A novel hybrid archimedes optimization algorithm for energy-efficient hybrid flow shop scheduling

(1) * Dana Marsetiya Utama Mail (Departement of Industrial Engineering, University of Muhammadiyah Malang, Malang, Indonesia)
(2) Ayu An Putri Salima Mail (Industrial Engineering Department, University of Muhammadiyah Malang, Malang, Indonesia)
(3) Dian Setiya Widodo Mail (Manufacturing Engineering Department, University of 17 Agustus 1945 Surabaya, Indonesia)
*corresponding author

Abstract


The manufacturing sector consumes most of the global energy and had been in focus since the outbreak of the energy crisis. One of the proposed strategies to overcome this problem is to implement appropriate scheduling, such as Hybrid Flow Shop Scheduling. Therefore, this study aims to create a Hybrid Archimedes Optimization Algorithm (HAOA) for solving the Energy-Efficient Hybrid Flow Shop Scheduling Problem (EEHFSP). It is hoped that this helps to provide new insights into advanced HAOA methods for resolving the EEHFSP as the algorithm has the potential to be a more efficient alternative. In this study, three stages of EEHFSP were considered in the problem as well as a sequence-dependent setup and removal times in the second stage. Experiments with three population variations and iterations were presented for testing the effect of HAOA parameters on energy consumption. Furthermore, ten job variations are also presented to evaluate the performance of the HAOA algorithm and the results showed that HAOA iteration and the population did not affect the removal and processing of energy consumption, but impacted that of setup and idle. The comparison of these ten cases revealed that the proposed HAOA produced the best total energy consumption (TEC) when compared to the other algorithms.

Keywords


Energy Consumption; Hybrid Flow Shop; Scheduling; Archimedes Algorithm

   

DOI

https://doi.org/10.26555/ijain.v8i2.724
      

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References


[1] L. Meng, C. Zhang, X. Shao, and Y. Ren, “MILP models for energy-aware flexible job shop scheduling problem,” J. Clean. Prod., vol. 210, pp. 710–723, Feb. 2019, doi: 10.1016/j.jclepro.2018.11.021.

[2] L. Shi, G. Guo, and X. Song, “Multi-agent based dynamic scheduling optimisation of the sustainable hybrid flow shop in a ubiquitous environment,” Int. J. Prod. Res., vol. 59, no. 2, pp. 576–597, Jan. 2021, doi: 10.1080/00207543.2019.1699671.

[3] D. Marsetiya Utama, “An Effective Hybrid Sine Cosine Algorithm to Minimize Carbon Emission on Flow-shop Scheduling Sequence Dependent Setup,” J. Tek. Ind., vol. 20, no. 1, pp. 62–72, Feb. 2019, doi: 10.22219/JTIUMM.Vol20.No1.62-72.

[4] H.-X. Qin et al., “An improved iterated greedy algorithm for the energy-efficient blocking hybrid flow shop scheduling problem,” Swarm Evol. Comput., vol. 69, p. 100992, Mar. 2022, doi: 10.1016/j.swevo.2021.100992.

[5] C. Lu, Q. Liu, B. Zhang, and L. Yin, “A Pareto-based hybrid iterated greedy algorithm for energy-efficient scheduling of distributed hybrid flowshop,” Expert Syst. Appl., vol. 204, p. 117555, Oct. 2022, doi: 10.1016/j.eswa.2022.117555.

[6] C. Lu, Y. Huang, L. Meng, L. Gao, B. Zhang, and J. Zhou, “A Pareto-based collaborative multi-objective optimization algorithm for energy-efficient scheduling of distributed permutation flow-shop with limited buffers,” Robot. Comput. Integr. Manuf., vol. 74, p. 102277, Apr. 2022, doi: 10.1016/j.rcim.2021.102277.

[7] D. M. Utama, “Minimizing Number of Tardy Jobs in Flow Shop Scheduling Using A Hybrid Whale Optimization Algorithm,” J. Phys. Conf. Ser., vol. 1845, no. 1, p. 012017, Mar. 2021, doi: 10.1088/1742-6596/1845/1/012017.

