Revenue management and dynamic pricing are concepts that have immense possibilities for application in the energy sector. Both can be considered as demand-side management tools that can facilitate the offering of different prices at different demand levels. This paper studies literature on various topics related to the dynamic pricing of electricity and lists future research avenues in pricing policies, consumers' willingness to pay and market segmentation in this field. Demand and price forecasting play an important role in determining prices and scheduling load in dynamic pricing environments. This allows different forms of dynamic pricing policies to different markets and customers depending on customers' willingness to pay. Consumers' willingness to pay for electricity services is also necessary in setting price limits depending on the demand and demand response curve. Market segmentation can enhance the effects of such pricing schemes. Appropriate scheduling of electrical load enhances the consumer response to dynamic tariffs.

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Mathematical programming techniques were used in the steel industry as early as 1958, and many applications of optimization in steel production have been reported since then. In this survey, we summarize published applications in the largest steel plants by type, including national steel planning, product-mix optimization, blending, scheduling, set covering, and cutting stock.Steel, Applications, Mathematical Programming, Optimization

Purpose The purpose of this paper is to estimate the relative efficiencies of banks of the Indian domestic banking sector by employing various models of data envelopment analysis (DEA) using the panel data of the recent decade (2008–2017). The paper provides a comparative analysis of these models based on the efficiency outputs. It compares the performance of banks based on their ownership and sizes and studies the decade-long trend of productivity using Malmquist indices. Design/methodology/approach This paper estimates overall technical, pure technical and scale efficiencies of 21 public sector banks and 17 private banks. It compares the descriptive statistics of efficiency estimates found out through 18 different DEA models and compares them using two non-parametric statistical tests. It studies the difference in efficiencies based on ownership and size by applying the same statistical tests. It employs the Malmquist index method to study the technological and technical progress in the banks’ productivity over the decade of FY 2008–FY 2017. Findings During FY 2016–2017, only 9 out of 38 banks were overall technically efficient with the whole sample having a mean overall technical inefficiency of 5 percent with scale inefficiency contributing more than pure technical inefficiency. The comparative study ascertains that private sector and public sector banks (PSBs) possess efficiencies that are similar based on super-efficiency slack-based model – variable returns to scale and non-oriented, a model that the authors argue to be the most suitable for the real-life business banking scenarios whereas the private sector banks possess better efficiency than the PSBs. The Malmquist indices prove that private sector banks have a higher increase in productivity based on both technological progress and efficiency improvements whereas PSBs had a loss of efficiency and comparatively less improvement in technology. Research limitations/implications This study has a limitation of choosing a single model of inputs and outputs. Improved insights can be drawn by employing more models based on different inputs and outputs. Further, relevance of each input and output can be examined using a regression-based feedback mechanism (Ouenniche and Carrales, 2018). The influence of environmental factors on the efficiencies can be studied using second-stage regression models and the relationship between efficiency scores and financial ratios can be examined. Originality/value This study is based on the panel data of the recent decade (2008–2017) and provides insights into the efficiency scenario of the Indian banking industry and how it changed over the past decade, to the leadership of banks, the banking regulators and the policy makers. The comparative analysis of DEA models based on a sample of Indian banks is first of its kind in the Indian context and helps the researchers to select an appropriate model and delve into further research on the same.

Artificial Neural Network (ANN) has been shown to be an efficient tool for non-parametric modelling of data in a variety of different contexts where the output is a non-linear function of the inputs. These include business forecasting, credit scoring, bond rating, business failure prediction, medicine, pattern recognition and image processing. A large number of studies have been reported in literature with reference to the use of ANN in modelling stock prices in western countries. However, not much work along these lines has been reported in the Indian context. In this article we discuss the modelling of the Indian stock market (price index) data using ANN. We study the efficacy of ANN in modelling the Bombay Stock Exchange (BSE) SENSEX weekly closing values. We develop two networks with three hidden layers for the purpose of this study which are denoted as ANN1 and ANN2. ANN1 takes as its inputs the weekly closing value, 52-week moving average of the weekly closing SENSEX values, 5-week moving average of the same, and the 10-week Oscillator for the past 200 weeks. ANN2 takes as its inputs the weekly closing value, 52-week moving average of the weekly closing SENSEX values, 5-week moving average of the same and the 5-week volatility for the past 200 weeks. Both the neural networks are trained using data for 250 weeks starting January 1997. To assess the performance of the networks we used them to predict the weekly closing SENSEX values for the two-year period beginning January 2002. The root mean square error (RMSE) and mean absolute error (MAE) are chosen as indicators of performance of the networks. ANN1 achieved an RMSE of 4.82 per cent and MAE of 3.93 per cent while ANN2 achieved an RMSE of 6.87 per cent and MAE of 5.52 per cent.

In this paper, a 1-D thermal-hydraulic model, THRUST, is developed to simulate and analyze the CANDU supercritical water reactor (SCWR) from the thermodynamic point of view without considering the effect of neutronic coupling. THRUST, where a characteristic-based finite difference scheme is used, is validated against the available numerical results. The model is, then, used for the analysis of the CANDU SCWR with a primary focus to determine the conditions for potential density wave oscillations. Extensive numerical studies are performed to obtain the marginal stability boundary in the operating regime of the reactor. The effect of various parameters, such as, mass flow rate, operating pressure, axial heat flux profile, local pressure drop coefficient, and friction factor, on the stability thresholds of the reactor have been investigated.

With increasing liquidity of the Indian sovereign debt market since 1997, it has become possible to estimate the term structure in India. However, the market is characterised by several frictions that cause individual securities to be priced differently from the ‘average’ pricing in the market. In such a scenario, traditional estimation procedures like ordinary least squares using various functional forms do not perform well. In this paper, we find that mean absolute deviation is a better estimation procedure in illiquid markets than the ordinary least square. We further discover a novel liquidity weighted objective function for parameter estimation. We model the liquidity function using the exponential and hyperbolic tangent functions and suggest the most robust model for estimating term structures in India.