scholarly journals Using Fuzzy Time Series (FTS) and Linear Programming for Production Planning and Planting Pattern Scheduling Red Onion

2019 ◽  
Vol 125 ◽  
pp. 23007 ◽  
Author(s):  
Aries Dwi Indriyanti ◽  
Dedy Rahman Prehanto ◽  
Ginanjar Setyo Permadi ◽  
Chamdan Mashuri ◽  
Tanhella Zein Vitadiar
Keyword(s):  
Time Series ◽  
Linear Programming ◽  
Production Planning ◽  
Fuzzy Time Series ◽  
Planning System ◽  
Cropping Pattern ◽  
Market Demand ◽  
Time Data ◽  
Cropping Patterns ◽  
Data Production

This study discusses the production planning system and scheduling shallots planting patterns using fuzzy time series and linear programming methods. In this study fuzzy time series to predict the number of requests and the results of predictions from fuzzy time series methods become one of the variables in the calculation of linear programming. Using supporting variables, demand data, production data, employment data, land area data, production profit data, data on the number of seedlings and planting time data are indicators used in processing the system. The system provides recommendations for cropping patterns and the number of seeds that must be planted in one period. The age of harvesting onions is ± 3-4 months from the planting process, the number of planting seeds is adjusted to the number of requests that have been predicted by using fuzzy time series and cropping pattern calculation process is carried out using linear programming. The results of this system are recommendations for farmers to plant seedlings, planting schedules, and harvest schedules to meet market demand.

scholarly journals Production Planning and Planting Pattern Scheduling Information System for Horticulture

2018 ◽  
Vol 31 ◽  
pp. 10004 ◽  
Author(s):  
Tanhella Zein Vitadiar ◽  
Farikhin Farikhin ◽  
Bayu Surarso
Keyword(s):  
Time Series ◽  
Information System ◽  
Production Planning ◽  
Research Result ◽  
Fuzzy Time Series ◽  
Series Method ◽  
Planting Pattern ◽  
Cropping Patterns ◽  
Planning Decisions ◽  
Time Series Method

This paper present the production of planning and planting pattern scheduling faced by horticulture farmer using two methods. Fuzzy time series method use to predict demand on based on sales amount, while linear programming is used to assist horticulture farmers in making production planning decisions and determining the schedule of cropping patterns in accordance with demand predictions of the fuzzy time series method, variable use in this paper is size of areas, production advantage, amount of seeds and age of the plants. This research result production planning and planting patterns scheduling information system with the output is recommendations planting schedule, harvest schedule and the number of seeds will be plant.

Impact of Optimal Cropping Patterns on Incomes in a Punjab District

1976 ◽  
Vol 15 (2) ◽  
pp. 218-221
Author(s):  
M. Arshad Chaudhry
Keyword(s):  
Developing Countries ◽  
Linear Programming ◽  
Short Note ◽  
Cropping Pattern ◽  
Small Farms ◽  
Cropping Patterns ◽  
Punjab Province ◽  
Optimal Cropping

To improve farm incomes in developing countries, the foremost question that the farmer must address himself to is: what cropping pattern best uses the fixed resources in order to get the highest returns? During the last decade, the agricultural economists have shown great interest in applying the tools of linear programming to individual farms. Most of the studies conducted elsewhere have shown that, under existing cropping pattern, farm resources were not being utilized optimally on the small farms.[l, 4]. We conducted a survey in the canal-irrigated areas of the Punjab province of Pakistan1 to investigate into the same problem. This short note aims at identifying the opti¬mal cropping pattern and to estimate the increase in farm incomes as a result of a switch towards it on the sampled farms.

scholarly journals PERENCANAAN BAHAN BAKU DAN HASIL PRODUKSI MENGGUNAKAN METODE LINIER PROGRAMMING SIMPLEK

2017 ◽  
Vol 12 (2) ◽  
Author(s):  
Monika Handayani ◽  
Eka Kusuma Dewi
Keyword(s):  
Linear Programming ◽  
Production Planning ◽  
Simplex Method ◽  
Raw Materials ◽  
Raw Material ◽  
Planning System ◽  
Testing System ◽  
Basic Solution ◽  
Linear Programming Method

