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This paper aims to develop efficient decision support tools for a firm’s environment protection by using greening effort while yet improving profitability by utilizing pricing and inventory decisions with discount consideration.

This study proposed a mathematical model for price- and greening effort-dependent demand rate with discount considerations. Later, the mathematical model is extended to the situation in which the demand rate is also dependent on the stock level, in addition to the price and greening effort. Efficient solution methodologies are developed for finding the optimal solution to the proposed models.

Simple yet elegant models are proposed to mimic real-life applications. Structural properties of the models are explored to outline efficient algorithms with quantity discounts.

The paper considers monopoly and assumes deterministic demand. Only a more commonly observed all-units discount scheme is studied.

The models provide decision support tools for firms in pursuit of joint profit maximization and environment consciousness goals.

The study develops environment-friendly approaches for inventory management and improving the profitability alike.

This study is among the first to consider environmental protection with an investment in greening effort along with inventory management and pricing decision. The study also explored the effect of all-unit quantity discounts.

The purpose of this paper is to develop a decision support tool for risk-based maintenance scheduling for a large heavily equipped gas sweetening unit in a Liquefied Natural Gas (LNG) plant. Two conflicting objectives, i.e., total maintenance cost and the reliability, are considered in the tool. The tool is tested with the real plant data and suggests several Pareto-optimal schedules for a decision maker to choose from. The financial impacts are assessed.

A bi-objective scheduling optimization model is developed for maintenance scheduling using a risk-based framework. The model is developed integrating genetic algorithm and simulation-based optimization to find Pareto-optimal schedules. The model delivered true Pareto front optimal solutions for given plant-specific data. The two conflicting objectives: the minimization of total expenditures incurred on maintenance-related activities and improving the total reliability are considered.

For large and complex processing facilities such as LNG plant, a shutdown of facility generates a significant financial impact, resulting in millions of dollars in production loss. The developed risk-based equipment selection strategy helps to minimize such an event of production loss by generating a thorough maintenance strategy for inspection, repair, overhaul or replacement schedule of the unit without initiating the shutdown. The proposed model has been successfully applied to obtain an optimize maintenance schedule for a gas sweetening unit.

A future work may consider the state-dependent models for various failure modes that will result in obtaining a better representation of the model. The proposed scheduling can further be extended to multi-criteria scheduling including availability, resource limitation and inflationary condition. A comparative analysis with other meta-heuristic techniques such as harmony search algorithm, tabu search, and simulated annealing will further help in confirming the schedule obtained from this application.

Maintenance scheduling using a conventional approach for special equipment generally does not consider the conflicting objectives. This research addresses this aspect using a bi-objective model. The usefulness of risk-based method is to assist in minimizing the financial and safety risk exposure to the operating companies, but some variation in results is expected due to varying risk matrix for different organizations.

Managing two objectives, i.e., minimizing the cost of maintenance-related activities, while at the same time maximizing the overall reliability dramatically, helps in mitigating adverse safety and financial risk due to fires, explosions, fatality and excessive maintenance cost.

Research develops a decision support tool for managing conflicting objectives for an LNG process. This research highlights the impact of utilizing the simulation-based approach coupled with risk-based equipment selection for complex processing unit or plant maintenance scheduling optimization.

This study research contributes in fulfilling the gap by carrying out a systematic literature review (SLR) of contemporary research studies in closed-loop supply chain (CLSC). To the best of the author’s knowledge, an SLR rooted in bibliometric analysis has not been carried focusing on advent developments in CLSC. SLR employs scientific methodologies to select papers from standard databases. The SLR using advanced bibliometric and network analysis enables unveiling the key features of the contemporary literature.

The author has analyzed over 333 documents published from 2008 and onward. Using the contemporary tools from bibliometric analysis tools, the author presented an exploratory analysis. A network analysis is utilized to visualize literature and create clusters for the cocited research studies, keywords and publication sources. A detailed multivariate analysis of most influential works published based top 100 articles via a cocitation matrix is done. The multivariate analysis used k-means clustering in which optimal number of clusters are estimated. The analysis is further extended by using a factor analysis, which enables determining the most influential clusters in the k-means clustering analysis.

The SLR using a bibliometric and network analysis enables unveiling the key features of the contemporary literature in CLSC. The author examined published research for influential authors, sources, region, among other key aspects. Network analysis enabled visualizing the clusters of cocited research studies, cowords and publication sources. Cluster analysis of cocited research studies is further explored using k-means clustering. Factor analysis extends findings by identifying most contributing grouping of research areas within CLSC research. Each clustering technique disclosed a unique grouping structure.

