Optimization of National Rice Production with Fuzzy Logic using Mamdani Method

Authors

DOI:

https://doi.org/10.47352/jmans.v1i1.3

Keywords:

fuzzy inference, mamdani method, rice production

Abstract

The purpose of this article is to optimization of national rice production with fuzzy logic using Mamdani method. Based on the results of the study, it is known that four parameters need to be considered to maintain the price stability of necessities, namely production; availability; demand and distribution. Optimization of production by producers and optimization of the ordering of goods by distributors are important steps to maintain price stability for necessities. Optimization of production and ordering of staple goods will have a significant impact on the financial sector because it is closely related to the prediction of the number of raw materials used, production costs, storage costs, and also distribution costs of goods. One of the fuzzy inference methods that can be used for this optimization is the Mamdani method. To get the output on the application of the fuzzy logic of the Mamdani method, four stages are needed, formation of fuzzy sets; application of implication functions; composition of rules and defuzzification. Fuzzy logic Mamdani method can be used to predict the amount of national rice that must be produced. If it is known that the need is 21,908,784 tons of rice and the supply is 65,457,456 tons,  the amount of national rice that must be produced is 14,624,592 tons.

References

[1] V. Erokhin. (2017). “Factors influencing food markets in developing countries: An approach to assess sustainability of the food supply in Russia”. Sustainability (Switzerland). 9 (8). 10.3390/su9081313.

[2] J. Premanandh. (2011). “Factors affecting food security and contribution of modern technologies in food sustainability”. Journal of the Science of Food and Agriculture. 91 (15): 2707–2714. 10.1002/jsfa.4666.

[3] E. R. Adiratma. (2004). “Stop Tanam Padi ? : Memikirkan Kondisi Petani Padi Indonesia dan Upaya Meningkatkan Kesejahteraannya”. Penebar Swadaya, Jakarta.

[4] Badan Pusat Statistik. (2019). “Distribusi Perdagangan Komoditas Beras Indonesia Tahun 2019”. BPS, Jakarta.

[5] D. Kumar and H. Ramakrishna. (2012). “Assessment of Supply Chain Agility Using Fuzzy Logic for a Manufacturing Organization”. The IUP Journal of Supply Chain Management. 4: 7–15.

[6] S. S. Jamsandekar and R. R. Mudholkar. (2013). “Performance Evaluation by Fuzzy Inference Technique”. International Journal of Soft Computing and Engineering (IJSCE). 3: 2231–2307.

[7] Wang Li Xin. (2009). “A Course in Fuzzy Systems and Control”. Pretince–Hall International, USA.

[8] L. A. Zadeh. (1973). “Outline of a New Approach to the Analysis of Complex Systems and Decision Processes”. IEEE Transactions on Systems, Man and Cybernetics. 3 (1): 28–44. 10.1109/TSMC.1973.5408575.

[9] M. Mehrdad and A. NA. (2011). “Supplier performance Evaluation Based on Fuzzy Logic”. International Journal of Applied Science and Technology. 1 (5): 257–265.

[10] S. Kusumadewi and H. Purnomo. (2010). “Aplikasi Logika Fuzzy untuk Pendukung Keputusan”. Graha Ilmu, Yogyakarta.

[11] A. M. Sánchez and M. P. Pérez. (2005). “Supply chain flexibility and firm performance: A conceptual model and empirical study in the automotive industry”. International Journal of Operations and Production Management. 25 (7): 681–700. 10.1108/01443570510605090.

[12] U. O. Ezutah and Y. W. Kuan. (2009). “Supply Chain Performance Evaluation: Trends and Challenges”. American Journal of Engineering and Applied Sciences. 2 (1): 202–211. 10.3844/ajeassp.2009.202.211.

[13] S. S. L. Chang and L. A. Zadeh. (1996). “On Fuzzy Mapping and Control”. IEEE transactions on systems, man, and cybernetics. 2: 180–184. 10.1142/9789814261302_0012.

[14] E. H. Mamdani and S. Assilian. (1975). “An experiment in linguistic synthesis with a fuzzy logic controller”. International Journal of Man-Machine Studies. 7 (1): 1–13. 10.1016/S0020-7373(75)80002-2.

[15] E. H. Mamdani. (1976). “Advances in the linguistic synthesis of fuzzy controllers”. International Journal of Man-Machine Studies. 8 (6): 669–678. 10.1016/S0020-7373(76)80028-4.

[16] M. A. Baten and A. A. Kamil. (2010). “Optimal fuzzy control with application to discounted cost production inventory planning problem”. WCE 2010 - World Congress on Engineering 2010. 3: 1898–1902.

[17] K. A. Halim, B. C. Giri, and K. S. Chaudhuri. (2008). “Fuzzy economic order quantity model for perishable items with stochastic demand, partial backlogging and fuzzy deterioration rate”. International Journal of Operational Research. 3 (1): 77–96. 10.1504/IJOR.2008.016155.

[18] A. Nagoor Gani and S. Maheswari. (2010). “Supply chain model for the retailer’s ordering policy under two levels of delay payments in fuzzy environment”. Applied Mathematical Sciences. 4 (21): 1155–1164.

[19] H. M. Lee and J. S. Yao. (1998). “Economic production quantity for fuzzy demand quantity and fuzzy production quantity”. European Journal of Operational Research. 109 (1): 203–211. 10.1016/S0377-2217(97)00200-2.

[20] C. Y. Lo, J. H. Leu, and C. I. Hou. (2007). “A study of the EPQ model using Fuzzy AHP when flaw of the products or unreliable machineries exists”. IEEM 2007: 2007 IEEE International Conference on Industrial Engineering and Engineering Management. 7 (1): 163–1170. 10.1109/IEEM.2007.4419375.

[21] S. H. Chen and S. M. Chang. (2008). “Optimization of fuzzy production inventory model with unrepairable defective products”. International Journal of Production Economics. 113 (2): 887–894. 10.1016/j.ijpe.2007.11.004.

[22] S. C. Chang. (1999). “Fuzzy production inventory for fuzzy product quantity with triangular fuzzy number”. Fuzzy Sets and Systems. 107 (1): 37–57. 10.1016/S0165-0114(97)00350-3.

[23] D. C. Lin and J. S. Yao. (2000). “Fuzzy economic production for production inventory”. Fuzzy Sets and Systems. 111 (3): 465–495. 10.1016/S0165-0114(98)00037-2.

[24] S. H. Chen, C. C. Wang, and S. M. Chang. (2007). “Fuzzy economic production quantity model for items with imperfect quality”. International Journal of Innovative Computing, Information and Control. 3 (1): 85–95.

[25] I. Iancu. (2012). “A Mamdani Type Fuzzy Logic Controller, Fuzzy Logic - Controls, Concepts, Theories and Applications”. InTech, London.

[26] R. Prasetyo, R. R. Amalia, R. Gunawan, M. Hartini, N. G. P. A. S. Lestari, R. Poerwaningsih, and K. Astuti. (2018). “Luas Panen dan Produksi Beras di Indonesia 2018 (Hasil Kegiatan Pendataan Statistik Pertanian Tanaman Pangan Terintegrasi dengan Metode Kerangka Sampel Area)”. Badan Pusat Statistik, Jakarta.

Downloads

Published

2021-01-10

How to Cite

[1]
W. Wawan, M. Zuniati, and A. Setiawan, “Optimization of National Rice Production with Fuzzy Logic using Mamdani Method”, J. Multidiscip. Appl. Nat. Sci., vol. 1, no. 1, pp. 36-43, Jan. 2021.