Optimization of Forecasting Moving Average Error in Probabilistic Demand Using Genetic Algorithm Based Fuzzy Logic

2012 ◽  
Vol 576 ◽  
pp. 710-713
Author(s):  
Chairul Saleh ◽  
Muhammad Ridwan Andi Purnomo ◽  
Hayati Mukti Asih
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Moving Average ◽  
Demand Forecasting ◽  
Fuzzy Time Series ◽  
Average Error ◽  
Demand Forecast ◽  
Advantages And Disadvantages ◽  
Excess Production ◽  
Before And After

Demand forecasting is one of the most critical factors in production planning. The uncertain demand, which is the basic idea of planning the production level, nowadays is one of serious problems. The inaccurate demand forecasting could affect to excess production or shortage stocks which led to lost sales. Usually, the forecasted result is hard to represent real condition. Some studies already conducted related to fuzzy time series, each of them has its own advantages and disadvantages compared to other approaches. This research presents the combination of simple moving average forecasting and fuzzy logic model to demand forecast. Then, genetic algorithm (GA) is applied to optimize the mean square error (MSE) inside the fuzzy system. The MSE before and after GA optimization is 0,2192 and 0,1821, respectively.

scholarly journals Penggunaan metode algoritma wagner within dalam upaya pengendalian persediaan scrap besi di PT XYZ

2020 ◽  
Vol 5 (1) ◽  
pp. 56-73
Author(s):  
Somadi ◽  
Syah Rajendra Hari Septa ◽  
Nila Dahlia Juita
Keyword(s):  
Control Method ◽  
Moving Average ◽  
Demand Forecasting ◽  
Lot Size ◽  
Total Size ◽  
Total Cost ◽  
Smoothing Methods ◽  
Before And After ◽  
Iron Scrap ◽  
Total Inventory

The research objective is to determine the total size of the lot of iron scrap orders, and the total cost of the company's inventory before and after carrying out the method of controlling iron scrap inventory using the Wagner Within Algorithm method. Demand forecasting uses the Single Moving Averge, Weight Moving Average, and Exponential Smoothing methods. Based on the results of the study, the total lot size of iron scrap material orders is smaller than the size of previous lot orders without using the inventory control method, which is 15,362 tons per year. Total inventory of Rp. 105,076,125,840 and the total cost is more optimal when compared with the total cost of inventory with the company system that is Rp. 109,734,165,840 so that the company can save costs by Rp. 4,658,040,000.

scholarly journals Comparison of Fuzzy Time Series and ARIMA to Forecast Tourist Arrivals to Homestay in Pahang

2021 ◽  
Vol 6 (4) ◽  
pp. 80-89
Author(s):  
Maizatul Akhmar Jafridin ◽  
Nur Fatihah Fauzi ◽  
Rohana Alias ◽  
Huda Zuhrah Ab Halim ◽  
Nurizatul Syarfinas Ahmad Bakhtiar ◽  
...  
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Demand Forecasting ◽  
Secondary Data ◽  
Fuzzy Time Series ◽  
Percentage Error ◽  
Strategic Decisions ◽  
Forecasting Performance ◽  
Future Events ◽  
Time Series Method

Predictions of future events must be incorporated into the decision-making process. For tourism demand, forecasting is very important to help directors and investors to make decisions in operational, tactical, and strategic decisions. This study focuses on forecasting performance between Fuzzy Time Series and ARIMA to forecast the tourist arrivals in homestays in Pahang. The main objective of this study is to compare and identify the best method between Fuzzy Time Series and Autoregressive Integrated Moving Average (ARIMA) in forecasting the arrival of tourists based on the secondary data of tourist arrivals to homestay in Pahang from January 2015 to December 2018. ARIMA models are flexible and widely used in time-series analysis and Fuzzy Time Series which do not need large samples and long past time series. These two methods have been compared by using the mean square error (MSE) and mean absolute percentage error (MAPE) as the forecast measures of accuracy. The results show that Fuzzy Time Series outperforms the ARIMA. The lowest value of MSE and MAPE was obtained from using the Fuzzy Time Series method at values 2192305.89 and 11.92256, respectively.

