An integrated fuzzy multi-attribute decision-making model for employees' performance appraisal

Evaluating and ranking the employees working in organisations are challenging tasks involving several factors. Each employee achieves certain skill levels in various factors and the resulting information can be overwhelming. This article demonstrates how an integrated tool like fuzzy multi-attribute decision making (FMADM), with fuzzy analytic hierarchy process (FAHP), fuzzy quality function deployment (FQFD), is applied as a fair evaluating and sorting tool to support the performance appraisal (PA) system. The fuzzy linguistic approach has the advantage of reducing distortion and losing of information. FMADM focuses on the best practices of employees for the purpose of improving overall performance. Unlike traditional PAs, FMADM searches for the highly skilled employees who will serve as peers. The FMADM process identifies employees who require improvements in certain factor(s), and the magnitude of improvements required. This study supports the ideas that rating formats need re-examination as an alternative to traditional rating methods. Earlier adopted methods have seldom identified and quantified the individual factors for improvement, whereas FMADM could overcome these shortfalls. Based on the results of FMADM, the improvement of employees' performance is possible by way of providing training, talent enhancement and further qualification wherever required.     

基于主成分分析法的财务风险评价模型

本文在分析了现有财务风险研究理论的基础上引入了主成分分析法的基本原理,构建了主成分分析模型并进行了相关实证研究,验证了模型的科学性和合理性.     

An unobserved component model for multi-rate forecasting of telephone call demand: the design of a forecasting support system

An Unobserved Components (UC) Model based on an enhanced version of the Dynamic Harmonic Regression model, including new multi-rate and modulated cycle procedures, is used to develop a customised package for forecasting and signal extraction applied to hourly telephone call numbers made to Barclaycard plc. service centres, with a forecasting horizon of up to several weeks in advance. The paper outlines both the methodological and algorithmic aspects of the modelling, forecasting and signal extraction procedures, including the design and implementation of forecasting support software with a specially designed Graphical User Interface within the ^® computing environment. The forecasting performance is evaluated comprehensively in comparison with the well-known seasonal ARIMA approach.     

Identification of backward district in India by applying the principal component analysis and fuzzy approach: A census based study

India is a large country with several axes of inequality. Among the various axes of inequality, the regional disparity is the prominent one. This study aims to find out the most backward districts in India with the help of the latest available statistics data. For this purpose, 25 indicators are selected and these are further categorized into six dimensions based on their characteristics. The principal component analysis is applied in R Studio for the data dimension reduction and the calculation of weight. Later on, the fuzzy approach is taken into consideration for integration purposes. The final output shows that 80 districts that are located mainly in Madhya Pradesh, Jharkhand, Odisha, and Bihar are the most backward. Contrary to this, 73 districts that are located mainly in the western coastal plain and north-west India are the least backward. Besides, this study also observes that development patterns in India are not uniform in character. Most of the developed districts are concentrated in few pockets. The suitability of this study is analyzed in comparison to other studies. The result shows comparatively better acceptability of this model in comparison to other studies.     

集成式零件尺寸数据采集与分析装置研究

文章介绍了一个集成式尺寸数据采集与分析装置的设计原理和实现方法。该装置采用容栅式数显量具作为尺寸数据采集的前端,C51单片机作为数据采集和处理的中间环节,PC机作为数据分析处理的末端,能完成成批零件常规尺寸的采集、存储和误差统计分析。     

An accurate and fully-automated ensemble model for weekly time series forecasting

Many businesses and industries require accurate forecasts for weekly time series nowadays. However, the forecasting literature does not currently provide easy-to-use, automatic, reproducible and accurate approaches dedicated to this task. We propose a forecasting method in this domain to fill this gap, leveraging state-of-the-art forecasting techniques, such as forecast combination, meta-learning, and global modelling. We consider different meta-learning architectures, algorithms, and base model pools. Based on all considered model variants, we propose to use a stacking approach with lasso regression which optimally combines the forecasts of four base models: a global Recurrent Neural Network (RNN) model, Theta, Trigonometric Box–Cox ARMA Trend Seasonal (TBATS), and Dynamic Harmonic Regression ARIMA (DHR-ARIMA), as it shows the overall best performance across seven experimental weekly datasets on four evaluation metrics. Our proposed method also consistently outperforms a set of benchmarks and state-of-the-art weekly forecasting models by a considerable margin with statistical significance. Our method can produce the most accurate forecasts, in terms of mean sMAPE, for the M4 weekly dataset among all benchmarks and all original competition participants.     

