Least Cost Diet for Children Two to Three Years in Malaysia Using Linear Programming Approach

The early period of life, namely from birth to two years of age is critical for the promotion o f optimal growth, brain development as well as health and behavioural development. Thus, attaining the daily required nutrition during this stage of life is very crucial since nutrition is strongly associated with a child’s development at a very young age . It is a major challenge for Malaysians to ensure children get a balanced diet, especially children from families of low socioeconomic status. As reported in the Edge Weekly, the review found that 24.9% or nearly one in four children in Malaysia experien ced moderate or severe food insecurity due to financial constraints. In this study, a linear programming diet model is used to determine an affordable cheapest food basket that satisfies the daily recommended nutritional requirements for children between two to three years old in Malaysia. POM -QM for Windows Version 5.2 by Howard J. Weiss is used. Initial finding shows that the average costs are RM2.69. This food basket consists of 474g of eggs, 55g of tofu, 29g of papaya, 5g of spinach and 201g of potato. With this food basket and estimated food expenditure, parents can save for 40% of their child’s daily food expenditure

Cyclical Nurse Scheduling in Shah Alam Hospital Using Goal Programming

A shift work schedule is extremely important to obtain the optimum result of work allocation since it involves 24 hours of continuous services. Every nurse could not avoid shift work schedule since their services are very important towards the patients in the hospital. The major objective of the study is to propose a cyclical nurse scheduling in the Coronary Care Unit (CCU) at Shah Alam hospital using Goal Programming. It is to help the head nurse to spend less effort on building new schedules periodically and increase the satisfaction among nurses by providing fairness towards their schedules. There were nine hard constraints and three soft constraints for the nurse scheduling model. The results presented the optimal solution where all goals were achieved thus, it provided a fair schedule for 15 nurses in 15 days. Then, the schedule pattern was rotated among nurses based on the 15 schedules set in 225 days. The first schedule set will be used by the nurse for the first 15 days, then will be continuously rotated for another 15 days until all the nurses reached and experienced every 15 sets of the schedule. The schedule was generated using LINGO software which it took a short time to solve the problem

A Comparison of Linear and Integer Linear Programming for the Profit Optimization in Bakery Production: A Case Study at Temptlicious Enterprise

Nowadays, the bakery industry is widely spread and famous because it can be run by a small industry or a large industry. Seeing as bakery businesses, especially small industries prefer to allocate scarce resources through trial and error to maximize profit. As a result, the company has had difficulty allocating scarce resources, affecting gross profit and gross profit margin (GPM). As a consequence, the goals of this study were (i) to determine the total number of selected products that Templicious Enterprise should produce, (ii) to compare final results using Linear Programming (LP), Integer Linear Programming (ILP), and trial-and-error methods and (iii) to find out the limits of the maximum and minimum for each type of product using sensitivity analysis. The LP and ILP methods are calculated manually and using QM for Windows. As a result, it shows that the Templicious Enterprise should produce a total of one cycle (3 units) of standard pavlova, three cycles (24 units) of superbaby pavlova and one cycle (2 units) of personal pavlova for a total profit of RM 446.99. The result was obtained using ILP, and lastly, it shows that if prices rise, the Temptlicious Enterprise will have to raise the price of the pavlova they make to avoid making a loss

Fuzzy Time Series for Projecting School Enrolment in Malaysia

There are a variety of approaches to the problem of predicting educational enrolment.  However, none of them can be used when the historical data are linguistic values.  Fuzzy time series is an efficient and effective tool to deal with such problems. In this paper, the forecast of the enrolment of pre-primary, primary, secondary, and tertiary schools in Malaysia is carried out using fuzzy time series approaches. A fuzzy time series model is developed using historical dataset collected from the United Nations Educational, Scientific, and Cultural Organization (UNESCO) from the year 1981 to 2018.  A complete procedure is proposed which includes: fuzzifying the historical dataset, developing a fuzzy time series model, and calculating and interpreting the outputs. The accuracy of the model are also examined to evaluate how good the developed forecasting model is. It is tested based on the value of the mean squared error (MSE), Mean Absolute Percent Error (MAPE) and Mean Absolute Deviation (MAD).  The lower the value of error measure, the higher the accuracy of the model.  The result shows that fuzzy time series model developed for primary school enrollments is the most accurate with the lowest error measure, with the MSE value being 0.38, MAPE 0.43 and MAD 0.43 respectively

Autoregressive Integrated Moving Average vs. Artificial Neural Network in Predicting COVID-19 Cases in Malaysia

On March 11,2020, the World Health Organization (WHO) declared Covid-19 as a global pandemic. The spread of Covid-19 has threatened many lives in nearly every country. In Malaysia, the health authorities have expressed concerns over an increasing number of cases and deaths. Due to the lockdown, this pandemic has also had an impact on most economic activities.  Consequently, it is crucial to develop a reliable forecasting model to anticipate the number of cases. This study proposes two models: Autoregressive Integrated Moving Average (ARIMA) and Multilayer Perceptron Neural Network (MPNN) in predicting the number of Covid-19 cases in Malaysia. Using Mean Absolute Error (MAE), the effectiveness and forecasting accuracy of the two models are compared and assessed.  The lowest the value of MAE, the more accurate the forecasted outputs.  The secondary data used in this study was the average number of Covid-19 cases each day in Malaysia from March 1, 2020, to March 29, 2021.  To evaluate the data, RStudio and Alyuda NeuroIntelligence are utilised.  As a consequence, the ARIMA (4,1,5) model provided the best fit to the data when compared to other ARIMA models, with a Mean Absolute Error (MAE) score of 1096.799.  However, Multilayer Perceptron Neural Network (MPNN), which had the lowest MAE value of 334.591, outperformed ARIMA in terms of performance.  The MPNN model was then used to forecast the number of Covid-19 instances for the next 30 days.  According to the findings, daily increases in cases are anticipated

