Optimal Input Combinations Research Articles

Satisfacción Laboral en las Microempresas Productoras de Derivados Lácteos del Cantón Cañar

El artículo resalta la importancia que tiene la satisfacción laboral de los empleados para el rendimiento empresarial; y, cuales son las variables relacionadas con la satisfacción, para ello se ha considerado el criterio de varios autores que aportan con sus conocimientos y definiciones que permiten fundamentar la investigación y comprender el panorama. El objetivo fue conocer el nivel de satisfacción laboral de los empleados que trabajan en las microempresas productoras de derivados lácteos, determinar cómo se sienten dentro de su puesto de trabajo, los factores internos y externos que conllevan a la satisfacción o insatisfacción y cómo esto influye en la productividad de las microempresas. De acuerdo a los resultados obtenidos se concluye que no existe correlación entre estas dos variables y cada una es independiente de la otra. La satisfacción laboral depende de otras variables como: la relación que existe con los jefes directivos, la supervisión recibida, las condiciones de trabajo, el sistema de recompensas, la motivación, las políticas de la empresa, las oportunidades de desarrollo profesional, la oportunidad de innovar y tomar riesgos, entre otras. Por otro lado la productividad está medida como la cantidad de productos obtenidos a partir de la utilización de los recursos; es decir que la combinación óptima de los insumos, una buena definición de procesos productivos y el ajuste de tiempos pueden mejorar los resultados esperados e incrementar la productividad.

Multilevel split of high-dimensional water quality data using artificial neural networks for the prediction of dissolved oxygen in the Danube River

In this study, a self-organizing network-based monitoring location similarity index (LSI) was coupled with Ward neural networks (WNNs) with the aim to create a more accurate, but less complex, multiple sites model for the prediction of dissolved oxygen (DO) content. This multilevel splitting approach comprises the LSI-based grouping of monitoring locations according to their similarity, and virtual splitting of processed data based on their features using WNN. The values of 18 water quality parameters monitored for 12 years at 17 sites on the Danube River flow thought Serbia were used. The optimal input combinations were selected using partial mutual information algorithm with termination based on the Akaike information criterion. LSI-based splitting has yielded two groups of monitoring sites that were modeled with separate WNN models. The number and types of selected inputs differed between those two groups of sites, which was in agreement with possible pollution sources. Multiple performance metrics have revealed that the WNN models perform similar or better than multisite DO prediction models published in the literature, while using two to four times less inputs and data patterns.

A Two-Stage Approach Integrating SOM- and MOGA-SVM-Based Algorithms to Forecast Spatial-temporal Groundwater Level with Meteorological Factors

To obtain accurate and effective forecasts of groundwater level, a two-stage approach integrating Self-Organizing Maps (SOM-), Multi-Objective Genetic Algorithm and Support Vector Machine (MOGA-SVM-based) algorithms is developed herein using the optimal input combinations of meteorological factors in a complex spatial-temporal groundwater system. In the first stage, an SOM-based clustering method is used to separate distinct and meaningful spatial groundwater zones. In the second stage, a temporal analysis model integrating MOGA with SVM is developed to identify the optimal input combinations. An actual application is conducted using the Choushui River Alluvial Fan in Taiwan as the case study; it currently has over-pumping and land subsidence problems. The MOGA-SVM model is compared with an existing model based on the SVM to demonstrate the superiority of the proposed approach. Moreover, the effective meteorological factors in different spatial zones can be determined by using the proposed approach to show the spatial characteristics, and these factors can significantly improve the forecasting performance, especially for long lead-time forecasting. In conclusion, the proposed spatial-temporal approach is applicable to a huge and complex groundwater system; it provides an alternative to the existing model for water resources management problems.

Global Solar Radiation Prediction Using Hybrid Online Sequential Extreme Learning Machine Model

Accurate global solar radiation prediction is highly essential for related research on renewable energy sources. The cost implication and measurement expertise of global solar radiation emphasize that intelligence prediction models need to be applied. On the basis of long-term measured daily solar radiation data, this study uses a novel regularized online sequential extreme learning machine, integrated with variable forgetting factor (FOS-ELM), to predict global solar radiation at Bur Dedougou, in the Burkina Faso region. Bayesian Information Criterion (BIC) is applied to build the seven input combinations based on speed (Wspeed), maximum and minimum temperature (Tmax and Tmin), maximum and minimum humidity (Hmax and Hmin), evaporation (Eo) and vapor pressure deficiency (VPD). For the difference input parameters magnitudes, seven models were developed and evaluated for the optimal input combination. Various statistical indicators were computed for the prediction accuracy examination. The experimental results of the applied FOS-ELM model demonstrated a reliable prediction accuracy against the classical extreme learning machine (ELM) model for daily global solar radiation simulation. In fact, compared to classical ELM, the FOS-ELM model reported an enhancement in the root mean square error (RMSE) and mean absolute error (MAE) by (68.8–79.8%). In summary, the results clearly confirm the effectiveness of the FOS-ELM model, owing to the fixed internal tuning parameters.