Enhancing capacity planning through forecasting: An integrated tool for maintenance of complex product systems

Despite the extensive amount of data generated and stored during the maintenance capacity planning process, Maintenance, Repair, and Overhaul (MRO) organizations have yet to explore their full potential in forecasting the required capacity to face future and unprecedented maintenance interventions. This paper explores the integration of time series forecasting capabilities in a tool for maintenance capacity planning of complex product systems (CoPS), intended to value data that is routinely generated and stored, but often disregarded by MROs. State space formulations with multiplicative errors for the simple exponential smoothing (SES), Holt’s linear method (HLM), additive Holt-Winters (AHW), and multiplicative Holt-Winters (MHW) are assessed using real data, comprised of 171 maintenance projects collected from a major Portuguese aircraft MRO. A state space formulation of the MHW, selected using the bias-corrected Akaike information criterion (AICc), is integrated in a Decision Support System (DSS) for capacity planning with probabilistic inference capabilities and used to forecast the workload probability distribution of a future and unprecedent maintenance intervention. The developed tool is validated by comparing forecasted values with workloads of a particular maintenance intervention and with a model simulating current forecasting practices employed by MROs.     

An integrated model of downtown parking and traffic congestion

This paper is the first to look at cruising for parking from an economic perspective. We present a downtown parking model that integrates traffic congestion and saturated on-street parking; the stock of cars cruising for parking adds to traffic congestion. Two major results emerge from the model, one of which is robust. The robust one is that, whether or not the amount of on-street parking is optimal, it is efficient to raise the on-street parking fee to the point where cruising for parking is eliminated without parking becoming unsaturated. The other is that, if the parking fee is fixed at a sub-optimal level, it is second-best optimal to increase the amount of curbside allocated to parking until cruising for parking is eliminated without parking becoming unsaturated     

An integrated model of downtown parking and traffic congestion

This paper is the first to look at cruising for parking from an economic perspective. We present a downtown parking model that integrates traffic congestion and saturated on-street parking; the stock of cars cruising for parking adds to traffic congestion. Two major results emerge from the model, one of which is robust. The robust one is that, whether or not the amount of on-street parking is optimal, it is efficient to raise the on-street parking fee to the point where cruising for parking is eliminated without parking becoming unsaturated. The other is that, if the parking fee is fixed at a sub-optimal level, it is second-best optimal to increase the amount of curbside allocated to parking until cruising for parking is eliminated without parking becoming unsaturated     

K-means clustering of item characteristic curves and item information curves via functional principal component analysis

Quality & Quantity - The assessment of students’ performances and learning skills plays a key role in the educational context. Common tools for analyzing test data are item response...     

An empirical analysis of neural network memory structures for basin water quality forecasting

This research investigates the cumulative multi-period forecast accuracy of a diverse set of potential forecasting models for basin water quality management. The models are characterized by their short-term (memory by delay or memory by feedback) and long-term (linear or nonlinear) memory structures. The experiments are conducted as a series of forecast cycles, with a rolling origin of a constant fit size. The models are recalibrated with each cycle, and out-of-sample forecasts are generated for a five-period forecast horizon. The results confirm that the JENN and GMNN neural network models are generally more accurate than competitors for cumulative multi-period basin water quality prediction. For example, the JENN and GMNN models reduce the cumulative five-period forecast errors by as much as 50%, relative to exponential smoothing and ARIMA models. These findings are significant in view of the increasing social and economic consequences of basin water quality management, and have the potential for extention to other scientific, medical, and business applications where multi-period predictions of nonlinear time series are critical.     

Construct a composite indicator based on integrating Common Weight Data Envelopment Analysis and principal component analysis models: An application for finding development degree of provinces in Iran

Balanced development of regions requires the fair distribution of facilities and services. Hence, it is necessary to find and estimate the development degree of regions for policy makers. This paper presents an integrated Common Weight Data Envelopment Analysis (CWDEA) – Principal Component Analysis (PCA) model to find out the development degree of provinces in Iran. First, 131 suitable indicators are selected and then, the indicators are classified in fourteen different classes. In classical DEA model, each Decision Making Unit (DMU) is free to set its weights to reach the efficient frontier. In this paper, to restrict flexibility in indicator weights, development degree of provinces in each class is calculated using CWDEA model. Since, the proposed CWDEA model is not capable of fully ranking of provinces with all indicators, hence, the development degrees generated by CWDEA model are considered as indicators of PCA and the final ranks are obtained using PCA model. The results of proposed CWDEA-PCA model show that Yazd, Semnan and Bushehr are top three provinces in Iran.     

