An integrated fuzzy mathematical model and principal component analysis algorithm for forecasting uncertain trends of electricity consumption

This paper introduces an integrated algorithm for forecasting electricity consumption (EL) based on fuzzy regression, time series and principal component analysis (PCA) in uncertain markets such as Iran. The algorithm is examined by mean absolute percentage error, analysis of variance (ANOVA) and Duncan Multiple Range Test. PCA is used to identify the input variables for the fuzzy regression and time series models. Monthly EL in Iran is used to show the superiority of the algorithm. Moreover, it is shown that the selected fuzzy regression model has better estimated values for total EL than time series. The algorithm provides as good results as intelligent methods. However, it is shown that the algorithm does not require utilization of preprocessing methods but genetic algorithm, artificial neural network and fuzzy inference system require preprocessing which could be a cumbersome task to deal with ambiguous data. The unique features of the proposed algorithm are three fold. First, two type of fuzzy regressions with and without preprocessed data are prescribed by the algorithm in order to minimize the bias. Second, it uses PCA approach instead of trial and error method for selecting the most important input variables. Third, ANOVA is used to statistically compare fuzzy regression and time series with actual data.     

Can Corporate Divestiture Activities Lead to Better Corporate Social Performance?

Journal of Business Ethics - Prior research showed that corporate divestitures could help firms restore their strategic (versus financial) controls and long-term focus. This suggests that...     

Machine-Part Cell Formation Problem Using a Group Based League Championship Algorithm

This article proposes a new algorithm for grouping problems that is a grouping version of league championship algorithm (GLCA). We compare the performance of GLCA with several well-known algorithms published in the present literature and select a set of 20 most widely used benchmarks of cell formation sample problems posing as a grouping problem. We used a truncated geometric algorithm to find the number of initial cells. Our computations reveal that GLCA can reach the best-known solution for 17 of the 20 benchmark problems, and improve the solution of three others with a 1.4% average gap.     

A resilience-based model for performance evaluation of information systems: the case of a gas company

Information systems (IS) are strongly influenced by changes in new technology and should react swiftly in response to external conditions. Resilience engineering is a new method that can enable these systems to absorb changes. In this study, a new framework is presented for performance evaluation of IS that includes DeLone and McLean’s factors of success in addition to resilience. Hence, this study is an attempt to evaluate the impact of resilience on IS by the proposed model in Iranian Gas Engineering and Development Company via the data obtained from questionnaires and Fuzzy Data Envelopment Analysis (FDEA) approach. First, FDEA model with α-cut = 0.05 was identified as the most suitable model to this application by performing all Banker, Charnes and Cooper and Charnes, Cooper and Rhodes models of and FDEA and selecting the appropriate model based on maximum mean efficiency. Then, the factors were ranked based on the results of sensitivity analysis, which showed resilience had a significantly higher impact on the proposed model relative to other factors. The results of this study were then verified by conducting the related ANOVA test. This is the first study that examines the impact of resilience on IS by statistical and mathematical approaches.     

A Flexible Neuro‐Fuzzy Approach for Improvement of Seasonal Housing Price Estimation in Uncertain and Non‐Linear Environments

Changes in housing price affect both individuals and government since they have substantial influence on the socio‐economic conditions. Valuations of housing are necessary in order to assess the benefits and liabilities in housing sector. This study presents a flexible meta‐modelling approach for improvement of housing price estimation in ambiguous and complex environments. It is composed of artificial neural network (ANN) and fuzzy linear regression (FLR). Seven FLR models are considered to cover latest approaches and viewpoints. Also, ANN is applied to data sets. The preferred FLR model is selected via mean absolute percentage of error (MAPE) for further considerations, and then the preferred FLR model and the best structure of ANN are applied to the data set. Finally, the preferred model is selected based on MAPE. The intelligent approach of this study is applied for estimation and forecasting housing price in Iran. The housing price in Iran mainly is based on eight economic indices including currency, oil income, general index, house service pricing index, rate of informal market, gross domestic production in basic price, added value of oil group and construction materials price. FLR is identified as the preferred model with lowest MAPE for housing price forecasting in Iran. This shows that the housing market of Iran is associated with severe environmental fuzziness and ambiguity. This is the first study that introduces a flexible neuro‐fuzzy approach for improved estimation and forecasting of housing price in noisy, complex and uncertain environments.     