CLSC has received considerable attention, and its core areas start with focusing on reverse logistics concepts relating reuse, recycling, remanufacturing, among others. Contemporarily, the studies have enhanced reverse logistics core functionalities interfaced with the other interesting avenues related to CO₂ emission reduction, greening and environmental protection, sustainability, product design and governmental policies. Earlier studies have presented a literature review of CLSC; however, these reviews are commonly conducted in the traditional manner where the authors select papers based on their area of expertise, interest and experience. As such these reviews fall short in utilizing the advanced tools from bibliometric analysis.

The purpose of this paper is to compare the effectiveness of two meta‐heuristics in solving the problem of scheduling maintenance operations and jobs processing on a single machine.

The two meta‐heuristic algorithms, tabu search and simulated annealing are hybridized using the properties of an optimal schedule identified in the existing literature to the problem. A lower bound is also suggested utilizing these properties.

In a numerical experimentation with large size problems, the best‐known heuristic algorithm to the problem is compared with the tabu search and simulated annealing algorithms. The study shows that the meta‐heuristic algorithms outperform the heuristic algorithm. In addition, the developed meta‐heuristics tend to be more robust against the problem‐related parameters than the existing algorithm.

A future work may consider the possibility of machine failure along with the preventive maintenance. This relaxes the assumption that the machine cannot fail but it is rather maintained preventively. The multi‐criteria scheduling can also be considered as an avenue of future work. The problem can also be considered with stochastic parameters such that the processing times of the jobs and the maintenance related parameters are random and follow a known probability distribution function.

The usefulness of meta‐heuristic algorithms is demonstrated for solving a large scale NP‐hard combinatorial optimization problem. The paper also shows that the utilization of the directed search methods such as hybridization could substantially improve the performance of a meta‐heuristic.

This research highlights the impact of utilizing the directed search methods to cause hybridization in meta‐heuristic and the resulting improvement in their performance for large‐scale optimization.

The purpose of this paper is to study the impact of differentiation price which has been utilized to segment demand, but results in imperfect segmentation. The use of a differentiation price is among the most widely used Revenue Management (RM) techniques to segment a firm’s demand to augment profitability.

Mathematical models are developed for a firm’s RM which use a differentiation price to categorize its market demand into two segments. Three distinct demand situations are considered: price-dependent deterministic demand, price-dependent stochastic demand whose distribution is known and price-dependent stochastic demand whose distribution is unknown. Models are analyzed to determine optimal joint control of a firm’s pricing and inventory decisions for each market segment.

The analysis of the firm’s RM model has shown that revenue is jointly concave in pricing and order quantity. In most demand situations, closed-form mathematical expressions for optimal pricing and inventory are obtained.

In RM models developed in this paper, a firm only selects a differentiation price. Thus, an optimal selection of the differentiation price along with the pricing and inventory decisions may lead to an additional profitability which has not been explored in this research.

The findings reported are relevant to RM managers and practitioners and help them to calibrate their optimal revenues by segmenting markets using a differentiation price.

This paper provides a quantitative perspective of a firm’s decision on the use of the differentiation price and the market response.

The paper provides a firm’s optimal decision on pricing and inventory when it experiences demand leakage due to categorizing its market demand into two segments using a differentiation price.

The purpose of this paper is to understand the reasons behind the intent for information technology (IT) outsourcing in academic institutions in GCC countries. It also aims to develop a multi-criteria decision model (MCDM) to aid the critical decision of IT outsourcing vendor selection.

The research utilizes a questionnaire-based survey to investigate reasons of IT outsourcing intent and the factors considered important for IT outsourcing vendor selection in academic institutions. The results of questionnaire-based study were utilized to develop a grey theory-based MCDM for vendor selection.

The results show that facilitating access to new technology, focus on core-competence, saving staff costs, and improved customer service are the most important factors for IT outsourcing intent while reputation of vendor, access to the state of art technology, quality of service, and knowledge of industry were considered as the most important factors for IT outsourcing vendor selection. Grey theory-based decision model was applied to a real case to facilitate the decision of selection of an IT outsourcing vendor.

Academic institutions that plan to outsource IT in future would be the major beneficiaries of this study. They can utilize the multi-criteria model to select the best vendor. The model facilitates a more rational decision making as it incorporates several criteria considered important for IT outsourcing vendor selection.