[8] D. S. Widodo and D. M. Utama, “The Hybrid Ant Lion Optimization Flow Shop Scheduling Problem for Minimizing Completion Time,” J. Phys. Conf. Ser., vol. 1569, no. 2, p. 022097, Jul. 2020, doi: 10.1088/1742-6596/1569/2/022097.

[9] D. M. Utama, D. S. Widodo, M. F. Ibrahim, and S. K. Dewi, “An effective hybrid ant lion algorithm to minimize mean tardiness on permutation flow shop scheduling problem,” Int. J. Adv. Intell. Informatics, vol. 6, no. 1, pp. 23–35, Mar. 2020, doi: 10.26555/ijain.v6i1.385.

[10] J. Chen, L. Wang, and Z. Peng, “A collaborative optimization algorithm for energy-efficient multi-objective distributed no-idle flow-shop scheduling,” Swarm Evol. Comput., vol. 50, p. 100557, Nov. 2019, doi: 10.1016/j.swevo.2019.100557.

[11] D. M. Utama, D. S. Widodo, M. F. Ibrahim, K. Hidayat, T. Baroto, and A. Yurifah, “The hybrid whale optimization algorithm: A new metaheuristic algorithm for energy-efficient on flow shop with dependent sequence setup,” J. Phys. Conf. Ser., vol. 1569, no. 2, p. 022094, Jul. 2020, doi: 10.1088/1742-6596/1569/2/022094.

[12] D. M. Utama and D. S. Widodo, “An energy-efficient flow shop scheduling using hybrid Harris hawks optimization,” Bull. Electr. Eng. Informatics, vol. 10, no. 3, pp. 1154–1163, Jun. 2021, doi: 10.11591/eei.v10i3.2958.

[13] D. M. Utama, D. S. Widodo, W. Wicaksono, and L. R. Ardiansyah, “A New Hybrid Metaheuristics Algorithm for Minimizing Energy Consumption in the Flow Shop Scheduling Problem,” Int. J. Technol., vol. 10, no. 2, pp. 320–331, Apr. 2019, doi: 10.14716/ijtech.v10i2.2194.

[14] Y. Li et al., “A discrete artificial bee colony algorithm for distributed hybrid flowshop scheduling problem with sequence-dependent setup times,” Int. J. Prod. Res., vol. 59, no. 13, pp. 3880–3899, Jul. 2021, doi: 10.1080/00207543.2020.1753897.

[15] Y. Zuo, Z. Fan, T. Zou, and P. Wang, “A Novel Multi-Population Artificial Bee Colony Algorithm for Energy-Efficient Hybrid Flow Shop Scheduling Problem,” Symmetry (Basel)., vol. 13, no. 12, pp. 1–22, Dec. 2021, doi: 10.3390/sym13122421.

[16] S. Schulz, “A Genetic Algorithm to Solve the Hybrid Flow Shop Scheduling Problem with Subcontracting Options and Energy Cost Consideration,” in Information Systems Architecture and Technology: Proceedings of 39th International Conference on Information Systems Architecture and Technology, 2019, pp. 263–273, doi: 10.1007/978-3-319-99993-7_23.

[17] B. Du, H. Chen, G. Q. Huang, and H. D. Yang, “Preference Vector Ant Colony System for Minimising Make-span and Energy Consumption in a Hybrid Flow Shop,” in Multi-objective Evolutionary Optimisation for Product Design and Manufacturing, London: Springer London, 2011, pp. 279–304. doi: 10.1007/978-0-85729-652-8_9

[18] X. Tao, J. Li, T. Huang, and P. Duan, “Discrete imperialist competitive algorithm for the resource-constrained hybrid flowshop problem with energy consumption,” Complex Intell. Syst., vol. 7, no. 1, pp. 311–326, Feb. 2021, doi: 10.1007/s40747-020-00193-w.

[19] Zeng Ling-Li, Zou Feng-Xing, Xu Xiao-hong, and Gao Zheng, “Dynamic scheduling of multi-task for hybrid flow-shop based on energy consumption,” in 2009 International Conference on Information and Automation, 2009, pp. 478–482, doi: 10.1109/ICINFA.2009.5204971.

[20] Z. Liu, J. Yan, Q. Cheng, C. Yang, S. Sun, and D. Xue, “The mixed production mode considering continuous and intermittent processing for an energy-efficient hybrid flow shop scheduling,” J. Clean. Prod., vol. 246, p. 119071, Feb. 2020, doi: 10.1016/j.jclepro.2019.119071.