Modelling the Impact of Environmental and Organizational Determinants on Green Supply Chain Innovation and Performance

Both customers and suppliers are becoming increasingly concerned about environmental issues in modern food chains. A firm’s decision to implement green supply chain management is based on the social objectives of the firm and its management, its desire to pursue corporate social responsibility, its relationships with channel partners, and environmental determinants such as government legislation. The speed at which green supply chain management is implemented within an organization depends on its agility and its ability to facilitate innovation. Innovation may take the form of new product development or new process development, including the introduction of environmental management systems and total quality management in both production and purchasing. This article presents a conceptual model to explain how the various theoretical constructs are related and how innovation effects green supply chain management and performance.     

Performance assessment and optimisation of a large information system by combined customer relationship management and resilience engineering: a mathematical programming approach

ISs and ITs play a critical role in large complex gas corporations. Many factors such as human, organisational and environmental factors affect IS in an organisation. Therefore, investigating ISs success is considered to be a complex problem. Also, because of the competitive business environment and the high amount of information flow in organisations, new issues like resilient ISs and successful customer relationship management (CRM) have emerged. A resilient IS will provide sustainable delivery of information to internal and external customers. This paper presents an integrated approach to enhance and optimise the performance of each component of a large IS based on CRM and resilience engineering (RE) in a gas company. The enhancement of the performance can help ISs to perform business tasks efficiently. The data are collected from standard questionnaires. It is then analysed by data envelopment analysis by selecting the optimal mathematical programming approach. The selected model is validated and verified by principle component analysis method. Finally, CRM and RE factors are identified as influential factors through sensitivity analysis for this particular case study. To the best of our knowledge, this is the first study for performance assessment and optimisation of large IS by combined RE and CRM.     

Linear and nonlinear Granger-causality between short-term and long-term interest rates during business cycles

This paper is about the causal relationship between short-term and long-term interest rates in the US and Canada. To that end, we apply a linear Granger causality test introduced by Toda and Yamamoto (1995 Toda, H. Y., and T. Yamamoto. 1995. “Statistical Inference in Vector Autoregressions with Possibly Integrated Processes.” Journal of Econometrics 66 (1–2): 225–250. doi:10.1016/0304-4076(94)01616-8.[Crossref], [Web of Science ®] , [Google Scholar]) and the nonlinear Granger causality test of Diks and Panchenko (2006 Diks, C., and V. Panchenko. 2006. “A New Statistic and Practical Guidelines for Nonparametric Granger Causality Testing.” Journal of Economic Dynamics and Control 30 (9–10): 1647–1669. doi:10.1016/j.jedc.2005.08.008.[Crossref], [Web of Science ®] , [Google Scholar]). By combining linear causality effects with the nonlinear ones, it is seen that the most common Granger causality direction between short-term and long-term interest rates is a bidirectional one. We also find that nonlinear Granger causality can be found where no linear causality had been uncovered. Moreover, our findings show that during recent business cycles, the federal funds rate (in the US) and the overnight rate (in Canada) still Granger-cause long-term interest rates significantly.     

Performance optimization of an online retailer by a unique online resilience engineering algorithm

Online shopping has become more attractive and competitive in electronic markets. Resilience engineering (RE) can help such systems divert to the normal state in case of encountering unexpected events. This study presents a unique online resilience engineering (ORE) approach for online shopping systems and customer service performance. Moreover, this study presents a new ORE algorithm for the performance optimisation of an actual online shopping system. The data are collected by standard questionnaires from both expert employees and customers. The problem is then formulated mathematically using data envelopment analysis (DEA). The results show that the design process which is based on ORE is more efficient than the conventional design approach. Moreover, on-time delivery is the most important factor from the personnel’s perspective. In addition, according to customers’ view, trust, security and good quality assurance are the most effective factors during transactions. This is the first study that introduces ORE for electronic markets. Second, it investigates impact of RE on online shopping through DEA and statistical methods. Third, a practical approach is employed in this study and it may be used for similar online shops. Fourth, the results are verified and validated through complete sensitivity analysis.