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This study aims to deal with the numerical investigation of ferrofluid flow and heat transfer inside a right-angle triangular cavity in the presence of a magnetic field. The vertical wall is partially heated, whereas other walls are kept cold. The effects of thermal radiation are included in the analysis. The governing equations including continuity, momentum and energy equations are converted to nondimensional form using viable variables.

Finite element method (FEM)-based simulations are performed using finite element approach to investigate the effects of the volume fraction of ferroparticles (Fe₃O₄), the length of the heating element and the dimensionless numbers including Rayleigh and Hartmann numbers on the streamlines, isotherms and Nusselt number.

It is demonstrated that both horizontal and vertical velocity components increase with the length of the heating element, whereas the dimensionless temperature decreases the heating domain. It is observed that an increase of 10% in the volume fraction of ferroparticles increases Nusselt number more than 12%, and 20% increase in the volume fraction of ferroparticles increases more than 30%, depending upon the length of the heating element.

This is a new study showing the significance of the magnetic nanoparticles for the enhancement of heat transfer rate in a triangular cavity.

This study aims to investigate the irreversibility associated with the Fe3O4–Co/kerosene hybrid-nanofluid past a wedge with nonlinear radiation and heat source.

This study reports the numerical analysis of the hybrid nanofluid model under the implications of the heat source and magnetic field over a static and moving wedge with slips. The second law of thermodynamics is applied with nonlinear thermal radiation. The system that comprises differential equations of partial derivatives is remodeled into the system of differential equations via similarity transformations and then solved through the Runge–Kutta–Fehlberg with shooting technique. The physical parameters, which emerges from the derived system, are discussed in graphical formats. Excellent proficiency in the numerical process is analyzed by comparing the results with available literature in limiting scenarios.

The significant outcomes of the current investigation are that the velocity field uplifts for higher velocity slip and magnetic strength. Further, the heat transfer rate is reduced with the incremental values of the Eckert number, while it uplifts with thermal slip and radiation parameters. An increase in Brinkmann’s number uplifts the entropy generation rate, while that peters out the Bejan number. The results of this study are of importance involving in the assessment of the effect of some important design parameters on heat transfer and, consequently, on the optimization of industrial processes.

This study is original work that reports the hybrid nanofluid model of Fe3O4–Co/kerosene.

This paper aims to build a novel model and approach that assist an aircraft MRO procurement and overhaul management problems from the perspective of aircraft maintenance service provider, in order to ensure its smoothness maintenance activities implementation. The mathematical model utilizes the data related to warehouse inventory management, incoming customer service planning as well as risk forecast and control management at the decision-making stage, which facilitates to alleviate the negative impact of the uncertain maintenance demands on the MRO spare parts inventory management operations.

A stochastic model is proposed to formulate the problem to minimize the impact of uncertain maintenance demands, which provides flexible procurement and overhaul strategies. A Benders decomposition algorithm is proposed to solve large-scale problem instances given the structure of the mathematical model.

Compared with the default branch-and-bound algorithm, the computational results suggest that the proposed Benders decomposition algorithm increases convergence speed.

The results among the same group of problem instances suggest the robustness of Benders decomposition in tackling instances with different number of stochastic scenarios involved.

Extending the proposed model and algorithm to a decision support system is possible, which utilizes the databases from enterprise's service planning and management information systems.

A novel decision-making model for the integrated rotable and expendable MRO spare parts planning problem under uncertain environment is developed, which is formulated as a two-stage stochastic programming model.

The purpose of this paper is to conduct a comprehensive review of the noteworthy contributions made in the area of the Feedforward neural network (FNN) to improve its generalization performance and convergence rate (learning speed); to identify new research directions that will help researchers to design new, simple and efficient algorithms and users to implement optimal designed FNNs for solving complex problems; and to explore the wide applications of the reviewed FNN algorithms in solving real-world management, engineering and health sciences problems and demonstrate the advantages of these algorithms in enhancing decision making for practical operations.

The FNN has gained much popularity during the last three decades. Therefore, the authors have focused on algorithms proposed during the last three decades. The selected databases were searched with popular keywords: “generalization performance,” “learning rate,” “overfitting” and “fixed and cascade architecture.” Combinations of the keywords were also used to get more relevant results. Duplicated articles in the databases, non-English language, and matched keywords but out of scope, were discarded.

