Academic literature on the topic 'Production Optimal Fuzzy Logic Fuzzy Tsukamoto'

Determining the amount of production in an industry is a very important thing before starting the production process. Therefore we need a mathematical model that can help industry players in performing production planning. Mathematical model is a formula that helps industry players in PT. XYZ in solving semi-structured problems in the form of production planning. Many techniques are used to create a mathematical model, one of them with Fuzzy Logic. Fuzzy logic is one of the problem solving techniques where membership degrees are usually represented by values between 0 and 1, so they can be more balanced. One of the fuzzy methods that can be used in solving the problem is the Fuzzy Tsukamoto Method which applies weighted average to calculate the amount of production at PT. XYZ as the end result. The mathematical model of determining the amount of production using the Fuzzy Tsukamoto Method is able to produce a more optimal and balanced quantity of production compared to the calculation without using the method.

PT. Samihasa Kita is a glass product distributor company that was founded in 1989, which is located in the city of Semarang, Central Java, with sales area coverage in Central Java, D.I Yogyakarta, Jakarta, and Kalimantan. So far, PT Samihasa Kita has an erratic amount of demand for goods, as a result, sometimes the number of goods produced for sale with the goods purchased by consumers is not balanced. This resulted in PT Samihasa Kita not getting the maximum profit. Based on this problem, Fuzzy Tsukamoto logic was chosen to determine the optimal daily production amount. By using fuzzy logic can be determined the relative size of the production of goods. The Fuzzy Tsukamoto method can also be used to forecast sales in the coming month based on the amount of inventory. Thus the amount of production and demand for goods PT. Samihasa Kita is predictable and balances transactions. E-transaction Point Of Sales (POS) with the Fuzzy Tsukamoto algorithm at PT. Samihasa aims to be able to predict demand and procurement of goods that will have an impact on optimizing revenue.

PT. Samihasa Kita is a glass product distributor company that was founded in 1989, which is located in the city of Semarang, Central Java, with sales area coverage in Central Java, D.I Yogyakarta, Jakarta, and Kalimantan. So far, PT Samihasa Kita has an erratic amount of demand for goods, as a result, sometimes the number of goods produced for sale with the goods purchased by consumers is not balanced. This resulted in PT Samihasa Kita not getting the maximum profit. Based on this problem, Fuzzy Tsukamoto logic was chosen to determine the optimal daily production amount. By using fuzzy logic can be determined the relative size of the production of goods. The Fuzzy Tsukamoto method can also be used to forecast sales in the coming month based on the amount of inventory. Thus the amount of production and demand for goods PT. Samihasa Kita is predictable and balances transactions. This research aims to be able to predict demand and procurement of goods that will have an impact on optimizing revenue at PT. Samihasa Kita.

Tile is a product that is in great demand by many people. This has become a trigger for producers to improve their management. The company's tile production management is still experiencing problems, namely frequent miscalculations in determining the agreement that must be issued in making tile production from customer requests. One of the efforts made is to predict the production that can be done to get the optimal amount obtained, to get a big profit. In this study, to obtain a prediction of the amount of tile production, computerized calculations were carried out using the Tsukamoto fuzzy logic method. This method uses the concept of rules from the C 4.5 decision tree in the building to make it easier to determine the rules that are built without having to consult an expert because C 4.5 will study existing datasets to serve as a reference in forming these rules according to conditions that often occur. The modeling results produce relevant rules after being compared with the actual results. The results of the comparison of predictions with actual production have an error percentage of 29.34%, with a truth of 70.66% (based on the calculation of the Average Forecasting Error Rate (AFER)). Therefore when implemented in the Tsukamoto Fuzzy Inference System it can produce predictions of tile production that are quite optimum. It is said to be quite optimum because all customer requests are met, either generated by the production prediction itself or the prediction results are added up with inventory data, and all predictions are close to actual production.

This study explores the position of catfish as a major aquaculture commodity in Indonesia. In 2013, Indonesia dominated global catfish production with a market share of 75.6%. Despite the higher growth rate of catfish farming compared to other commodities, production did not meet the set targets. The government is working to strengthen national catfish production and enhance its role as a leading commodity in Indonesia. The advantages of catfish lie in its content of leucine and lysine, which are important for children's growth and nitrogen balance. Environmental factors such as freshwater pH (6.5-8.6), optimal temperature (23-30°C), oxygen levels (2-5 mg/L), and ammonia levels (5-7 mg/L) affect the water quality in catfish farming ponds. Natural and human factors such as extreme weather and overfeeding can impact the living conditions and growth of catfish. This study aims to investigate the relationship between pond water quality and catfish growth. The methods used include confusion matrix and Tsukamoto fuzzy logic to determine the accuracy of the water quality assessment system. The main objective of this research is to provide solutions and benefits for the community or the field of study.