Maksimalisasi Keuntungan Dengan Pendekatan Metode Simpleks Kasus Pada Pabrik Sosis SM

The purpose of this study are to (1) determine the combination of inputs used in producing products such as beef sausages and veal sausage meatball; and (2) determine the optimal combination whether the product can provide the maximum profit. In order to determine the combination of inputs and maximum benefits can be used linear programming with graphical and simplex method. The valuation result shows that the optimal input combination would give a profit of Rp. 1.115 million per day.

Determinants of tomato farmers efficiency in Mymensingh district of Bangladesh: Data Envelopment Analysis approach

Tomato is one of the major vegetables in Bangladesh that experienced massive productivity growth after independence. Nevertheless, farmers are struggling to find out optimal input combination in their farm that causes inefficient input use. Therefore, this study estimated the factors affecting efficiency of tomato farmers in Mymensingh of Bangladesh. Input oriented Data Envelopment Analysis (DEA) was employed for measuring efficiency while Tobit regression model was used to estimate the factors affecting efficiency. A total of 60 tomato farmers were selected using random sampling technique. Mean technical efficiency for tomato farmers was 0.83 implies that tomato farmers can reduce their input use by 17%. Education, training and high yielding variety adoption had positive effect on efficiency while age of tomato farmer’s had negative effect on efficiency. Efficiency increased with the farmer’s education, training and variety adoption. Farmer’s adopting local high yielding variety was more efficient than that of exotic high yielding variety. In addition, efficiency of farmers reduced with their age. Improvement of tomato farmer’s efficiency is possible if farmers received education, training and local high yielding variety.J. Bangladesh Agril. Univ. 16(1): 93-97, April 2018

Combined gamma and M-test-based ANN and ARIMA models for groundwater fluctuation forecasting in semiarid regions

The shortage of surface water in arid and semiarid regions has led to the more use of the groundwater resources. In these areas, the groundwater is essential for activities such as water supply and irrigation. One of the most important stages in sustainable yield of groundwater resources is awareness of groundwater level. In this study, we have applied artificial neural networks (ANN) and autoregressive integrated moving average (ARIMA) models for groundwater level forecasting to 4 months ahead in Shiraz basin, southwestern Iran. Time series analysis was conducted according to the Box–Jenkins method. Meanwhile, gamma and M-test were considered for determining the optimal input combination and length of training and testing data in the ANN model. The results indicated that performance of multilayer perceptron neural network (4, 14, 1) and ARIMA (2, 1, 2) is satisfactory in the groundwater level forecasting for one month ahead. The performance comparison shows that the ARIMA model performs appreciably better than the ANN.

New formulation for forecasting streamflow: evolutionary polynomial regression vs. extreme learning machine

Abstract Streamflow forecasting is crucial in hydrology and hydraulic engineering since it is capable of optimizing water resource systems or planning future expansion. This study investigated the performances of three different soft computing methods, multilayer perceptron neural network (MLPNN), optimally pruned extreme learning machine (OP-ELM), and evolutionary polynomial regression (EPR) in forecasting daily streamflow. Data from three different stations, Soleyman Tange, Perorich Abad, and Ali Abad located on the Tajan River of Iran were used to estimate the daily streamflow. MLPNN model was employed to determine the optimal input combinations of each station implementing evaluation criteria. In both training and testing stages in the three stations, the results of comparison indicated that the EPR technique would generally perform more efficiently than MLPNN and OP-ELM models. EPR model represented the best performance to simulate the peak flow compared to MLPNN and OP-ELM models while the MLPNN provided significantly under/overestimations. EPR models which include explicit mathematical formulations are recommended for daily streamflow forecasting which is necessary in watershed hydrology management.

Pengembangan teknik penentuan kombinasi penggunaan input-input produksi yang optimal: Studi Kasus KJA di Danau Maninjau, Kabupatan Agam, Sumatera Barat

The objective of this research was to develope the formulation (equation) to estimate the optimal combination of production inputs used on the cage fish culture operation that most rational and harmonious, and consistent either to the theories of research results.