<p>CV. Baja Utama Landasan Ulin is a business entity that manufactures various products using the basic ingredients of iron. In the management of raw materials for the production of common regulatory process raw materials into sections for further processing. This setting is often done manually without doing careful planning, so that at the end of each production process there are many remaining pieces of the raw materials that should be used in production. In addition to the determination of the production is necessary to reference how the product should be made for each type of existing products. This is often an important factor that pushed for the optimization of production planning in determining the number of products for each type of product and raw material consumption.Linear Programming is one of the methods used in production planning to regulate the use of raw materials is limited. Simplex method is part of the linear programming method that can be used in the production planning system implementation. Simplex method identifies an initial basic solution and then move systematically to other basic solution that has the potential to improve the value of the objective function.The calculation result of production planning using the simplex method can be used as a reference in the decision making production planning. By building an application using the simplex method can assist in the calculation of production peencanaan more efficiently and effectively. Accuracy testing system constructed show significant results with great value reached 94% level of accuracy.<br />Keywords: simplex, production planning, the maximum gain, linear programming</p>

scholarly journals FUZZY TIME SERIES MODEL CHENG UNTUK MERAMALKAN VOLUME HASIL PANEN PADA TANAMAN GARUT

Telematika ◽  
2020 ◽  
Vol 17 (1) ◽  
pp. 11
Author(s):  
Rifki Indra Perwira ◽  
Danang Yudhiantoro ◽  
Endah Wahyurini
Keyword(s):  
Time Series ◽  
Seed Quality ◽  
Crop Yields ◽  
Genetic Data ◽  
Time Series Model ◽  
Environmental Data ◽  
Fuzzy Time Series ◽  
Market Demand ◽  
Cultivation Technique ◽  
Processed Products

Arrowroot is an alternative food substitute that can be used as processed flour or starch. This arrowroot can also produce several processed products such as arrowroot chips. The number of arrowroot requests from various regions causes the need for accurate calculations related to the volume of harvest from the arrowroot. Fuzzy logic is a method that can be used to predict arrowroot yields every period to meet market demand. The parameters used in this system are based on environmental data (temperature humidity, climate, altitude), genetic data (age and variety), and cultivation technique data (seed quality, fertilizing, planting media). The results of this study are in the form of an application to predict the volume of arrowroot crop yields based on these parameters. From the results of MAPE, get a percentage of 11.7% which indicates that the level of accuracy using the fuzzy cheng time series model is said to be useful for forecasting on arrowroot plants.

scholarly journals A Phenology-Based Method to Map Cropping Patterns under a Wheat-Maize Rotation Using Remotely Sensed Time-Series Data

2018 ◽  
Vol 10 (8) ◽  
pp. 1203 ◽  
Author(s):  
Jianhong Liu ◽  
Wenquan Zhu ◽  
Clement Atzberger ◽  
Anzhou Zhao ◽  
Yaozhong Pan ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Vegetation Index ◽  
Agricultural Land ◽  
Threshold Model ◽  
Series Data ◽  
Cropping Pattern ◽  
Validation Data ◽  
Cropping Patterns ◽  
Modis Evi

Agricultural land use and cropping patterns are closely related to food production, soil degradation, water resource management, greenhouse gas emission, and regional climate alterations. Methods for reliable and cost-efficient mapping of cropping pattern, as well as their changes over space and time, are therefore urgently needed. To cope with this need, we developed a phenology-based method to map cropping patterns based on time-series of vegetation index data. The proposed method builds on the well-known ‘threshold model’ to retrieve phenological metrics. Values of four phenological parameters are used to identify crop seasons. Using a set of rules, the crop season information is translated into cropping pattern. To illustrate the method, cropping patterns were determined for three consecutive years (2008–2010) in the Henan province of China, where reliable validation data was available. Cropping patterns were derived using eight-day composite MODIS Enhanced Vegetation Index (EVI) data. Results show that the proposed method can achieve a satisfactory overall accuracy (~84%) in extracting cropping patterns. Interestingly, the accuracy obtained with our method based on MODIS EVI data was comparable with that from Landsat-5 TM image classification. We conclude that the proposed method for cropland and cropping pattern identification based on MODIS data offers a simple, yet reliable way to derive important land use information over large areas.

scholarly journals Studi Optimasi Pola Tanam Daerah Irigasi Gong Gang Kecamatan Parang Kabupaten Magetan

2016 ◽  
Vol 14 (1) ◽  
pp. 51
Author(s):  
Ernawan Setyono ◽  
Safik Mucharom
Keyword(s):  
Linear Programming ◽  
Spatial Patterns ◽  
Food Production ◽  
Cropping Pattern ◽  
Water Requirements ◽  
Cropping Intensity ◽  
Cropping Patterns ◽  
Optimization Study ◽  
Planting Season

