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The purpose of this paper is to propose methods for fast incremental indexing with effective and efficient query processing in XML element retrieval. The effectiveness of a search system becomes lower if document updates are not handled when these occur frequently on the Web. The search accuracy is also reduced if drastic changes in document statistics are not managed. However, existing studies of XML element retrieval do not consider document updates, although these studies have attained both effectiveness and efficiency in query processing. Thus, the authors add a function for handling document updates to the existing techniques for XML element retrieval.

Though it will be important to enable fast updates of indices, preliminary experiments have shown that a simple incremental update approach has two problems: some kinds of statistics are inaccurate, and it takes a long time to update indices. Therefore, two methods are proposed: one to approximate term weights accurately with a small number of documents, even for dynamically changing statistics; and the other to eliminate unnecessary update targets.

Experimental results show that this proposed system can update indices up to 32 per cent faster than the simple incremental updates while the search accuracy improved by 4 per cent compared with the simple approach. The proposed methods can also be fast and accurate in query processing, even if document statistics change drastically.

The paper shows that there could be a more practical XML element search engine, which can access the latest XML documents accurately and efficiently.

Recent years have witnessed a significant rise in Indian healthcare establishments (HCEs) which indicate that there is a constant need to improve the healthcare quality services through the adoption and implementation of TQM enablers. The purpose of this paper is to identify such enablers and then propose a ranking model for TQM implementation in Indian HCEs for improved performance.

The study identifies 20 TQM enablers through comprehensive literature survey and expert’s opinion, and classifies them into five main categories. The prominence of these enablers is established using a recently developed novel multi-criteria decision making (MCDM) method, i.e. best-worst method (BWM). The importance of the various main category and sub-category enablers is decided on the basis of their weights which are determined by the BWM. In comparison to other MCDM methods, such as analytical hierarchy process, BWM requires relatively lesser comparison data and also provides consistent comparisons which results in both optimal and reliable weights of the enablers considered in this paper. Further, a sensitivity analysis is also carried out to ensure that the ranking (based on the optimal weights) of the various enablers is reliable and robust.

The results of this study reveal that out of five main category enablers, the “leadership-based enablers (E1)” and the “continuous improvement based enablers (E5)” are the most and the least important enablers, respectively. Similarly, among the 20 sub-category enablers, “quality leadership and role of physicians (E14)” and “performing regular survey of customer satisfaction and quality audit (E52)” are the most and the least dominating sub-category enablers, respectively.

This study does not explore the interrelationship between the various TQM enablers and also does not evaluate performance of the various HCEs based on the weights of the enablers.

The priority of the TQM enablers determined in this paper enables decision makers to understand their influence on successful implementation of the TQM principles and policies in HCEs leading to an overall improvement in the system’s performance.

This study identifies the various TQM enablers in HCEs and categorizes them into five main categories and ranks them using the BWM. The findings of this research are quite useful for management of the HCEs to properly understand the relative importance of these enablers so that managers can formulate an effective and efficient strategy for their easy and smooth implementation which is necessary for continuous improvement.

In a globalized environment, small and medium enterprises (SMEs) are facing formidable challenges. Not only do they have to keep up their profitability, but there is also a pressure from various stakeholders to add to their environmental and quality performance .The solution obviously lies in continuously adopting and improving upon lean-green practices in their operations. This work aims at identifying, classifying and building up a duly tested robust ranked-order model of such “enablers”, related to lean-green practices, that puts them (the enablers) in an order of being the most significant to being the least significant further to be accorded the same or similar weight in strategy formulation and implementation stage by Indian SMEs for enhancing their overall organizational performance.

The study identifies 20 enablers (12 lean and 08 green manufacturing enablers) through extensive literature review and experts' opinion survey and classifies them into three main categories. The ranking and significance of each of the main and subcategory enablers is evaluated according to its weight which is determined by the best-worst method (BWM) approach, one of the novel multi-criteria decision-making (MCDM) methods. Further, the results have been drawn after running accuracy check of the rankings (based upon optimal weights) and testing the robustness of the ranked-order model through sensitivity analysis.