Groundwater Depth Prediction Using Data-Driven Models with the Assistance of Gamma Test

Prediction of the groundwater dynamics via models can help better manage the groundwater resources and guarantee their sustainable use. Three types of data-driven models are built for groundwater depth prediction in the plain of Shijiazhuang, the capital of Hebei Province in North China. The data-driven models include the Power Function Model (PFM), Back-Propagation Artificial Neural Network (BPANN) and Support Vector Machines (SVM) with two kernel functions of linear kernel function (LKF) and radial basis function (RBF). Five classes of factors (including 12 indices) are considered as potential model input variables. The Gamma Test (GT) is adopted in this study to help identify the relative importance of the input indices and tackle the tricky issue of the optimal input combinations for the data-driven models. The established models are evaluated in both fitting and testing procedures based on the root mean squared error (RMSE) and Nash-Sutcliffe efficiency (E) for different input combination schemes. The results show that SVM (RBF) performs the best. It is interesting to find that the natural factors (i.e., precipitation and evaporation) are less relevant to the groundwater depth variations. The methods used in this study have much significance for groundwater depth prediction in areas lacking hydrogeological data.

Measuring Corporate Sustainability and Environmental, Social, and Corporate Governance Value Added

The aim of the paper is to propose a model for measuring sustainable value which would complexly assess environmental, social, and corporate governance contribution to value creation. In the paper the concept of the Sustainable Environmental, Social and Corporate Governance Value Added is presented. The Sustainable Environmental, Social and Corporate Governance Value Added is based on the Sustainable Value Added model and combines weighted environmental, social, and corporate governance indicators with their benchmarks determined by Data Envelopment Analysis. Benchmark values of indicators were set for each company separately and determine the optimal combination of environmental, social, and corporate governance inputs to economic outcomes. The Sustainable Environmental, Social and Corporate Governance Value Added methodology is applied on real-life corporate data and presented through a case study. The value added of most of the selected companies was negative, even though economic indicators of all of them are positive. The Sustainable Environmental, Social and Corporate Governance Value Added is intended to help owners, investors, and other stakeholders in their decision-making and sustainability assessment. The use of environmental, social, and corporate governance factors helps identify the company’s strengths and weaknesses, and provides a more sophisticated insight into it than the one-dimensional methods based on economic performance alone.

Classic Kriging versus Kriging with bootstrapping or conditional simulation: classic Kriging’s robust confidence intervals and optimization

Kriging is a popular method for estimating the global optimum of a simulated system. Kriging approximates the input/output function of the simulation model. Kriging also estimates the variances of the predictions of outputs for input combinations not yet simulated. These predictions and their variances are used by ‘efficient global optimization’ (EGO), to balance local and global search. This article focuses on two related questions: (1) How to select the next combination to be simulated when searching for the global optimum? (2) How to derive confidence intervals for outputs of input combinations not yet simulated? Classic Kriging simply plugs the estimated Kriging parameters into the formula for the predictor variance, so theoretically this variance is biased. This article concludes that practitioners may ignore this bias, because classic Kriging gives acceptable confidence intervals and estimates of the optimal input combination. This conclusion is based on bootstrapping and conditional simulation.

Partial Differential Equation-Based Multivariable Control Input Optimization for Laser-Aided Powder Deposition Processes

This paper deals with the systematic optimization method for multiple input variables (laser irradiation power and scanning speed) in a class of laser-aided powder deposition (LAPD) processes. These processes are normally described by a coupled system of nonlinear partial differential equations (PDEs). To begin with, a desired solid–liquid (S/L) interface geometry is first approximated from a few practical process target parameters that define the desired process properties. Then, the control problem is formulated as one of seeking the optimal combination of process inputs that achieves close tracking of the desired S/L interface in quasi-steady state. The paper details the derivation of the adjoint-based solution for this PDE-constrained multivariable control input optimization problem. The effectiveness of the proposed method is illustrated via a case study on a laser cladding process.

Convex and Monotonic Bootstrapped Kriging

Distribution-free bootstrapping of the replicated responses of a given discreteevent simulation model gives bootstrapped Kriging (Gaussian process) metamodels; we require these metamodels to be either convex or monotonic. To illustrate monotonic Kriging, we use an M/M/1 queueing simulation with as output either the mean or the 90% quantile of the transient-state waiting times, and as input the traffic rate. In this example, monotonic bootstrapped Kriging enables better sensitivity analysis than classic Kriging; i.e., bootstrapping gives lower MSE and confidence intervals with higher coverage and the same length. To illustrate convex Kriging, we start with simulation optimization of an (s, S) inventory model, but we next switch to a Monte Carlo experiment with a second-order polynomial inspired by this inventory simulation. We could not find truly convex Kriging metamodels, either classic or bootstrapped; nevertheless, our bootstrapped \nearly convex' Kriging does give a confidence interval for the optimal input combination

On Measurement of Efficiency of Cobb-Douglas Production Function with Additive and Multiplicative Errors