An operational mathematical programming model for the planning of economic activities in relation to the environment

The model described in this article is a mathematical programming model to assess the economic implications of different industrialization policies. It consists of input-output relationships connected with an environmental quality model converting emission data into expected distribution over space.     

An integrative strategy for Venezuela's electricity sector (VES), from an analysis of stakeholder perspectives

This work aims to determine the most important aspects of an integrative strategy to end the stagnation of Venezuela's energy sector (VES). The first step is to extract the various stakeholders' perspectives on policies needed to improve VES. Q methodology combines qualitative and quantitative approaches to learn the subjective opinions of a group of individuals on a specific topic to determine the different perspectives at hand. Using Q methodology we analyzed the preferences of VES-involved stakeholders regarding the structure of the industry, its services, the infrastructure framework, and, finally, energy governance design. Analyzing commonalities and disagreements between perspectives resulted in three perspectives or mindsets: i) a partial vertical restructuring of the industry, ii) the development of hydroelectricity and large-scale renewable energies, and iii) the improvement of control mechanisms to increase the sector's efficiency.     

我国房地产上市公司竞争力综合分析——基于主成分分析的实证研究

本文选取较为全面的竞争力指标对我国62家房地产上市公司的竞争力进行定量评价并得出相关结论,揭示当前及未来我国房地产企业竞争的格局,在此基础上提出建议,为相关企业提供有益的借鉴。     

Increase of electricity price and energy efficiency: analysis using the macroeconomic interindustry model of Taiwan

From 2012 to 2013, the price of electricity in Taiwan increased by 19.78%. This large increase forced producers to invest in improving electricity efficiency. In this paper, the macroeconomic interindustry model of Taiwan (MEIT) is developed to study the economic effects of high electricity prices and consequential improvement in energy efficiency. MEIT describes the structure of 47 industries, from both real and price-income approaches. To resolve inconsistent data, RAS is employed. A technical model is also integrated to consider technical feasibility, which offsets the usual shortcomings of technological analysis in an economic model. The iron and steel industry is taken as a case study. Estimated results indicate that higher electricity prices negatively affect Taiwan’s economy. However, improving energy efficiency can reduce some of this effect.     

Restricted forecasting with VAR models: An analysis of a test for joint compatibility between restrictions and forecasts

A restricted forecasting compatibility test for Vector Autoregressive Error Correction models is analyzed in this work. It is shown that a variance–covariance matrix associated with the restrictions can be used to cancel out model dynamics and interactions between restrictions. This allows us to interpret the joint compatibility test as a composition of the corresponding single restriction compatibility tests. These tests are useful for appreciating the contribution of each and every restriction to the joint compatibility between the whole set of restrictions and the unrestricted forecasts. An estimated process adjustment for the test is derived and the resulting feasible joint compatibility test turns out to have better performance than the original one. An empirical illustration of the usefulness of the proposed test makes use of Mexican macroeconomic data and the targets proposed by the Mexican Government for the year 2003.     

The first algorithm for linear programming: An analysis of Kantorovich's method

An analysis is given of Kantorovich's method of resolving multipliers. It is shown that the method is equivalent to a parametric method but that it is also equivalent with the simplex method with a special rule for the choice of the new basic variable.     

知识管理项目绩效的一种评价方法

文章在以往的研究基础上,构建了一套知识管理项目绩效评估指标,并针对该评价体系存在的主观判断模糊性和信息不完全的特点,运用模糊数学和灰色系统理论,提出了知识管理项目绩效的灰色模糊综合评价模型,并结合具体算例,证明了该模型的有效性。     

An alternative model for the analysis of variance with random effects

The standard model for the analysis of variance with random effects implies, for the case of two independent variables, that single effects must be tested not against the error, but against the interaction mean squares. This causes, in comparison with the fixed effects AV, a considerable loss of test power, particularly for the 2 × 2 table. An alternative modelling of the interaction effect is proposed which completely avoids the loss of power.