The results of this study reveal that out of the three main category enablers, “operational performance enablers (E1)” and “quality performance enablers (E3)” are the most and the least significant enablers, while in the group of 20 subcategory enablers, “Kaizen (E17)” and “environment emission control (E28)” are the most and the least significant subcategory enablers, respectively.

The prioritization model or ranked-order model of the lean-green manufacturing enablers proposed through this study may serve as a standard model to managers to help them decide and allocate their efforts and resources accordingly in managing their operations. This will also help them adopt high-ranking lean-green manufacturing enablers in their firms and benchmark and standardize their existing practices accordingly, leading to greater competitive advantage.

The study identifies various green-lean manufacturing enablers in SMEs, classifies them into three main categories and ranks them using BWM approach. The findings of this study should be extremely relevant to managers, manufacturing engineers and practitioners in Indian SMEs from the perspective of developing deeper appreciation of these enablers as per their relative ranked importance to further formulating an effective and efficient strategy for their implementation resulting in optimal results.

Agility is the ability of an organization to adjust its supply chain tactics and operations to respond quickly against altering business environments such as fluctuating demand pattern, supply chain disruption and global competition. An agile organization must possess a promising capability of swiftly responding to dynamic conditions while being cost-effective without compromising the efficiency. Such high-performance adaptability necessitates the role of supply chain managers to maximize the agility of the supply chain through the efficient use of input resources. Therefore, the purpose of this study to reveal a new decision support tool that would allow the key decision-makers to maximize the agility of the supply chain while deploying the input resources more effectively.

In present study, an integrated approach of popular analytic hierarchy process (AHP) and goal programming (GP) has been adopted as a potential solution methodology. AHP has been implemented to allocate the local and global weights to decision variables, whereas GP incorporates the AHP weights into the desired model.

It was found that the proposed decision support tool restricts the value of the decision variables for maximizing the agility and optimizing the usage of input resources. The results obtained from the model validate the objective of achieving targeted agility level within the available resource limitations.

The decision support tool developed in the proposed study offers a systematic and effectively simple approach to supply chain managers with a goal of identifying the degree of focus under each decision variable in the respective manufacturing organizations.

A novel decision support tool has been developed known as an agility control system), which helps the decision-maker to achieve the required agility in the supply chain by controlling the decision variables.

The purpose of this study is to improve supplier performance and strategic sourcing decisions by integrating jobshop scheduling, inventory management and agile new product development. During the COVID-19 pandemic, the organizations have struggled a lot to maintain the supplier performance and strategic sourcing decisions in the organizational benefit. However, in this context, the organization’s agile new product development (ANPD) process must be aligned with this requirement by maintaining the inventory and jobshop scheduling. As a result, identifying ANPD indicators, performance metrics and developing a structural framework to guide practitioners at various stages for smooth adoption is essential to improve the overall performance.

A comprehensive literature review is conducted to identify jobshop scheduling, inventory management and ANPD indicators along with the performance metrics, and the hierarchical structure is developed with the help of expert opinion. The modified stepwise weight assessment ratio analysis (SWARA) and weighted aggregated sum product assurance (WASPAS) techniques, along with expert judgement, are used in this study to calculate the weights of the indicators and the ranking of the performance metrics.

As per the weight computation by SWARA method, the strategy indicators have the highest relative weight, followed by the product design indicators, management indicators, technical indicators, supply chain indicators and organization culture indicators. According to the ranking of performance metrics obtained through WASPAS, the “frequency of new product development is at the top”, followed by “advances in product design and development” and “estimated versus actual time to market”.

It is believed that the framework developed will help industrial practitioners to plan effectively to improve supplier performance. The indicators identified may guide the ANPD penetration, and performance metrics may be useful for evaluation and comparison.

The outcomes of the present study will be extremely beneficial for the industry practitioners to improve the supplier performance. The indicators identified may guide the ANPD penetration, and performance metrics may be useful for evaluation and comparison.

A unique combination of modified SWARA–WASPAS technique has been used in this study which would be beneficial for organizations willing to adopt the jobshop scheduling and inventory management and ANPD for improving supply chain performance.