In developing counties, efficiency of economic development has determined by the analysis of industrial production. An examination of the characteristic of industrial sector is an essential aspect of growth studies. The most of the developed countries are highly industrialized as they brief “The more industrialization, the more development”. For proper industrialization and industrial development we have to study industrial input-output relationship that leads to production analysis. For a number of reasons econometrician’s belief that industrial production is the most important component of economic development because, if domestic industrial production increases, GDP will increase, if elasticity of labor is higher, implement rates will increase and investment will increase if elasticity of capital is higher. In this regard, this paper should be helpful in suggesting the most suitable Cobb-Douglas production function to forecast the production process for some selected manufacturing industries of developing countries like Bangladesh. This paper choose the appropriate Cobb-Douglas function which gives optimal combination of inputs, that is, the combination that enables it to produce the desired level of output with minimum cost and hence with maximum profitability for some selected manufacturing industries of Bangladesh over the period 1978-79 to 2011-2012. The estimated results shows that the estimates of both capital and labor elasticity of Cobb-Douglas production function with additive errors are more efficient than those estimates of Cobb-Douglas production function with multiplicative errors.

The Supply Perspective of Economic Growth During the Last Decades

Abstract The economic crises of the years 2008-2009 stopped the economic growth of many economies and highlighted the problem associated with the method of financing and the nature of the economic growth of the country. An increasing problem is to find the optimal combination of inputs that would ensure efficiency and sustainable growth in a world of strong limitations of available resources. The aim of this paper is to compare the economic growth in selected countries of EU – Italy and Great Britain. We used supply perspective of economic growth calculation based on the neoclassical production function (the connection between Cobb-Douglas‘s production function and productivity).

A real-time forecasting model for the spatial distribution of typhoon rainfall

• We propose a model to yield 1- to 6-h ahead forecasts of hourly rainfall. • A proposal for spatial characteristics of the rainfall process is presented. • We identify the optimal input combinations for each lead time. • The model yields accurate forecasts of the spatial distribution of rainfall. Accurate forecasts of hourly rainfall are necessary for early warning systems during typhoons. In this paper, a typhoon rainfall forecasting model is proposed to yield 1- to 6-h ahead forecasts of hourly rainfall. First, an input optimization step integrating multi-objective genetic algorithm (MOGA) with support vector machine (SVM) is developed to identify the optimal input combinations. Second, based on the results of the first step, the forecasted rainfall from each station is used to obtain the spatial characteristics of the rainfall process is presented. An actual application to Tsengwen river basin is conducted to demonstrate the advantage of the proposed model. The results clearly indicate that the proposed model effectively improves the forecasting performance and decreases the negative impact of increasing forecast lead time. The optimal input combinations can be obtained from the proposed model for different stations with different geographical conditions. In addition, the proposed model is capable of producing more acceptable the results of rainfall maps than other model. In conclusion, the proposed modeling technique is useful to improve the hourly typhoon rainfall forecasting and is expected to be helpful to support disaster warning systems.

Bootstrapping and Conditional Simulation in Kriging: Better Confidence Intervals and Optimization?

Kriging is a popular method for estimating the global optimum of a simulated system. Kriging approximates the input/output function of the simulation model. Kriging also estimates the variances of the predictions of outputs for input combinations not yet simulated. These predictions and their variances are used by efficient global optimization(EGO), to balance local and global search. This article focuses on two related questions: (1) How to select the next combination to be simulated when searching for the global optimum? (2) How to derive confidence intervals for outputs of input combinations not yet simulated? Classic Kriging simply plugs the estimated Kriging parameters into the formula for the predictor variance, so theoretically this variance is biased. This article concludes that practitioners may ignore this bias, because classic Kriging gives acceptable confidence intervals and estimates of the optimal input combination. This conclusion is based on bootstrapping and conditional simulation.

20-Over Versus 50-Over Cricket

Using 2008-2009 data, we identify winning input combinations for the game of cricket in two different formats: 50-over one-day internationals and 20-over games from Twenty20 internationals and the Indian Premier League. We find that attacking batting and bowling are the best determinants of the winning probability in both formats in an international setting despite their 30-over difference. Interestingly, attacking batting and defensive bowling are the optimal input combinations in the league version of the 20-over format. Finally, we speculate about their implications for the future of cricket, especially for the popularity of formats, choices of players, and player development.

Estimation of daily dew point temperature using genetic programming and neural networks approaches

The present study investigates the ability of two different artificial neural network (ANN) models and gene expression programming (GEP) technique for estimating daily dew point temperature by using recorded weather data. The weather data used consist of 8 years of daily records of air temperature, wind speed, relative humidity, atmospheric pressure, incoming solar radiation and dew point temperature from two weather stations (Seoul and Incheon, in the Republic of Korea). Two different data management scenarios are applied in this paper. In the first scenario, weather data obtained from each station are used to estimate Tdew at the same station (at-station approach). In the second scenario, the ANN and GEP models are used for estimating dew point temperature of each station by using the data of the other station (cross-station application), through the optimal input combinations of the first scenario. Comparison of the results reveals that the GEP model surpasses ANN in estimating daily dew point temperature values.