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This paper aims to present an integrated framework for hotel revenue room maximization. The revenue management (RM) model presented in this work treats the shortcomings in existing systems. In particular, it extends existing optimization techniques for hotel revenue management to address group reservations and uses “forecasted demand” arrivals generated from the real data.

The proposed forecasting module attempts to model the hotel reservation process from first principles. In particular, it models hotel arrivals as an interrelated process of stochastic parameters like reservations, cancellations, duration of stay, no shows, seasonality, trend, etc. and simulates forward in time the actual process of reservations to obtain the forecast. On the other hand, the proposed optimization module extends existing optimization techniques for hotel revenue management to address group reservations, while including integrality constraints and using “forecasted demand” arrivals generated from the data. The optimization model is based on large‐scale integer programming model to optimize decision rules for accepting reservations.

A case study based on three different sets of reservation records of simulated hotel data was conducted to test the operation of the system on real data. Results showed that the model is able to generate effective recommendations to maximize revenue.

The main value of this paper is that it presents an integrated framework for hotel room revenue maximization. The novelty introduced in this approach is that it is based on an advanced room demand forecast model that simulated the reservation process from its first principles and produces demand scenarios that are used by an optimization model to generate proper recommendations.

Electrical characteristics of transformer oil (TO) have been studied during normal and thermal aging conditions. In this paper, breakdown voltage (BDV), partial discharge (PD), heat transfer results and the physical mechanisms considering the impact of varying the diameter of Al₂O₃ nanoparticles (NPs) have been investigated. Different quantities of the two sizes of Al₂O₃ were added to the oil using a two-step method to determine the positive effect of NPs on the electrical and thermal properties of TO. Finally, the physical mechanisms related to the obtained experimental results have been performed.

The implementation of nanoparticles in this paper was provided by US Research Nanomaterials, Inc., USA. The provided Al₂O₃ NPs have an average particle size of 20–80 nm and a specific surface area of 138 and 58 m²/g, respectively, which have a purity of over 99%. Thermal aging has been done. The IEC 60156 standard has been implemented to calculate the BDV, and a 500-mL volume test cell (Apar TO 1020) has been used. PD test is performed according to Standard IEC 60343, and a JDEVS-PDMA 300 device was used for this test.

BDV tests indicate that 20 nm Al₂O₃ is more effective at improving BDV than 80 nm Al₂O₃, with an improvement of 113% compared to 99% for the latter. The analysis of Weibull probability at BDV indicates that 20 nm Al₂O₃ performs better, with improvements of 141%, 125% and 112% at probabilities of 1, 10 and 50%, respectively. The results of the PD tests using the PDPR pattern also show that 20 nm Al₂O₃ is superior. For the heat transfer test, 0.05 g/L of both diameters were used to ensure fair conditions, and again, the advantage was with 20 nm Al₂O₃ (23% vs 18%).

The effect of Al₂O₃ NP diameter (20 and 80 nm) on various properties of virgin and aged TO has been investigated experimentally in this paper to examine the effect of proposed NP on electrical improvement of TO.

This study aims to propose and empirically examine the simultaneous complementary mediating role of bank branch collective human capital and justice climate between implemented high-performance work system (HPWS) and bank branch performance in the banking sector.

Data were collected at three different intervals of time between March 2022 to July 2022 from a final sample of 323 branch managers and 1,369 employees of commercial banks operating in Pakistan. Partial least square structural equation modeling was used to test the theoretical model proposed by this study.

Study results revealed that collective human capital and justice climate simultaneously mediate the relationship between implemented HPWS and branch performance.

The study contributes to the strategic HRM theory by proposing the complementary mediating roles of human capital and organizational justice to reap the benefits of implementing HPWS for improving branch-level performance. The managers should focus on developing and exploiting the knowledge, skills and experiences (human capital) of branch employees and improve their collective perceptions of justice to reap the benefits of HPWS for enhancing branch-level performance.

Drawing upon the resource-based view of the firm and organizational justice theory, this novel study examines the simultaneous and complementary mediating effects of collective human capital and justice climate between implemented HPWS and branch performance relationships at the branch-level analysis.

Bankruptcy is a financial failure of a business or an organization. Different kinds of bankruptcy prediction techniques are proposed to predict it. But, they are restricted as techniques in predicting the bankruptcy and not addressing the associated activities like acquiring the suitable data and delivering the results to the user after processing it. This situation demands to look for a comprehensive solution for predicting bankruptcy with intelligence. The paper aims to discuss these issues.

To model Business Intelligence (BI) solution for BP the concept of reference model is used. A Reference Model for Business Intelligence to Predict Bankruptcy (RMBIPB) is designed by applying unit operations as hierarchical structure with abstract components. The layers of RMBIPB are constructed from the hierarchical structure of the model and the components, which are part of the reference model. In this model, each layer is designed based on the functional requirements of the Business Intelligence System (BIS).

This reference model exhibits the non functional software qualities intended for the appropriate unit operations. It has flexible design in which techniques are selected with minimal effort to conduct the bankruptcy prediction. The same reference model for another domain can be implemented with different kinds of techniques for bankruptcy prediction.

This model is designed using unit operations and the software qualities exhibited by RMBIPB are limited by unit operations. The data set which is applied in RMBIPB is limited to Indian banks.

A comprehensive bankruptcy prediction model using BI with customized reporting.