This study contributes to the body of research on IT outsourcing. It is first of its kind with its focus on academic institutions in GCC countries where currently education is a priority and IT is a backbone for its delivery. Another novelty of this research is that it propose a MCDM for IT outsourcing vendor selection. The findings of this study would serve as a guide to those institutions that intend to outsource IT functions to meet the ever growing needs of managing IT effectively.

The aim of this paper is to study pharmacy service impact on patient satisfaction and to determine what factors saliently link with pharmaceutical service performance at Hamad General Hospital.

A patient satisfaction questionnaire was designed using the literature and consultation with Hamad General Hospital medical experts. The questionnaire contained 22 items that focused on five influencing factors: promptness; attitude; supply; location; medication education; and respondent demographic aspects. A total of 220 respondents completed the questionnaire. An exploratory factor analysis was used to group items and a structural equation model was developed to test causality between five factors along with their influence on patient satisfaction.

The study establishes statistical evidence that patient satisfaction is positively influenced by service promptness, pharmacist attitude, medication counseling, pharmacy location and waiting area. Several socio‐demographic characteristics have statistically different effect on satisfaction, notably: gender; marital status; health status; age; educational level; and ethnicity. However, medication supply did not influence patient satisfaction.

Pharmaceutical services are recognized as an essential healthcare‐system component. Their impact on customer satisfaction has been investigated in many countries; however, there is no such study in Qatar.

The findings identify pharmaceutical service performance indicators and provide guidelines to improve Qatari pharmaceutical services.

This study explores the service quality dimensions in Internet banking and their impact on e-customer’s satisfaction and e-customer’s loyalty. This study tries to inspect the structural association between Internet banking service quality, electronic customer satisfaction and electronic customer loyalty based on separate constructs.

In this present research, quantitative approach is applied. The data is gathered from 500 bank clients in Pakistan by using structured questionnaires, and the theoretical model is tested by partial least square structured equation modeling (PLS-SEM). Moreover, convergent validity and discriminant validity were assessed.

Results show that all the dimensions are found to have a positive and significant influence on customer satisfaction while customer’s satisfaction has a significant and positive impact on customer’s loyalty. Findings indicate that service quality plays a very important role in every society, as it has become the basis for how customers interpret online banking and, in the end, how it interacts and operates with online services.

This research adds up considerably to the literature of bank marketing, and it is also fruitful for the academicians since it demonstrates the way Internet banking service quality determinants predict e-satisfaction of clients which ultimately raises the e-loyalty of clients. This study is useful for those E-retailers and managers who want to grab e-retailing market.

This research suggests a model which ultimately enhances customer loyalty towards Internet banking service quality through customer satisfaction in Pakistan. It involves modified model of E-SERVQUAL (user friendliness, efficiency of websites, personal need, and site organization) which connects it to electronic customer satisfaction and electronic customer loyalty. Therefore, it will assist the Internet banking sector in building effective marketing tactics, establishing long lasting relationships with clients and acquiring the competitive edge in the market.

The earth needs to go green as it clarifies that humans should adopt a sustainable lifestyle that will be friendly to the environment and society. The emerging stream of academic literature on “go green” adds a new perspective to the deep-rooted conception of entrepreneurship. The objective of green entrepreneurship is to follow the traditional concepts of entrepreneurship but also brings the additional potential for society and the environment. The present paper is designed to study the impact of personality traits and university green entrepreneurial support on green entrepreneurial intentions along with the moderating role of environmental values.

The data are collected by using the survey method, and the five-point Likert scale is used for this purpose. The statistical techniques applied to the dataset were confirmatory factor analysis and partial least square structural equation modeling.

The results revealed that personality traits are positively and significantly associated with green entrepreneurial intention, but risk-averse students depict insignificant results towards green entrepreneurial intention. Also, university green entrepreneurial support results in positive green entrepreneurial intention. Environmental values also strengthen the association.

Previously, researchers emphasized the entrepreneurial intentions for the sake of improving the economy, decreasing unemployment and promoting small ventures, but none focused on the green aspects of entrepreneurship. To date, we still lack knowledge regarding students' green entrepreneurial intentions and how personality traits and green support affect the intention. The focus of the present study is to sustain nature and ecosystems through green entrepreneurship by providing directions and economic and noneconomic gains for investors, entrepreneurs, students, universities and societies.