[21] D. Lei and T. Wang, “Solving distributed two-stage hybrid flowshop scheduling using a shuffled frog-leaping algorithm with memeplex grouping,” Eng. Optim., vol. 52, no. 9, pp. 1461–1474, Sep. 2020, doi: 10.1080/0305215X.2019.1674295.

[22] L. Meng, C. Zhang, X. Shao, Y. Ren, and C. Ren, “Mathematical modelling and optimisation of energy-conscious hybrid flow shop scheduling problem with unrelated parallel machines,” Int. J. Prod. Res., vol. 57, no. 4, pp. 1119–1145, Feb. 2019, doi: 10.1080/00207543.2018.1501166.

[23] M. Li, D. Lei, and J. Cai, “Two-level imperialist competitive algorithm for energy-efficient hybrid flow shop scheduling problem with relative importance of objectives,” Swarm Evol. Comput., vol. 49, pp. 34–43, Sep. 2019, doi: 10.1016/j.swevo.2019.05.006.

[24] H.-X. Qin, Y.-Y. Han, Y.-P. Liu, J.-Q. Li, Q.-K. Pan, and Xue-Han, “A collaborative iterative greedy algorithm for the scheduling of distributed heterogeneous hybrid flow shop with blocking constraints,” Expert Syst. Appl., vol. 201, p. 117256, Sep. 2022, doi: 10.1016/j.eswa.2022.117256.

[25] J. Mou, P. Duan, L. Gao, X. Liu, and J. Li, “An effective hybrid collaborative algorithm for energy-efficient distributed permutation flow-shop inverse scheduling,” Futur. Gener. Comput. Syst., vol. 128, pp. 521–537, Mar. 2022, doi: 10.1016/j.future.2021.10.003.

[26] J. Dong and C. Ye, “Green scheduling of distributed two-stage reentrant hybrid flow shop considering distributed energy resources and energy storage system,” Comput. Ind. Eng., vol. 169, p. 108146, Jul. 2022, doi: 10.1016/j.cie.2022.108146.

[27] F. A. Hashim, K. Hussain, E. H. Houssein, M. S. Mabrouk, and W. Al-Atabany, “Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems,” Appl. Intell., vol. 51, no. 3, pp. 1531–1551, Mar. 2021, doi: 10.1007/s10489-020-01893-z.

[28] B. S. Yıldız, N. Pholdee, S. Bureerat, M. U. Erdaş, A. R. Yıldız, and S. M. Sait, “Comparision of the political optimization algorithm, the Archimedes optimization algorithm and the Levy flight algorithm for design optimization in industry,” Mater. Test., vol. 63, no. 4, pp. 356–359, Apr. 2021, doi: 10.1515/mt-2020-0053.

[29] L. Zhang, J. Wang, X. Niu, and Z. Liu, “Ensemble wind speed forecasting with multi-objective Archimedes optimization algorithm and sub-model selection,” Appl. Energy, vol. 301, p. 117449, Nov. 2021, doi: 10.1016/j.apenergy.2021.117449.

[30] E. H. Houssein, B. E. Helmy, H. Rezk, and A. M. Nassef, “An enhanced Archimedes optimization algorithm based on Local escaping operator and Orthogonal learning for PEM fuel cell parameter identification,” Eng. Appl. Artif. Intell., vol. 103, p. 104309, Aug. 2021, doi: 10.1016/j.engappai.2021.104309.

[31] G. Liang, F. Panahi, A. N. Ahmed, M. Ehteram, S. S. Band, and A. Elshafie, “Predicting municipal solid waste using a coupled artificial neural network with archimedes optimisation algorithm and socioeconomic components,” J. Clean. Prod., vol. 315, p. 128039, Sep. 2021, doi: 10.1016/j.jclepro.2021.128039.

[32] X. Sun, G. Wang, L. Xu, H. Yuan, and N. Yousefi, “Optimal estimation of the PEM fuel cells applying deep belief network optimized by improved archimedes optimization algorithm,” Energy, vol. 237, p. 121532, Dec. 2021, doi: 10.1016/j.energy.2021.121532.




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