A poor performance of the cold supply chain (CSC) may increase the loss of quality and potency of perishables and temperature-sensitive products that deteriorate the financial and environmental aspects of the same. The purpose of the current research work is to identify the critical performance factors (criteria) and their co-factors (sub-criteria) that are responsible for the performance measurement of CSC and suggest the best possible solutions (alternatives) to improve the same.

To achieve the objective of the research, a hierarchical model has been developed and analyzed using Analytic Hierarchy Process (AHP)-Fuzzy TOPSIS as a hybrid approach to obtain the severity weights of the identified criteria and prioritization toward their relative importance for the best possible alternatives.

Analysis reveals that the criteria “energy consumption” comes out to be the most critical criteria, and alternative “application of passive cold devices” is the most effective solution for improving the performance of CSC. Higher energy consumption leads to a higher rate of greenhouse gas (GHG) emissions increasing the global warming phenomenon, high operational cost and degradation of natural energy resources. The Application of Passive Cold Devices (PCDs) utilizes solar energy to operate the refrigeration units reducing the energy consumption, environmental and operating cost of CSC.

The research work provides several insights into the critical issues related to the CSC and suggests significant findings that enable the management and decision-makers to adopt these practices for performance evaluation and improvement of the same. The key findings of the work, such as “application of passive cold devices” and “application of IoT in cold chain logistics”, facilitate an improved platform to improve the CSC performance and proposed several directions that will enhance the merit of future research.

The presented study consolidates the various perspectives associated with CSC performance, explores the most critical criteria and proposes the best suitable cold chain practices for organizational growth. The work also provides an analytical analysis with the essence of practicalities and sensitivity analysis to support the robustness of the results. By enriching the literature and quantitative analysis of the new proposed model, this paper forms vast managerial and research implications in the field of CSC.

The purpose of this paper is to provide a critical and informative exploration of the emerging roles and rising influence of the Global South in shaping the future of global governance. Specifically, it inquires into the following questions: How is the Global South impacting the way we govern globally? What are the pushers, pulls and weights to the futures of global governance? Using Jim Dator’s alternative futures archetype, what is the future of global governance? What are the emerging issues and trends?

It uses Sohail Inayatullah’s futures triangle to map the drivers – the pushes, pulls and weights of global governance and Jim Dator’s archetypes – continued economic growth, collapse, conserver and transformation – to imagine and construct alternative futures of global governance.

The futures triangle analysis maps and reveals three diverse but causally linked Global South narratives of global governance. The pulls of the future include the Brazil, Russia, India, China and South Africa leading the way, and emerging economies reinforcing the pluralization of global governance discourses and systems. New governance regimes create new global governance dynamics and North – South relations. Their increasing social, political and economic clout leads to new governance structures. The Global South’s rising human development index, economic growth, decreasing financial reliance, the rise of minilateralism and South – South cooperation is a push of the present. Weights are recurring financial constraints, their lack of technical capacity, existing international laws, stagnating bureaucracy, poverty, domestic issues and state centrism (among others). Four alternative global governance scenarios emerge: a harmonious world is everybody’s business – a state-centric and economic growth global governance future. Here, the dynamics of global governance remain the same as zero-sum thinking informs the rules of the game. In dangerous transitions and the rise of the rest, however, the status quo is disrupted as power shifts rapidly and detrimentally. Then, in mosaic of the old structure, the South embraces protectionism, and the old vanguards return. Finally, in all boats rise substantially, power is redistributed as emerging states gain larger, formal (and informal) leadership roles in global governance. The global world order is re-designed for the Global South. A world parliament is created and stronger regional confederation or unions emerge.

This paper extensively utilizes existing and emerging literature, official reports, blogs, interviews, books and other digital texts on global governance. The sources relevance is analyzed using the futures triangle tool and dissected to present four detailed scenarios using Dator’s alternative futures archetype. This study seeks to initially explore alternative futures of global governance from the perspective of the Global South. While some studies have approached the topic, only a few authors have addressed global governance using futures tools and methods. The goal of this research is to map and explore some alternative futures of global governance. The paper is less useful in predicting what lies ahead. Its intention is to highlight the “rise of the different” and to create a space for more meaningful conversations on global governance.

