DOI: https://doi.org/10.14710/jati.19.2.58-69

OPTIMASI PERENCANAAN PRODUKSI AGREGAT PRODUK TUNGGAL DENGAN MEMPERTIMBANGKAN KAPASITAS PRODUKSI

*Denny Suci Prastiya  -  Department of Industrial Engineering, Universitas Gunadarma, Jl. Margonda Raya No. 100, Depok, Jawa Barat, Indonesia 16424, Indonesia
Rossi Septy Wahyuni scopus  -  Department of Industrial Engineering, Universitas Gunadarma, Jl. Margonda Raya No. 100, Depok, Jawa Barat, Indonesia 16424, Indonesia

Citation Format:
Abstract

Pertumbuhan pada industri air minum dalam kemasan mengalami peningkatan secara signifikan. Peningkatan pada industri air minum dalam kemasan dipengaruhi oleh tingginya permintaan akan produk air minum bersih. PT X pada studi kasus ini mengalami fluktuasi permintaan yang diakibatkan tingginya persaingan antara industri sejenis. Oleh karena itu PT X pada studi kasus ini menjalin kerja sama subkontrak dalam memenuhi permintaan. Permasalahan yang dialami oleh PT X adalah meningkatnya harga per unit saat melakukan subkontrak. Tingginya harga unit subkontrak dapat mengakibatkan tingginya biaya perencanaan produksi. Penelitian ini dilakukan untuk meminimasi jumlah unit subkontrak dengan mengoptimalkan produksi internal. Model optimasi dibangun dengan fungsi tujuan linear dan fungsi pembatas non linear sebagai usulan optimasi perencanaan produksi agregat. Di samping itu, berkurangnya jumlah unit subkontrak dapat mengakibatkan tingginya produksi internal perusahaan, sehingga model usulan perlu mempertimbangkan kapasitas produksi. Usulan evaluasi kapasitas dilakukan menggunakan pendekatan rough-cut capacity planning. Berdasarkan hasil optimasi model usulan dapat menghemat biaya perencanaan produksi agregat sebesar 5% dibandingkan dengan kondisi nyata.

 

Abstract

[Optimization of Single Product Aggregrate Production Planning Considering Production Capacity] Bottled drinking water industry growth has increased significantly. The increase in the bottled drinking water industry is influenced by the high demand for clean drinking water products. PT X in this case study experienced fluctuations in demand due to high competition between similar industries. Therefore, PT X established sub-contract cooperation to fulfill demand in this case study. The problem experienced by PT X was when carrying out sub-contract cooperation. The high price per unit of sub-contracted products can result in high production planning cost. The research is conducted to minimize the number of sub-contract units while optimizing internal production. The optimization model was built with a linear objective function and a nonlinear constraint function as an optimization proposal for aggregrate production planning. Besides that, the reduced number of sub-contract units can result in high internal production for the company, therefore it is necessary to consider production capacity. The proposed capacity evaluation is conducted using a rough-cut capacity planning approach. Based on the results of the optimization of the proposed model, it can save 5% of aggregrate production planning costs compared to actual conditions.

Keywords: integer nonlinear programming; production capacity; aggregrate production planning

Fulltext View|Download
Keywords: integer nonlinear programming; kapasitas produksi; perencanaan produksi agregat

Article Metrics:

Article Info
Section: Research Articles
Language : ID
Recent articles
PERANCANGAN ALAT BANTU PEMANENAN ERGONOMIS UNTUK MENGURANGI RISIKO MSDS BAGI PETANI SAYUR DI KABUPATEN BANDUNG BARAT DESIGNING AN E-KANBAN SYSTEM TO IMPROVE DRUG AVAILABILITY AND MINIMISE INVENTORY WASTE AT THE HOSPITAL PHARMACY INSTALLATION OPTIMASI PERENCANAAN PRODUKSI AGREGAT PRODUK TUNGGAL DENGAN MEMPERTIMBANGKAN KAPASITAS PRODUKSI More recent articles
Most cited articles
PEMILIHAN PARAMETER PRE TREATMENT PADA PROSES PENGAWETAN BAMBU LEMINASI ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI KEPUTUSAN PEMBELIAN VIA INTERNET PADA TOKO ONLINE PENGEMBANGAN SISTEM INFORMASI BERBASIS KOMPUTER UNTUK EFISIENSI DAN EFEKTIVITAS PAKAN PADA USAHA PENGGEMUKAN SAPI POTONG KORELASI ANTARA EMPLOYEES INVOLVEMENT, TURNOVER SERTA PENGARUHNYA TERHADAP PRODUKTIVITAS PENINGKATAN PENGENDALIAN KUALITAS MELALUI METODE LEAN SIX SIGMA More cited articles
  1. Attia, E. A., Megahed, A., AlArjani, A., Elbetar, A., & Duquenne, P. (2022). Agregrate production planning considering organizational learning with case based analysis. Ain Shams Engineering Journal, 13(2), 101575. https://doi.org/10.1016/j.asej.2021.09.002
  2. Baydoun, G., Haït, A., Pellerin, R., Clément, B., & Bouvignies, G. (2016). A rough-cut capacity planning model with overlapping. OR Spectrum, 38(2), 335–364. https://doi.org/10.1007/s00291-016-0436-0
  3. Bhosale, K. C., & Pawar, P. J. (2020). Production planning and scheduling problem of continuous parallel lines with demand uncertainty and different production capacities. Journal of Computational Design and Engineering, 7(6), 761–774. https://doi.org/10.1093/jcde/qwaa055
  4. Chopra, Sunil & Peter Meindl. (2019). Supply Chain Management Strategy, Planning and Operation. (pp 238-244) 7th Edition. New Jersey: Prentice Hall. Inc
  5. Dal Borgo, E., & Meneghetti, A. (2019). Production and shipment planning for Project Based Enterprises: Exploiting learning-forgetting phenomena for sustainable assembly of Curtain Walls. Computers and Industrial Engineering, 131(January), 488–501. https://doi.org/10.1016/j.cie.2019.01.058
  6. Elekidis, A. P., Corominas, F., & Georgiadis, M. C. (2019). Production Scheduling of Consumer Goods Industries. Industrial and Engineering Chemistry Research, 58(51), 23261–23275. https://doi.org/10.1021/acs.iecr.9b04907
  7. Firmansyah, F., Mawengkang, H., Mujib, A., & Mathelinea, D. (2022). A Decision Model to Plan Optimally Production-Distribution of Seafood Product with Multiple Locations. Mathematics, 10(18), 1–13. https://doi.org/10.3390/math10183240
  8. García-Cáceres, R. G., Castañeda-Galvis, M. T., & Suárez-Fajardo, J. F. (2019). Towards an efficient and sustainable planning of the drinking water supply chain. Journal of Cleaner Production, 230, 394–401. https://doi.org/10.1016/j.jclepro.2019.05.062
  9. Gómez-Rocha, J. E., & Hernández-Gress, E. S. (2022). A Stochastic Programming Model for Multi-Product Agregrate Production Planning Using Valid Inequalities. Applied Sciences (Switzerland), 12(19). https://doi.org/10.3390/app12199903
  10. Jacobs, F. R., W. L. Berry, D. C. Whybark, & T. E. Vollmann. (2018). Manufacturing Planning and Control for Supply Chain Management. (pp 242- 265) 2nd. New York: Mc. GrawHill
  11. J.K. Sharma. (2016). Operations Research Theory and Applications (pp 806-847). Sixth Edition. New Delhi: Trinity Press
  12. Hahn, G. J., & Brandenburg, M. (2018). A sustainable agregrate production planning model for the chemical process industry. Computers and Operations Research, 94, 154–168. https://doi.org/10.1016/j.cor.2017.12.011
  13. Haoues, M., Dahane, M., & Mouss, N. K. (2019). Optimization of single outsourcer–single subcontractor outsourcing relationship under reliability and maintenance constraints. Journal of Industrial Engineering International, 15(3), 395–409. https://doi.org/10.1007/s40092-019-0307-9
  14. Krajčovič, M., Furmannová, B., Grznár, P., Furmann, R., Plinta, D., Svitek, R., & Antoniuk, I. (2021). System of parametric modeling and assessing the production staff utilisation as a basis for agregrate production planning. Applied Sciences (Switzerland), 11(19). https://doi.org/10.3390/app11199347
  15. Oluyisola, O. E., Sgarbossa, F., & Strandhagen, J. O. (2020). Smart production planning and control: Concept, use-cases and sustainability implications. Sustainability (Switzerland), 12(9). https://doi.org/10.3390/su12093791
  16. Omatseye, O., & Urbanic, R. J. (2022). System reconfiguration for reverse logistics: A case study. IFAC-PapersOnLine, 55(10), 115–120. https://doi.org/10.1016/j.ifacol.2022.09.377
  17. Rivera-Gómez, H., Medina-Marin, J., Santana-Robles, F., Montaño-Arango, O., Barragán-Vite, I., & Cisneros-Flores, G. (2022). Impact of Unreliable Subcontracting on Production and Maintenance Planning Considering Quality Decline. Applied Sciences (Switzerland), 12(7). https://doi.org/10.3390/app12073379
  18. Sabah, B., Nikolay, T., Sylverin, K. T., & Institute of Electrical and Electronics Engineers. (2019). IESM 2019 : proceedings of the 2019 International Conference on Industrial Engineering and Systems Management : 25-27 September 2019, Shanghai, China. Production Planning under Demand Uncertainty Using Monte Carlo Simulation Approach: A Case Study in Fertilizer Industry, 1–6
  19. Shi, Z., Liu, P., Gao, H., & Shi, L. (2015). Production planning for a class of batch processing problem. IEEE International Conference on Automation Science and Engineering, 2015-Octob, 1188–1193. https://doi.org/10.1109/CoASE.2015.7294259
  20. Sinha, A. K., Davich, T., & Krishnamurthy, A. (2016). Optimisation of production and subcontracting strategies. International Journal of Production Research, 54(8), 2377–2393. https://doi.org/10.1080/00207543.2015.1077285
  21. Xu, W., & Song, D. P. (2022). Integrated optimisation for production capacity, raw material ordering and production planning under time and quantity uncertainties based on two case studies. Operational Research, 22(3), 2343–2371. https://doi.org/10.1007/s12351-020-00609-y
  22. Yu, V. F., Kao, H. C., Chiang, F. Y., & Lin, S. W. (2022). Solving Agregrate Production Planning Problems: An Extended TOPSIS Approach. Applied Sciences (Switzerland), 12(14). https://doi.org/10.3390/app12146945

Last update:

No citation recorded.

Last update: 2024-11-11 14:20:59

No citation recorded.