The authors studied a total of 80 articles and classified them into six categories according to the nature of the algorithms proposed in these articles which aimed at improving the generalization performance and convergence rate of FNNs. To review and discuss all the six categories would result in the paper being too long. Therefore, the authors further divided the six categories into two parts (i.e. Part I and Part II). The current paper, Part I, investigates two categories that focus on learning algorithms (i.e. gradient learning algorithms for network training and gradient-free learning algorithms). Furthermore, the remaining four categories which mainly explore optimization techniques are reviewed in Part II (i.e. optimization algorithms for learning rate, bias and variance (underfitting and overfitting) minimization algorithms, constructive topology neural networks and metaheuristic search algorithms). For the sake of simplicity, the paper entitled “Machine learning facilitated business intelligence (Part II): Neural networks optimization techniques and applications” is referred to as Part II. This results in a division of 80 articles into 38 and 42 for Part I and Part II, respectively. After discussing the FNN algorithms with their technical merits and limitations, along with real-world management, engineering and health sciences applications for each individual category, the authors suggest seven (three in Part I and other four in Part II) new future directions which can contribute to strengthening the literature.

The FNN contributions are numerous and cannot be covered in a single study. The authors remain focused on learning algorithms and optimization techniques, along with their application to real-world problems, proposing to improve the generalization performance and convergence rate of FNNs with the characteristics of computing optimal hyperparameters, connection weights, hidden units, selecting an appropriate network architecture rather than trial and error approaches and avoiding overfitting.

This study will help researchers and practitioners to deeply understand the existing algorithms merits of FNNs with limitations, research gaps, application areas and changes in research studies in the last three decades. Moreover, the user, after having in-depth knowledge by understanding the applications of algorithms in the real world, may apply appropriate FNN algorithms to get optimal results in the shortest possible time, with less effort, for their specific application area problems.

The existing literature surveys are limited in scope due to comparative study of the algorithms, studying algorithms application areas and focusing on specific techniques. This implies that the existing surveys are focused on studying some specific algorithms or their applications (e.g. pruning algorithms, constructive algorithms, etc.). In this work, the authors propose a comprehensive review of different categories, along with their real-world applications, that may affect FNN generalization performance and convergence rate. This makes the classification scheme novel and significant.

The purpose of this paper is three-fold: to review the categories explaining mainly optimization algorithms (techniques) in that needed to improve the generalization performance and learning speed of the Feedforward Neural Network (FNN); to discover the change in research trends by analyzing all six categories (i.e. gradient learning algorithms for network training, gradient free learning algorithms, optimization algorithms for learning rate, bias and variance (underfitting and overfitting) minimization algorithms, constructive topology neural networks, metaheuristic search algorithms) collectively; and recommend new research directions for researchers and facilitate users to understand algorithms real-world applications in solving complex management, engineering and health sciences problems.

The FNN has gained much attention from researchers to make a more informed decision in the last few decades. The literature survey is focused on the learning algorithms and the optimization techniques proposed in the last three decades. This paper (Part II) is an extension of Part I. For the sake of simplicity, the paper entitled “Machine learning facilitated business intelligence (Part I): Neural networks learning algorithms and applications” is referred to as Part I. To make the study consistent with Part I, the approach and survey methodology in this paper are kept similar to those in Part I.

Combining the work performed in Part I, the authors studied a total of 80 articles through popular keywords searching. The FNN learning algorithms and optimization techniques identified in the selected literature are classified into six categories based on their problem identification, mathematical model, technical reasoning and proposed solution. Previously, in Part I, the two categories focusing on the learning algorithms (i.e. gradient learning algorithms for network training, gradient free learning algorithms) are reviewed with their real-world applications in management, engineering, and health sciences. Therefore, in the current paper, Part II, the remaining four categories, exploring optimization techniques (i.e. optimization algorithms for learning rate, bias and variance (underfitting and overfitting) minimization algorithms, constructive topology neural networks, metaheuristic search algorithms) are studied in detail. The algorithm explanation is made enriched by discussing their technical merits, limitations, and applications in their respective categories. Finally, the authors recommend future new research directions which can contribute to strengthening the literature.

The FNN contributions are rapidly increasing because of its ability to make reliably informed decisions. Like learning algorithms, reviewed in Part I, the focus is to enrich the comprehensive study by reviewing remaining categories focusing on the optimization techniques. However, future efforts may be needed to incorporate other algorithms into identified six categories or suggest new category to continuously monitor the shift in the research trends.

The authors studied the shift in research trend for three decades by collectively analyzing the learning algorithms and optimization techniques with their applications. This may help researchers to identify future research gaps to improve the generalization performance and learning speed, and user to understand the applications areas of the FNN. For instance, research contribution in FNN in the last three decades has changed from complex gradient-based algorithms to gradient free algorithms, trial and error hidden units fixed topology approach to cascade topology, hyperparameters initial guess to analytically calculation and converging algorithms at a global minimum rather than the local minimum.

The existing literature surveys include comparative study of the algorithms, identifying algorithms application areas and focusing on specific techniques in that it may not be able to identify algorithms categories, a shift in research trends over time, application area frequently analyzed, common research gaps and collective future directions. Part I and II attempts to overcome the existing literature surveys limitations by classifying articles into six categories covering a wide range of algorithm proposed to improve the FNN generalization performance and convergence rate. The classification of algorithms into six categories helps to analyze the shift in research trend which makes the classification scheme significant and innovative.