Along with the increasing population growth, the need for food also increased. To meet that need for optimization studies of the factors that influence spatial patterns of planting in order to increase the volume of food production. Determination of the cropping pattern that will be used after the first known dependable flow and water requirements. Through the design cropping pattern is expected cropping intensity can be enhanced and existing water sources can be used optimally. linear programming used in this optimization study using QM for Windows 4 software. The most optimal results from the optimization that has been done is an alternative was began on November  cropping patterns : rice-palawija-sugarcane season crops beginning 1st week of November, profits amounted to Rp 106.729.700.000 to the area that can be cultivated for the planting season I: Rice = 1990 ha, palawija = 307 ha sugarcane = 89 ha, planting season II: Rice = 1990 ha, palawija = 307 ha sugarcane = 89 ha, and planting season III:  Rice = 258,2753 ha Palawija = 2038,725 ha, sugarcane = 89 ha

scholarly journals Optimization of irrigation cropping pattern by using linear programming: Case study on irrigation area of Parsanga, Madura Island, Indonesia

2018 ◽  
Vol 39 (1) ◽  
pp. 51-60 ◽  
Author(s):  
Pitojo T. Juwono ◽  
Lily Montarcih Limantara ◽  
Fathor Rosiadi
Keyword(s):  
Linear Programming ◽  
Water Discharge ◽  
Agricultural Product ◽  
Cropping Pattern ◽  
Optimization Analysis ◽  
Cropping Patterns ◽  
Irrigation Area ◽  
Maximum Benefit ◽  
Production Benefit

AbstractThe irrigation area of Parsanga is located in Sumenep Regency, Madura Island of Indonesia. This irrigation area is 500 ha and the existing cropping pattern is paddy–paddy–second crop. There is water discharge deficiency due to the existing cropping pattern mainly in the dry season. Thus, this study intends to optimize the cropping pattern for 3 condition so that it can produce the maximum benefit of agricultural product. The first cropping pattern is paddy/second crop–second crop–paddy/second crop; the second proposition is paddy/second crop –paddy/second crop–second crop; and the third proposition is paddy–second crop–paddy/second crop. The optimization analysis is carried out by using the linear programming. The suggested three cropping patterns are not only able to solve the water deficiency; they can also present the more production benefit than the existing condition.

scholarly journals Peramalan Data IHSG Menggunakan Fuzzy Time Series

2013 ◽  
Vol 7 (1) ◽  
Author(s):  
Seng Hansun
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Time Series Analysis ◽  
Real Time ◽  
Soft Computing ◽  
Time Series Data ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Time Data ◽  
Series Analysis

AbstrakFuzzy time series merupakan salah satu metode soft computing yang telah digunakan dan diterapkan dalam analisis data runtun waktu. Tujuan utama dari fuzzy time series adalah untuk memprediksi data runtun waktu yang dapat digunakan secara luas pada sembarang data real time, termasuk data pasar modal.Banyak peneliti yang telah berkontribusi dalam pengembangan analisis data runtun waktu menggunakan fuzzy time series, seperti Chen dan Hsu [1], Jilani dkk. [2], serta Stevenson dan Porter [3]. Dalam penelitian ini, dicoba untuk menerapkan metode fuzzy time series pada salah satu indikator pergerakan harga saham, yakni data IHSG (Indeks Harga Saham Gabungan).Kinerja metode yang diusulkan dievaluasi dengan menghitung tingkat akurasi dan tingkat kehandalan metode fuzzy time series yang diterapkan pada data IHSG. Melalui pendekatan ini, diharapkan metode fuzzy time series dapat menjadi alternatif untuk memprediksi data IHSG yang merupakan salah satu indikator pergerakan harga saham di Indonesia. Kata kunci – fuzzy time series, data runtun waktu, soft computing, IHSG AbstractFuzzy time series is one of the soft computing method that been used and implemented in time series analysis. The main goal of fuzzy time series is to predict time series data that can be used widely in any real time data, including stock market share.Many researchers have contributed in the development of fuzzy time series analysis, such as Chen and Hsu [1], Jilani [2], and Stevenson and Porter [3]. In this research, we will try to implement the fuzzy time series method in one of the stock market change indicator, i.e. the Jakarta composite index or also known as IHSG (Indeks Harga Saham Gabungan).The research is continued by calculating the accuracy and robustness of the method which has been implemented on IHSG data. By this approach, we hope it can be an alternative to predict the IHSG data which is an indicator of stock price changes in Indonesia. Keywords – fuzzy time series, time series data, soft computing, IHSG

A production planning system based on linear programming

Omega ◽  
1973 ◽  
Vol 1 (4) ◽  
pp. 499-504 ◽  
Author(s):  
Goeran Ahrsjoe ◽  
Stig Svedunger
Keyword(s):  
Linear Programming ◽  
Production Planning ◽  
Planning System
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