The furniture industry is experiencing the problem of uncertainty in determining the optimal amount of wood raw material inventory. Fuzzy logic is one of the methods used to analyze systems that contain uncertainty. This study discusses the application of fuzzy logic in solving the problem of supplying wood raw materials in the furniture industry with the Fuzzy-Mamdani approach. The data used in this study are data on the entry, distribution, and supply of wood raw materials from January to December 2018. The design of the system to obtain output is carried out in the following stages: (a) Formation of fuzzy sets, (b) Application of functions implications, (c) Composition of rules, (d) Affirmation (defuzzification). Solving the problem using the Fuzzy Tsukamoto method is carried out by the Tsukamoto method with the help of Visual Basic software so that the desired results will be obtained on the output variable. From the results of calculations using the Fuzzy Tsukamoto method, it can be analyzed that the comparison between the realized wood raw material inventory with the Fuzzy Tsukamoto approach looks different and the results from the Fuzzy Tsukamoto approach are more optimal.

Rumah produksi berkah bolu merupakan salah satu UMKM yang memproduksi kue bolu di Kota Pekanbaru. Pemilik usaha tersebut kesulitan dalam menentukan jumlah produksi kue karena hanya berdasarkan pada jumlah permintaan yang ada. Berdasarkan permasalahan tersebut, maka dipilihlah metode fuzzy Tsukamoto karena menggunakan penalaran monoton dalam setiap aturannya. Terdapat 4 tahapan yang dipakai dalam perhitungan metode Tsukamoto yaitu fuzzifikasi, inferensi, komposisi/Agregasi dan defuzzyfikasi. Hasil MAPE yang diperoleh dengan metode fuzzy Tsakomoto adalah 6,91% dan tingkat keakuratan sebesar 93,01%, yang memiliki arti bahwa metode fuzzy Tsukamoto sangat baik dalam memprediksi jumlah produksi, sehingga dapat digunakan sebagai sistem untuk mendukung keputusan dalam penentuan jumlah produksi kue bolu di rumah produksi Berkah Bolu. The Berkah Bolu Production House is one of the Small and Medium Enterprise (SME) that produces bolu cakes in Pekanbaru City. The business owner has difficulty in determining the amount of cake production because it is only based on the number of existing requests. Based on these problems, the Tsukamoto fuzzy method was chosen because it uses monotonous reasoning in each rule. There are 4 stages used in the calculation of the Tsukamoto method, namely fuzzification, inference, composition or aggregation, and defuzzification. The MAPE result obtained by the fuzzy Tsukomoto method is 6.91% and the accuracy level is 93.01%, which means that the fuzzy Tsukamoto method is very good at predicting the amount of production, so it can be used as a decision support system in determining the amount of bolu cake production in the Berkah Bolu Production House.

Strategic efforts to maintain rice production, which is the staple food of the Indonesian people, must continue to be carried out, one of which is maintaining production in rice-producing areas such as Indramayu Regency is a step that must be prioritized. Knowing the prediction of the upcoming rice harvest is the right effort to make. Fuzzy Tsukamoto logic is a method that can be used as a calculation to make predictions, the selection of fuzzy Tsukamoto is due to its ability to predict with factors that have uncertainty such as rainfall, in addition, land area and harvest yields can also be used as variables to obtain predictions through fuzzy Tsukamoto. This study was conducted to implement fuzzy Tsukamoto for predicting rice harvest yields, where the results of the fuzzy Tsukamoto logic process that have been carried out produce six rules at the inference stage in the category of increasing or decreasing harvests, which are used as the basis for the defuzzification process to find the output value of the harvest. The calculation results are then applied in a website-based system using the Laravel framework and MySql DBMS so that they can be used to predict rice harvest yields. Based on the prediction process carried out through a system based on the fuzzy Tsukamoto method, out of 31 sub-districts, 16 sub-districts have the potential to experience a decline, and 15 sub-districts have experienced an increase in rice harvest production in Indramayu Regency.

The decision-making process using a fuzzy inference system (FIS) logic can use one of the methods called the Tsukamoto method. The process carried out in this method is the same as the fuzzy method in general, namely the formation of fuzzy sets, the fuzzification process, defuzzification, and measuring the accuracy of the result. The purpose of this study was to apply the Tsukamoto method to predict the yield of oil palm production at PT. Waru Kaltim Plantation. Based on the analysis using the Tsukamoto method, 36 fuzzy rules were obtained for each data from February 2013 to December 2015. The prediction results of palm oil production in 2013 did not change, except for May and August. In February, March, June, and August 2014 the level of production is constant, and almost throughout 2015, there was constant. The predicted MAPE for oil palm production was 31,522%, or in the fairly good category.

This research aims at knowing the comparison among Tsukamoto method, Mamdani method, and Sugeno method in deciding the production of incense at CV. Dewi Bulan. The research discussed about Tsukamoto method, Mamdani method and Sugeno method which consisted of four step, they are: fuzzyfication, forming a fuzzy rules, fuzzy logic analysis, and defuzzyfication. In conclusion, Sugeno method was found to be the best to be used in deciding the number of incense sticks production, comparing with the others. Sugeno method has probability of error value about 1,314%.