Genetic algorithm based fuzzy time series tourism demand forecast model

2016 ◽  
Vol 116 (3) ◽  
pp. 483-507 ◽  
Author(s):  
Sumit Sakhuja ◽  
Vipul Jain ◽  
Sameer Kumar ◽  
Charu Chandra ◽  
Sarit K Ghildayal
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Population Size ◽  
Fuzzy Time Series ◽  
Interval Length ◽  
Marginal Effect ◽  
Tourism Demand ◽  
Demand Forecast ◽  
Content Type ◽  
Series Order

Purpose – Many studies have proposed variant fuzzy time series models for uncertain and vague data. The purpose of this paper is to adapt a fuzzy time series combined with genetic algorithm (GA) to forecast tourist arrivals in Taiwan. Design/methodology/approach – Different cases are studied to understand the effect of variation of fuzzy time series order, number of intervals and population size on the fitness function which decreases with increase in fuzzy time series order and number of fuzzy intervals, but do not have marginal effect due to change in population size. Findings – Results based on an example of forecasting Taiwan’s tourism demand was used to verify the efficacy of proposed model and confirmed its superiority to existing models providing solutions for different orders of fuzzy time series, number of intervals and population size with a smaller forecasting error as measured by root mean square error. Originality/value – This study provides a viable forecasting methodology, adapting a fuzzy time series combined with an evolutionary GA. The proposed hybridized framework of fuzzy time series and GA, where GA is used to calibrate fuzzy interval length, is flexible and replicable to many industrial situations.

scholarly journals Time Series Analysis to Predicting Demand of Roasted Coffee Production

2019 ◽  
Vol 10 (5) ◽  
pp. 26
Author(s):  
Agatha Rinta Suhardi ◽  
Shendy Amalia ◽  
Shinta Oktafien ◽  
Siska Ayudia Adiyanti ◽  
Siti Komariah ◽  
...  
Keyword(s):  
Mean Squared Error ◽  
Moving Average ◽  
Demand Forecasting ◽  
Moving Averages ◽  
Roasted Coffee ◽  
Coffee Production ◽  
Absolute Deviation ◽  
Forecasting Method ◽  
Excess Production ◽  
Production Demand

Consumer demand conditions for fluctuating roasted coffee and ineffective production planning often lead to excessive production. Excess production will lead to wasteful costs and maintenance of quality on roasted coffee. Production demand forecasting is the basis for making production demand decisions. The purpose of this study is to predict the number of production requests for the next period and determine the most suitable forecasting method in determining the amount of roasted coffee production demand. The object of the data taken is roasted coffee. Analysis methods use moving averages, weighted moving averages, and exponential smoothing. In determining the most suitable forecasting method based on the Mean Absolute Deviation (MAD) forecasting value and the smallest Mean Squared Error (MSE) of each method used. The results of this study indicate that the most suitable forecasting method is using a Weighted Moving Average with a three-month period and forecasting roasted coffee production for November 2016 of 38.3 kg.

scholarly journals Stocks management through application of demand forecast methods: a case study

2016 ◽  
Vol 7 (5) ◽  
pp. 699
Author(s):  
Lucas Lopes Filholino Rodrigues ◽  
Igor Henrique Inácio de Oliveira ◽  
Maurílio Fagundes Alexandre ◽  
Rodrigo Rodrigues Castorani ◽  
Celso Jacubavicius
Keyword(s):  
Inventory Management ◽  
Moving Average ◽  
Demand Forecasting ◽  
Metropolitan Region ◽  
Demand Forecast ◽  
Naive Method ◽  
Forecast Models ◽  
The One ◽  
First Moment ◽  
Forecasting Techniques

The present study consists in assessing the feasibility of implementing demand forecasting techniques due to the optimization of inventory management, so that it is objective the reduce storage costs and to have the least amount of stationary material stock in a certain period. Data analysis was for application of techniques based on the real case of a multinational company in the segment of electronic and digital systems in the infrastructure area, which operates in the metropolitan region of São Paulo.The study aims to evaluate the behavior of the studied company demand, in order to demonstrate some forecast models for it and thus being able to identify the most appropriate method to get the highest possible degree of assertiveness. At a first moment, there will be a survey of data concerning the company's historical demand, including the forecast used at the latest period, and then to survey the state of the art discussed topic, in order to clarify the reader, and as a result: the analysis of the collected data and the implementation of demand forecasting techniques presented in bibliographic references.After performing an analysis of the naive method demand forecasting practiced by the company, was carried out the application of different forecasting methods and found out that the method that best suits the given demand was the moving average, which provided the optimization of cost of storage in approximately 63% of the one presented by the naive method and also a gain of approximately R $ 2,000,000.00 during the studied period, thus proving the effectiveness of demand forecasting application for inventory management.

scholarly journals Vision-Based Structural FE Model Updating Using Genetic Algorithm

2021 ◽  
Vol 11 (4) ◽  
pp. 1622
Author(s):  
Gun Park ◽  
Ki-Nam Hong ◽  
Hyungchul Yoon
Keyword(s):  
Genetic Algorithm ◽  
Model Updating ◽  
Structural Parameters ◽  
Fe Model ◽  
Structural Members ◽  
Stiffness Reduction ◽  
Before And After ◽  
Scale Test ◽  
Fe Model Updating ◽  
Story Building

