang, Z. Yang, and J. Su, “Solving the flexible job shop scheduling problem using a discrete improved grey wolf optimization algorithm,” Machines, 2022. Available From: https://doi.org/10.3390/machines10111100
Y. Jieran, W. Aimin, G. Yan, and S. Xinyi, “An improved grey wolf optimizer for flexible job-shop scheduling problem,” in IEEE 11th International Conference on Mechanical and Intelligent Manufacturing Technologies (ICMIMT), 2020, pp. 213–217. Available From: https://doi.org/10.1109/ICMIMT49010.2020.9041184
C. Lin, Z. Cao, and M. Zhou, “Learning-based grey wolf optimizer for stochastic flexible job shop scheduling,” IEEE Transactions on Automation Science and Engineering, vol. 19, pp. 3659–3671, 2022. Available From: https://doi.org/10.1109/TASE.2021.3129439
Y. Wang, Y. Song, Y. J. Zou, Q. Lei, and X. Wang, “A hybrid gray wolf weed algorithm for flexible job-shop scheduling problem,” Journal of Physics: Conference Series, vol. 1828, no. 1, 2021. Available From: https://doi.org/10.1088/1742-6596/1828/1/012162
D. Maharana and P. Kotecha, “Optimization of job shop scheduling problem with grey wolf optimizer and JAYA algorithm,” Smart Innovations in Communication and Computational Sciences, 2018. Available From: https://doi.org/10.1007/978-981-10-8968-8_5
S. Chen and J. Zheng, “Hybrid grey wolf optimizer for solving permutation flow shop scheduling problem,” Concurrency and Computation: Practice and Experience, 2023. Available From: https://doi.org/10.1002/cpe.7942
C. Lu, L. Gao, X. Li, and S. Xiao, “A hybrid multi-objective grey wolf optimizer for dynamic scheduling in a real-world welding industry,” Engineering Applications of Artificial Intelligence, vol. 57, pp. 61–79, 2017. Available From: https://doi.org/10.1016/j.engappai.2016.10.013
C. Zhang, K. Wang, Q. Ma, X. Li, and L. Gao, “A discrete grey wolf optimizer for solving flexible job shop scheduling problem with lot-streaming,” in IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2021, pp. 969–974. Available From: https://doi.org/10.1109/CSCWD49262.2021.9437886
T. Jayabarathi, T. Raghunathan, B. Adarsh, and P. Suganthan, “Economic dispatch using hybrid grey wolf optimizer,” Energy, vol. 111, pp. 630–641, 2016. Available From: https://doi.org/10.1016/J.ENERGY.2016.05.105
T. Jiang, C. Zhang, H. Zhu, and G. Deng, “Energy-efficient scheduling for a job shop using grey wolf optimization algorithm with double-searching mode,” Mathematical Problems in Engineering, 2018. Available From: https://doi.org/10.1155/2018/8574892
S. Luo, L. Zhang, and Y. Fan, “Energy-efficient scheduling for multi-objective flexible job shops with variable processing speeds by grey wolf optimization,” Journal of Cleaner Production, 2019. Available From: https://doi.org/10.1016/J.JCLEPRO.2019.06.151
Pseudocode of grey wolf optimization algorithm, ResearchGate, accessed: https://www.researchgate.net/figure/Pseudocode-of-grey-wolf-optimization-algorithm_fig1_322192028
