Analisis Performansi Algoritma Grey Wolf Optimizer dan Algoritma Genetika untuk Model Persediaan Multi Supplier Multi Buyer dengan Pertimbangan Biaya Transportasi
DOI:
https://doi.org/10.30587/matrik.v23i2.5282Keywords:
Optimasi, persediaan, algoritma metaheuristik, vendor managed inventory, algoritma gray wolf optimizer, algoritma genetikaAbstract
Pengendalian persediaan terintegrasi perlu dilakukan banyak pertimbangan terkait batasan yang ada untuk menemukan nilai optimal. Hal ini merupakan hal yang tidak mudah bila dilakukan perhitungan secara manual. Dengan adanya algoritma metaheuristik sebagai alat optimasi, dapat membantu untuk menemukan keputusan optimal. Model persediaan yang digunakan adalah Multi-Supplier Multi-Buyer (MSMB), dimana fungsi objektifnya adalah untuk memaksimasi Joint Total Profit (JTP). Biaya transportasi untuk pengiriman bahan baku dari supplier dan produk jadi ke buyer dipertimbangkan secara eksplisit ke dalam model. Penelitian ini mengusulkan Algoritma Grey Wolf Optimizer (GWO) dan Algoritma Genetika (GA) yang diklaim pada penelitian lain terkait optimasi persediaan dapat memberikan performa yang baik. Analisis dalam penelitian ini menggunakan studi kasus pada perusahaan skala mikro yang disertai analisis sensitivitas dan perbandingan hasil optimasi antar algoritma. Hasil penelitian ini menunjukkan bahwa biaya transportasi memiliki pengaruh besar terhadap Joint Total Profit (JTP), dan GWO memiliki performansi yang lebih baik dibandingkan GA dalam mengoptimasi model persediaan MSMB.
References
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[3] A. Gharaei, M. Karimi, and S. A. Hoseini Shekarabi, "Joint Economic Lot-sizing in Multi-product Multi-level Integrated Supply Chains: Generalized Benders Decomposition," International Journal of Systems Science: Operations & Logistics, vol. 7, no. 4, pp. 309-325, 2020/10/01 2020, doi: https://doi.org/10.1080/23302674.2019.1585595.
[4] S. Ghourchiany and M. Khakzar, "A multi-product inventory management model in a three-level supply chain with multiple members at each level," Uncertain Supply Chain Management, vol. 7, pp. 109-120, 01/01 2019, doi: http://doi.org/10.5267/j.uscm.2018.4.001.
[5] G. Fauza, H. Prasetyo, N. K. Dania, and B. S. Amanto, "Development of food inventory model in an integrated vendor-buyer system: Case study in food industry," in AIP Conference Proceedings, 2018, vol. 1977, no. 1: AIP Publishing LLC, p. 020053, doi: https://doi.org/10.1063/1.5042909.
[6] T. Sekar and R. Uthayakumar, "A production inventory model for single vendor single buyer integrated demand with multiple production setups and rework," Uncertain Supply Chain Management, vol. 6, no. 1, pp. 75-90, 2018, doi: http://dx.doi.org/10.5267/j.uscm.2017.6.001.
[7] I. D. Wangsa, S. Tiwari, H. M. Wee, and S. Reong, "A sustainable vendor-buyer inventory system considering transportation, loading and unloading activities," Journal of Cleaner Production, vol. 271, p. 122120, 2020/10/20/ 2020, doi: https://doi.org/10.1016/j.jclepro.2020.122120.
[8] M. A. Kaasgari, D. M. Imani, and M. Mahmoodjanloo, "Optimizing a vendor managed inventory (VMI) supply chain for perishable products by considering discount: Two calibrated meta-heuristic algorithms," Comput. Ind. Eng., vol. 103, no. C, pp. 227–241, 2017, doi: https://doi.org/10.1016/j.cie.2016.11.013.
[9] D. M. Utama, D. S. Widodo, M. F. Ibrahim, K. Hidayat, and S. K. Dewi, "The Sustainable Economic Order Quantity Model: A Model Consider Transportation, Warehouse, Emission Carbon Costs, and Capacity Limits," Journal of Physics: Conference Series, vol. 1569, p. 022095, 2020/07 2020, doi: http://doi.org/10.1088/1742-6596/1569/2/022095.
[10] S. Khalilpourazari, S. H. R. Pasandideh, and S. T. A. Niaki, "Optimizing a multi-item economic order quantity problem with imperfect items, inspection errors, and backorders," Soft Computing, vol. 23, no. 22, pp. 11671-11698, 2019/11/01 2019, doi: http://doi.org/10.1007/s00500-018-03718-1.
[11] S. Khalilpourazari and S. H. R. Pasandideh, "Multi-objective optimization of multi-item EOQ model with partial backordering and defective batches and stochastic constraints using MOWCA and MOGWO," Operational Research, vol. 20, no. 3, pp. 1729-1761, 2020/09/01 2018, doi: http://doi.org/10.1007/s12351-018-0397-y.
[12] A. H. Nobil, L. E. Cárdenas–Barrón, and E. Nobil, "Optimal and simple algorithms to solve integrated procurement-production-inventory problem without/with shortage," RAIRO-Operations Research, vol. 52, no. 3, pp. 755-778, 2018, doi: https://doi.org/10.1051/ro/2017067.
[13] N. Manavizadeh, M. Shaabani, and s. aghamohamadi, "Designing a green location routing inventory problem considering transportation risks and time window: a case study," (in en), Journal of Industrial and Systems Engineering, vol. 12, no. 4, pp. 27-56, 2019. [Online]. Available: http://www.jise.ir/article_96020.html.
[14] H. Tarhini, M. Karam, and M. Y. Jaber, "An integrated single-vendor multi-buyer production inventory model with transshipments between buyers," International Journal of Production Economics, vol. 225, p. 107568, 2020, doi: https://doi.org/10.1016/j.ijpe.2019.107568.
[15] L. R. Rodrigues and J. P. P. Gomes, "A Bi-Objective Grey Wolf Optimizer With A Dynamic Number of Leaders Applied to Spare Parts Stock Level Optimization Problems," vol. 53, p. 135221, 2021.
[16] A. Alejo-Reyes, E. Cuevas, A. Rodríguez, A. Mendoza, and E. Olivares-Benitez, "An improved grey wolf optimizer for a supplier selection and order quantity allocation problem," Mathematics, vol. 8, no. 9, p. 1457, 2020, doi: https://doi.org/10.3390/math8091457.
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Published
2023-03-24
How to Cite
maulana, S. kurnia dwi budi, Widodo, E., & Hisyam, I. (2023). Analisis Performansi Algoritma Grey Wolf Optimizer dan Algoritma Genetika untuk Model Persediaan Multi Supplier Multi Buyer dengan Pertimbangan Biaya Transportasi. Matrik : Jurnal Manajemen Dan Teknik Industri Produksi, 23(2), 141–150. https://doi.org/10.30587/matrik.v23i2.5282
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