Optimization Of Sandal Production Using Linear Programming
DOI:
https://doi.org/10.59934/jaiea.v3i3.470Keywords:
Linear, Optimization, Production, Sandal, SimplexAbstract
In order to maximize income and minimize material costs, sandal manufacture involves other operational expenditures in addition to raw material costs that must be calculated. The goal of this study is to maximize earnings by optimizing sandal production costs, with a focus on the Diona Shoes home sector. By identifying the restrictions and inequalities present in the linear program, you can utilize linear programming to solve production cost optimization challenges. The simplex method is a technique for solving linear programming problems that involve numerous inequalities and variables by doing iterative calculations until the most optimal solution is found. The simplex approach (iteration) of production result optimization, data collection and observation, mathematical model creation, production result optimization employing Lindo software tools that are anticipated to yield results, and production result optimization are the stages taken to optimize overall production costs. optimally with the lowest possible manufacturing costs for sandals with heel, pansus, and back straps and can optimize the earnings from all sandal goods
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A. Karmakar, S.S. Roy, F. Vercauteren, and I. Verbauwhede, “Efficient finite field multiplication for isogeny based post quantum cryptography,” 2017, doi: 10.1007/978-3-319-55227-9_14.
AMH Pardede, M. Zarlis, and H. Mawengkang, "Optimization of Health Care Services with Limited Resources,"Int. J. Adv. Sci. Eng. Inf. Technol., vol. 9, no. 4, pp. 1444–1449, 2019, doi: 10.18517/ijaseit.9.4.8348.
AMH Pardede, Y. Maulita, and R. Buaton, "Application modeling ipv6 (internet protocol version 6) on e-id card for identification number for effectiveness and efficiency of registration process identification of population," inJournal of Physics: Conference Series, 2018, vol. 978, no. 1, doi: 10.1088/1742-6596/978/1/012017.
SP Mohanty, U. Choppali, and E. Kougianos, “Everything you want to know about smart cities,”IEEE Consum. Electrons. Mag., vol. 5, no. 3, pp. 60–70, 2016, doi: 10.1109/MCE.2016.2556879.
D. S. Sinaga, A. P. Windarto, R. A. Nasution, and I. S. Damanik, “PREDICTION OF PRODUCT SALES RESULTS USING ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS)”, j. of artif. intell. and eng. appl., vol. 1, no. 2, pp. 92–101, Feb. 2022.
N. S. . Pasaribu, J. T. . Hardinata, and H. . Qurniawan, “Application of The Fuzzy Tsukamoto Method in Determining Household Industry Products”, j. of artif. intell. and eng. appl., vol. 1, no. 1, pp. 71–75, Oct. 2021.
WA Jabbar, WK Saad, and M. Ismail, "MEQSA-OLSRv2: A multicriteria-based hybrid multipath protocol for energy-efficient and QoS-aware data routing in MANET-WSN convergence scenarios of IoT,"IEEE Access, 2018, doi: 10.1109/ACCESS.2018.2882853.
D. Niyigena, C. Habineza, and T.S. Ustun, “Computer-based smart energy management system for rural health centers,” 2016, doi: 10.1109/IRSEC.2015.7455005.
F.-Z. Younsi, A. Bounnekar, D. Hamdadou, and O. Boussaid, “SEIR-SW, Simulation Model of Influenza Spread Based on the Small World Network,”Tsinghua Sci. Technol., vol. 20, no. 5, pp. 460–473, 2015.
R. Buaton, Z. Muhammad, Elviwani, and A. Dilham, “Optimization of Higher Education Internal Quality Audits Based on Artificial Intelligence”, j. of artif. intell. and eng. appl., vol. 1, no. 2, pp. 158–161, Feb. 2022.
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