Forecasting of Air Pollution Index PM2.5 Using Support Vector Machine(SVM)

Air pollution is a current monitored problem in areas with high population density such as big cities. Many regions in Malaysia are facing extreme air quality issues. This situation is caused by several factors such as human behavior, environmental awareness and technological development.  Accessing the air pollution index (API) accurately is very important to control its impact on environmental and human health.  The work presented here aims to access air pollution index of PM2.5 using Support Vector Machine (SVM) and to compare the accuracy of four different types of the kernel function in Support Vector Machine (SVM).  The data used is provided by the Department of Environment (DOE) and it is recorded from two Continuous Air Quality Monitoring Stations (CAQM) located at Tanah Merah and Kota Bharu. The results are analyzed using mean absolute error (MAE) and root mean squared error (RMSE). It is found that the proposed model using Radial Basis Function (RBF) with its parameters of cost and gamma equal to 100 can effectively and accurately forecast the air pollution index with Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) of 0.03868583 and 0.06251793 respectively for API in Kota Bharu and 0.03857308 (MAE) and 0.05895648 (RMSE) for API in Tanah Merah

A Comparison Study on Fuzzy Time Series and Holt-Winter Model in Forecasting Tourist Arrival in Langkawi, Kedah

The tourism industry in Malaysia has been growing significantly over the years. Tourism has been one of the major donors to Malaysia’s economy. Based on the report from the Department of Statistics, a total of domestic visitors in Malaysia were recorded at about 221.3 million in 2018 with an increase of 7.7% alongside a higher record in visitor arrivals and tourism expenditure.  This study aims to make a comparison between two methods, which are Fuzzy Time Series and Holt-Winter in forecasting the number of tourist arrival in Langkawi based on the monthly tourist arrival data from January 2015 to December 2019. Both models were generated using Microsoft Excel in obtaining the forecast value.  The Mean Square Error (MSE) has been calculated in this study to get the best model by looking at the lowest value. The result found that Holt-Winter has the lowest value that is 713524285 compared to the Fuzzy Time Series with a value of 2625517469. Thus, the Holt-Winter model is the best method and has been used to forecast the tourist arrival for the next 2 years. The forecast value for the years 2020 and 2021 are displayed by month

Comparison of Fuzzy Time Series and ARIMA Model for Predicting Stock Prices

The stock market has always been a contentious topic in society, and it is a place where economic standards are established. The stock market is incredibly unpredictable and turbulent. This means that the shares may fluctuate for reasons that are sometimes difficult to understand.  Due to this uncertainty, many investors believe the stock market as a risky investment.  Therefore, having an accurate picture of future market environment is crucial to minimising losses. Forecasting is a technique of predicting the future based on the outcome of the previous data.  There are a wide range of forecasting algorithms, however, this study only focuses on these two techniques: Auto Regressive Moving Average (ARIMA) model and Fuzzy Time Series (FTS) Model. The goal of this study is to evaluate and compare the effectiveness of the ARIMA model and the FTS model in predicting sample data of stock prices of Top Glove Corporation Berhad since this company is the largest glove supplier in the world and plays a significant role in the Covid-19 global pandemic crisis. The error measures that were taken into consideration consist of Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE). These measurements were computed numerically and graphically using a statistical programme called EViews.  The outcome shows that the ARIMA model performs better than the FTS model in terms of forecasting accuracy and provides the lowest values of MAPE, MSE, and RMSE, which are 10.58757, 0.926354, and 0.962473, respectively

Shift Scheduling with the Goal Programming Approach in Fast-Food Restaurant: McDonald’s in Kelantan

A major fast-food restaurant chain, such as McDonald's, must perform well to maintain its credibility with customers and dominance over other competitors. A fair and balanced shift schedule of workers must be generated to ensure that the workers provide the best service and production for the restaurant. Consequently, this study proposed a fair and efficient workforce schedule at a McDonald's restaurant in Kelantan, Malaysia. Furthermore, the goal programming method and the LINGO software are used in this study to develop the best schedule for the workers over a 28-day period. Five hard constraints and three soft constraints are identified. The primary goal of this study, which demanded the same total workload for each worker, was met. However, the other two goals are not fully achieved but have little impact on the workers due to the 18-hour operation and rotation of schedules among workers. Finally, the generated schedule pattern has been shown to provide a better schedule in terms of having the same total number of shifts for each worker and giving each worker the same total number of off days. Â

Prediction of Future Stock Price Using Recurrent Neural Network

The stock market can affect businesses in various ways, as the rise and fall of a company's share price values impact its market capitalization and overall market value. However, forecasting stock market returns is challenging because financial stock markets are unpredictable and non-linear, with factors such as market trends, supply and demand ratios, global economies, and public opinion affecting stock prices. With the advent of artificial intelligence and increased processing power, intelligent prediction techniques have become more effective in forecasting stock values. This study proposes a Recurrent Neural Network (RNN) model that uses a deep learning machine to predict stock prices. The process includes five stages: data analysis, dataset preparation, network design, network training, and network testing. The accuracy of the model is determined by the mean square error (MSE) and root mean square error (RMSE), which are 1.24 and 1.12, respectively. The predicted closing price is then compared to the actual closing price to assess the accuracy of the model. Finally, it is suggested that this approach can also be used to forecast other volatile time-series data