Structural members can be damaged from earthquakes or deterioration. The finite element (FE) model of a structure should be updated to reflect the damage conditions. If the stiffness reduction is ignored, the analysis results will be unreliable. Conventional FE model updating techniques measure the structure response with accelerometers to update the FE model. However, accelerometers can measure the response only where the sensor is installed. This paper introduces a new computer-vision based method for structural FE model updating using genetic algorithm. The system measures the displacement of the structure using seven different object tracking algorithms, and optimizes the structural parameters using genetic algorithm. To validate the performance, a lab-scale test with a three-story building was conducted. The displacement of each story of the building was measured before and after reducing the stiffness of one column. Genetic algorithm automatically optimized the non-damaged state of the FE model to the damaged state. The proposed method successfully updated the FE model to the damaged state. The proposed method is expected to reduce the time and cost of FE model updating.

scholarly journals A new evaluation and prediction model of sound quality of high-speed permanent magnet motor based on genetic algorithm-radial basis function artificial neural network

2021 ◽  
Vol 104 (3) ◽  
pp. 003685042110311
Author(s):  
Kai Hu ◽  
Guangming Zhang ◽  
Wenyi Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Basis Function ◽  
High Speed ◽  
Objective Evaluation ◽  
Sound Quality ◽  
Average Error ◽  
Permanent Magnet Motor ◽  
Predicted Model

Sound quality (SQ) has become an important index to measure the competitiveness of motor products. To better evaluate and optimize SQ, a novelty SQ evaluation and prediction model of high-speed permanent magnet motor (HSPMM) with better accuracy is presented in this research. Six psychoacoustic parameters of A-weighted sound pressure level (ASPL), loudness, sharpness, roughness, fluctuation strength (FS), and perferred-frequency speech interference (PSIL) were adopted to objectively evaluate the SQ of HSPMM under multiple operating conditions and subjective evaluation was also conducted by the combination of semantic subdivision method and grade scoring method. The evaluation results show that the SQ is poor, which will have a certain impact on human psychology and physiology. The correlation between the objective evaluation parameters and the subjective scores is analyzed by coupling the subjective and objective evaluation results. The average error of multiple linear regression (MLR) model is 7.10%. It has good accuracy, but poor stability. In order to improve prediction accuracy, a new predicted model of radial basis function (RBF) artificial neural network was put forward based on genetic algorithm (GA) optimization. Compared with MLR, its average error rate is reduced by 3.16% and the standard deviation is reduced by 1.841. In addition, the weight of each objective parameter was analyzed. The new predicted model has a better accuracy. It can evaluate and optimize the SQ exactly. The research methods and conclusions of this paper can be extended to the evaluation, prediction, and optimization of SQ of other motors.

scholarly journals Selection of Scholarship Recipient by Implementing Genetic Algorithm and Fuzzy Logic

2021 ◽  
Vol 1933 (1) ◽  
pp. 012069
Author(s):  
Yohanssen Pratama ◽  
Monalisa Pasaribu ◽  
Joni Nababan ◽  
Dayani Sihombing ◽  
Dicky Gultom
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Selection Of

MULTIFRACTAL FLUCTUATIONS OF JIUZHAIGOU TOURISTS BEFORE AND AFTER WENCHUAN EARTHQUAKE

Fractals ◽  
2013 ◽  
Vol 21 (01) ◽  
pp. 1350001 ◽  
Author(s):  
KAI SHI ◽  
WEN-YONG LI ◽  
CHUN-QIONG LIU ◽  
ZHENG-WEN HUANG
Keyword(s):  
Wenchuan Earthquake ◽  
Moving Average ◽  
Long Term Memory ◽  
Short Term ◽  
Dynamical Mechanism ◽  
Before And After ◽  
Previous State ◽  
Multifractal Behavior ◽  
Multifractal Nature

In this work, multifractal methods have been successfully used to characterize the temporal fluctuations of daily Jiuzhai Valley domestic and foreign tourists before and after Wenchuan earthquake in China. We used multifractal detrending moving average method (MF-DMA). It showed that Jiuzhai Valley tourism markets are characterized by long-term memory and multifractal nature in. Moreover, the major sources of multifractality are studied. Based on the concept of sliding window, the time evolutions of the multifractal behavior of domestic and foreign tourists were analyzed and the influence of Wenchuan earthquake on Jiuzhai Valley tourism system dynamics were evaluated quantitatively. The study indicates that the inherent dynamical mechanism of Jiuzhai Valley tourism system has not been fundamentally changed from long views, although Jiuzhai Valley tourism system was seriously affected by the Wenchuan earthquake. Jiuzhai Valley tourism system has the ability to restore to its previous state in the short term.

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