Energy-saving behavior of individuals is essential to minimize energy use and reduce the emission of toxic gases. This study's actual focus is to find out the determinants of the energy-saving behavior of individuals in the workplace.

As a theoretical research model, the extended theory of planned behavior (TPB) has been used to analyze the determinants of energy-saving intentions. A survey method is used to collect 289 valid data, and structural equation modeling (SEM) is used to analyze the data.

The final result shows that the variables attitude at home, subjective norm (SN) and descriptive norms positively impact intention to save energy at the workplace. In contrast, the construct attitude and perceived behavior control is insignificant in this research. On the other hand, the personal moral norm (PMN) is a powerful predictor of individual energy-saving intentions at the workplace.

This research provides insights that will help the organizations understand the behavior of individuals at the workplace for energy-saving intentions to formulate such policies that will enhance individuals' practice for energy savings.

With the challenge of ecological business sustainability, concepts like green design, eco-friendly products, sustainable technologies and efficient processes have compelled the organizations to adopt change. The purpose of this paper is to focus on understanding the impact of green supply chain (GSC) management practices and institutional pressures on economic and environmental performances of organizations in an unstable developing economy.

Data were collected from the supply chain specialists working in manufacturing firms through a questionnaire. Valid data of 101 respondents were used for analyzing the relationship among the constructs with the help of structural equation modeling.

The result of this study reveals that internal GSC practices and institutional pressure have a negative insignificant impact on economic performance, whereas all the constructs are the significant contributors toward improving environmental performance.

This study will help the supply chain decision makers to make a strategy that is beneficial for improving both economic and environmental dimensions of the performance of a firm.

An environmental management study under a rapidly changing scenario is always helpful to understand the behavior and its impact. This study is very useful and need of a time in the context of any developing country facing an economic and environmental crisis.

This study aims to examine the effect of supply chain agility (SCA) on the export performance of the Pakistani textile industry. Despite being one of the leading manufacturing and exporting sectors, only a handful of the extant literature is found on the textile industry.

A structured questionnaire was prepared using the extant literature. Data was gathered from 146 respondents associated with the textile industry of Pakistan. Hypotheses were tested using structural equation modeling after ensuring the reliability and validity of the data collected for this study.

This study provides several crucial insights for export-oriented firms. International entrepreneurial orientation and domestic competition are the crucial drivers for a firm’s agility. This study confirms that SCA has a significant impact on escalating export performance of the Pakistani textile industry in the international market.

To the best of the authors’ knowledge, the theoretical framework developed for this study is original and drawn from the extant literature. The findings of resulted from empirical testing of the theoretical model in the context of developing countries provide new information in the knowledge body.

This research aims to propose a framework for measuring customer loyalty for third party logistics (3PL) industry by exploring the attributes that are more attractive to customers and ascertain the mechanisms for increasing customer loyalty in 3PL industry.

Data were collected from one hundred and thirty-three (133) respondents who were employees of different industries that outsource 3PL services. The partial least square structural equation modeling (PLS–SEM) was deployed for analysis.

The results showed that service quality has a significant positive impact on customer orientation, customer satisfaction and relationship quality. On the other hand, customer orientation has been observed to positively impact customer satisfaction but an insignificant impact on customer loyalty and relationship quality. Customer satisfaction has a significant positive impact on relationship quality but an insignificant impact on customer loyalty. Also, relationship quality has a significant positive impact on customer loyalty.

The results recommend that 3PL companies' managers focus more on developing quality relationships with their customers, delivering exemplary service quality and offering customer orientation.

This study will help the stakeholders gain much more understanding and insights on how competitive advantage can be achieved and, consequently, help 3PL become the market leaders.

Humanitarian services usually perform in the face of uncertainty in which mobilization of resources in an efficient and effective manner is a big challenge. Sharing timely and correct information among logistics partners and workers is a key to drive rapid response logistics effectively. The purpose of this paper is to understand how coordinated effort effects resources management (RM).

This study uses quantitative research methodology and collected data from 82 humanitarian workers dealing with logistical activities from a densely populated city of Pakistan. Data were then statistically analyzed through partial least squares–structural equation modeling.

The results suggest that the success of humanitarian supply network depends upon the level of trust among the partners, which accelerates commitment through strong coordination. Information sharing reduces behavioral uncertainty and enhances swift trust (ST). ST then helps to improve coordination and commitment from all stakeholders in order to manage resources to lead effective relief operations.

The study guides the practitioners and relief operations’ policy makers to lay emphasis on distributing right and timely information flow among the partners, which can lead to effective, efficient and swift humanitarian relief operations.

This study on RM during humanitarian logistics is well timed in the context of developing country with high uncertain events, improper infrastructure and very limited resources.