This research could provide futurists, policy-makers, international relations scholars and global governance advocates some alternative narratives, frameworks and images of global governance. While it does not offer any specific structures and solutions, it offers a number of emerging issues and perspectives from the Global South that decision-makers and institutions might want to consider as they rethink global governance.

This paper highlights the emerging roles and perspectives of the Global South in global governance. It identifies some “trading zones” and “emerging issues” that may inspire actors to create new global governance spaces, innovate alternative narratives and design new frameworks of global governance.

It maps and constructs some plausible scenarios of global governance that emphasize Global South perspectives while using futures tools and methods.

The purpose of this paper is to develop a model for sustainable manufacturing by adopting a combined approach using AHP, fuzzy TOPSIS and fuzzy EDAS methods. The proposed model aims to identify and prioritize the sustainable factors and technical requirements that help in improving the sustainability of manufacturing processes.

The proposed approach integrates both AHP, Fuzzy EDAS and Fuzzy TOPSIS. AHP method is used to generate the weights of the sustainable factors. Fuzzy EDAS and Fuzzy TOPSIS are applied to rank and determine the application priority of a set of improvement approaches. The ranks carried out from each MCDM approach is assessed by computing the spearman's correlation coefficient.

The results reveal the proposed model is efficient in sustainable factors and the technical requirements prioritizing. In addition, the results carried out from this study indicate the high efficiency of AHP, Fuzzy EDAS and Fuzzy TOPSIS in decision making. Besides, the results indicate that the model provides a useable methodology for managers' staff to select the desirable sustainable factors and technical requirements for sustainable manufacturing.

The main limitation of this paper is that the proposed approach investigates an average number of factors and technical requirements.

This paper investigates an integrated MCDM approach for sustainable factors and technical requirements prioritization. In addition, the presented work pointed out that AHP, Fuzzy EDAS and Fuzzy TOPSIS approach can manipulate several conflict attributes in a sustainable manufacturing context.

The paper aims to investigate the application of particle swarm optimisation and back propagation in weights optimisation and training of artificial neural networks within the mass appraisal industry and to compare the performance with standalone back propagation, genetic algorithm with back propagation and regression models.

The study utilised linear regression modelling before the semi-log and log-log models with a sample of 3,242 single-family dwellings. This was followed by the hybrid systems in the selection of optimal attribute weights and training of the artificial neural networks. Also, the standalone back propagation algorithm was used for the network training, and finally, the performance of each model was evaluated using accuracy test statistics.

The study found that combining particle swarm optimisation with back propagation in global and local search for attribute weights enhances the predictive accuracy of artificial neural networks. This also enhances transparency of the process, because it shows relative importance of attributes.

A robust assessment of the models’ predictive accuracy was inhibited by fewer accuracy test statistics found in the software. The research demonstrates the efficacy of combining two models in the assessment of property values.

This work demonstrated the practicability of combining particle swarm optimisation with back propagation algorithms in finding optimal weights and training of the artificial neural networks within the mass appraisal environment.

This study aims to set out to identify and evaluate potential obstacles to successfully implementing lean construction (LC) as a result.

Several indicators were recognized as major obstacles following an exhaustive assessment of the literature and a multicriteria decision analysis based on the analytic hierarchy process (AHP) of information obtained from a questionnaire survey that was directed to practitioners in the Indian construction industry.

The results of this AHP model suggest that “Managerial” and “Inadequate resources” categories with a priority weight of “0.361” and “0.309” have the highest levels of influence, respectively, while “Inadequate knowledge” and “just in time (JIT)” categories with a priority weight of “0.053” and “0.034” have the lowest levels of influence, respectively.

Construction companies can use the study’s findings as a guide to determine whether they are ready to embrace LC, learn more about the components needed for implementation or investigate any challenges that may arise. These businesses can then create plans to promote the adoption and application of the lean philosophy.

The Indian construction industry may see great success with LC management initiatives. LC concepts have been adopted by many nations, but during the past 20 years, there has only appeared to be a limited amount of lean implementation in the Indian construction industry. It seems that several structural and cultural barriers are preventing its effective implementation. Organizations will not be able to determine what improvement efforts are required, where these efforts should be directed or which initiatives could provide the best outcomes if they are unaware of the elements that influence the effective implementation of LC.