The ergonomics and comfort of employee’s health and working conditions are reflected in the efficiency of the work. For this reason, the analysis and evaluation of ergonomic risks in the working environment is of great importance. A novel REBA-FMEA models-based Pythagorean Fuzzy-VIKOR integrated model approach is introduced to assess ergonomic risks. The proposed methodology incorporates PF-VIKOR methodologies based on integrated REBA-FMEA. The following 10 phases comprise the suggested method. A production line case study was conducted. An assessment is conducted on six distinct hazardous occupational positions. The REBA method is used to compute the risk ratings associated with these hazards. The most optimal outcome in the assessment of multi decision-makers with uncertainty in the risk analysis of ergonomic working positions with the novel technique was obtained by computing Pythagorean fuzzy. The novel model overcomes the limitations of traditional methods with the integration of reliability engineering approaches and Pythagorean fuzzy logic. Assessments of ergonomic risks often involve subjective judgments, especially when considering human factors. Different individuals may perceive risks differently, and this subjectivity can introduce variability into the assessment process. The novel method proposed in this study fills the gap in the literature on the subjectivity of decision makers in evaluations.

ABSTRACT: Unconventional resources have become the core business of many petroleum companies to meet the increasing demand for energy. Several technologies and methods have been developed and deployed to unlock the potential of tight and ultra-tight formations. Hassi Tarfa (HTF) oil field is a thin and tight sandstone reservoir in Algeria, with an average permeability of less than 0.5 mD. However, all drilled wells in this field are vertical. Hydraulic Fracturing (HF) is the prime stimulation technique that is applied to increase oil recovery in unconventional reservoirs. Although the well production tremendously increases after fracking operation, it does not sustain for longer period of time, which keeps the estimated ultimate recovery (EUR) to be relatively low. In this study, a reservoir model was built and history matched, in order to consider three scenarios to optimize the horizontal lateral length in the HTF field. Then, multistage HF design was simulated using advanced 3D finite element software and exported to the model to estimate the potential increase of EUR. Sensitivities on number of HF stages, fluid volumes, and proppant were conducted to identify the optimal number of HF stages. The results of this study showed that, employing multistage hydraulic fracturing along horizontal drilling can significantly improve the oil recovery in HTF formation. Fracture length and the number of stages showed to be important design parameters. This study also identified the optimal range of operational parameters such as pumping schedule, proppant mass and perforation interval which are crucial to the cost reduction and operation efficiency. 1. INTRODUCTION Global energy demand has increased considerably in the last decades due to the increase of the world population, manufacturing activities, and the high living standards, which calls for efficient energy recovery methods. Oil recovery factor from primary depletion for shale and tight formation has been predicted typically to be less than 10% (Alvarez et al, 2016). To overcome the resistance of the low to extremely low permeability to hydrocarbons flow from reservoir to bottom-hole, the petroleum industry has started implementing several enhanced oil recovery (EOR) mechanisms and technologies (Chemmakh et al., 2021; Ozotta et al., 2021). The combination of horizontal well drilling and multi-stage fracturing, and completion technologies has been the key, over the last two decades, to economically unlock the potential of unconventional reservoirs (Shengnan and Wang, 2012; Kegang et al, 2016). The horizontal section drilled maximizes exposure to the reservoir, while multi-stage fractures increase drainage area and build an effective connection between reservoir and horizontal wellbore. Cost effective Hydraulic Fracturing (HF) design is crucial to mobilize oil in tight formation. An integrated approach is essential to optimize fracture parameters using a reliable geomechanical model and reliable reservoir simulations to predict the post-fracture productivity (Rahman et al, 2014). The industry experience has showed that one third of the fractured clusters contribute with up to 75% of production. Far et al. (2015) observed that resulting production is lithology dependent and some specific variables correlate well with reservoir fracturability and production. The approach developed maximizes the use of well logs and cuttings analysis to determine where to place the frac stage and perforation clusters. Several studies had investigated the optimum horizontal length to maximize recovery. In the Bakken formation in the Williston Basin, USA for instance, the horizontal lateral length typically exceeds 10,000 ft with more than 30 HF stages. It is substantiated in literature that the various in lithology and geology lead to different optimal stage intervals, therefore different ultimate oil recovery. Wang et al. (2019) proposed an innovative approach to optimize horizontal well fracturing with great success. A set of seismic methods were implemented to accurately visualize the formation around the wellbore, select the best azimuth, frac location and direction. Recently, artificial intelligence has been widely implemented in the petroleum industry. Andrei and Connel (2019) applied fuzzy logic to build a data driven model, which was used to identify reservoir quality and eventually horizontal well placement. Likewise, Elkin et al. (2018) employed Monte Carlo technique to optimize horizontal well length. Al Shueili et al. (2022) presented the lessons learned about how to approach horizontal well multistage fracturing program in tight multi-layered and laminated reservoir. Based on the production model, they reported that four to five HF stages for 1000 m lateral length is sufficient to connect the required layers.