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Based on complex network theory, a method for critical elements identification of China Railway High-speed 2 (CRH2) train system is introduced in this paper.

First, two methods, reliability theory and complex theory, are introduced, and the advantages and disadvantages for their application in identifying critical elements of high-speed train system are summarized. Second, a multi-layer multi-granularity network model including virtual and actual nodes is proposed, and the corresponding fusion rules for the same nodes in different layers are given.

Finally, taking CRH2 train system as an example, the critical elements are identified by using complex network theory, which provides a reference for train operation and maintenance.

A method of identifying key elements of CRH2 train system based on integrated importance indices is introduced, which is a meaningful extension of the application of complex network theory to identify key components.

Evaluation of energy and climate policy interactions is a complex issue, whereas stakeholders' preferences incorporation has not been addressed systematically. The purpose of this paper is to present an integrated weighting methodology that has been developed in order to incorporate weighting preferences into an ex ante evaluation of climate and energy policy interactions.

A multi‐criteria analysis (MCA) weighting methodology which combines pair‐wise comparisons and ratio importance weighting methods has been elaborated. It initially introduces the users to the evaluation process through a warming up holistic approach for an initial rank of the criteria and then facilitates them to express their ratio relative importance in pair‐wise comparisons of criteria by providing them an interactive mean with verbal, numerical and visual representation of their preferences. Moreover, it provides a ranking consistency test where users can see the degree of (in)consistency of their preferences.

Stakeholders and experts in the energy policy field who tested the methodology stated their approval and satisfaction for the combination of both ranking and pair‐wise comparison techniques, since it allows the gradual approach to the evaluation problem. In addition, main difficulties in MCA weights elicitation processes were overcome.

The methodology is tested by a small sample of stakeholders, whereas a larger sample, a broader range of stakeholders and applications on different climate policy evaluation cases merit further research.

The novel aspect of the developed methodology consists of the combination of ranking and pair‐wise comparison techniques for the elicitation of stakeholders' preferences.

The purpose of this paper is to explore the teaching performance of Turkish Business Schools (BSs). It also aims to determine the degree of importance of factors affecting the teaching performance of Turkish BSs. The final objective is to test the functionality and applicability of the model.

This study presents a ranking approach based on grey relational analysis (GRA). While evaluating the BSs, data were collected for 19 Turkish BSs in terms of five main criteria such as OSS score; Number of faculty members; Number of students per faculty member; the mean of KPSS score; and the standard deviation of KPSS score. In the analysis, three weighted methods were integrated into the GRA in order to weight the criteria.

According to this result, the main factor influencing the teaching performance of Turkish BSs is the OSS score. This study can also confirm that the results obtained from the ranking orders using the proposed methods are reliable and these results can help decision makers to identify the best alternative.

In order to provide benchmarking data more effectively, in future, it would be helpful to collect data from both foundation and state universities with a research focus. Moreover, as an interesting suggestion for future research, fuzzy environment may be further integrated into the framework of GRA.

In contrast to prior research, this study makes comparisons based on the scores of national exams instead of different bibliometric indicators. Furthermore, there are no studies which have used GRA and these weighted methods as combined in education sector.

The hiring process for information technology (IT) personnel, given its high stakes and intricacies, demands an objective, methodical and nuanced approach. The multifaceted nature of IT roles necessitates a comprehensive evaluation methodology to identify and prioritize pertinent skills and competencies. Therefore, this study aims to devise and implement a robust multi-criteria decision-making model integrating the fuzzy analytic hierarchy process (AHP) and fuzzy technique for order preference by similarity to ideal solution (TOPSIS) for assessing and ranking the skills required for IT personnel.

This study integrates expert opinions and insights from the literature to identify five key criteria and 21 sub-criteria essential for IT personnel selection. The fuzzy Analytic Hierarchy Process (AHP) was applied to determine the relative importance of each criterion and sub-criterion. These weighted criteria were then utilized in the fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to systematically rank five candidate alternatives based on their suitability for IT roles.

Results identified “Objective of factors” to be the most significant criteria, whereas “Assessment Centre Score” as the most viable sub-criterion. Using the integrated model results of fuzzy AHP-TOPSIS candidate, A3 was the most fitted IT personnel, whereas Candidate A4 has emerged as the poor/ unsuitable fit.

This research helps firms and policymakers use their limited resources efficiently. Furthermore, this study acts as a guideline for future researchers to empirically investigate the impact of listed skills on employee performance.

This study is a pioneering effort in integrating fuzzy AHP and TOPSIS to address the challenges in IT personnel selection, catering to the intricate layers of human evaluative judgments. Its extended applicability and innovative approach makes it a valuable contribution to the existing body of knowledge, serving as a benchmark for future endeavors in related domains.

This paper is focused on developing an integrated algorithmic approach named as data envelopment analysis and multicriteria decision-making (DEA-MCDM) for ranking decision-making units (DMUs) based on cross-efficiency technique and subjective preference(s) of the decision maker.

Self-evaluation in data envelopment analysis (DEA) lacks in discrimination power among DMUs. To fix this, a cross-efficiency technique has been introduced that ranks DMUs based on peer-evaluation. Different cross-efficiency formulations such as aggressive and benevolent and neutral are available in the literature. The existing ranking approaches fail to incorporate subjective preference of “one” or “some” or “all” or “most” of the cross-efficiency evaluation formulations. Therefore, the integrated framework in this paper, based on DEA and multicriteria decision-making (MCDM), aims to present a ranking approach to incorporate different cross-efficiency formulations as well as subjective preference(s) of decision maker.

The proposed approach has an advantage that each of the aggressive, benevolent and neutral cross-efficiency formulations contribute to select the best alternative among the DMUs in a MCDM problem. Ordered weighted averaging (OWA) aggregation is applied to aggregate final cross-efficiencies and to achieve complete ranking of the DMUs. This new approach is further illustrated and compared with existing MCDM approaches like simple additive weighting (SAW) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to prove its validity in real situations.

The choice of cross-efficiency formulation(s) as per subjective preference of the decision maker and different orness levels lead to different aggregated scores and thus ranking of the DMUs accordingly. The proposed ranking approach is highly useful in real applications like R and D projects, flexible manufacturing systems, electricity distribution sector, banking industry, labor assignment and the economic environmental performances for ranking and benchmarking.

To prove the practical applicability and robustness of the proposed integrated DEA-MCDM approach, it is applied to top twelve Indian banks in terms of three inputs and two outputs for the period 2018–2019. The findings of the study (1) ensure the impact of non-performing assets (NPAs) on the ranking of the selected banks and (2) are enormously valuable for the bank experts and policy makers to consider the impact of peer-evaluation and subjective preference(s) in formulating appropriate policies to improve performance and ranks of underperformed banks in competitive scenario.

To the best of the authors’ knowledge, this is the first study that has integrated both DEA and MCDM via OWA aggregation to present a ranking approach that can incorporate different cross-efficiency formulations and subjective preference(s) of the decision maker for ranking DMUs.

In the context of India, this article proposes an integrated multicriteria decision-making (MCDM) regression-based methodology to evaluate input-level performance of the schools and investigate the impact of this performance along with contextual factors, i.e. medium of instruction and location of the school, on the school's output level performance, i.e. student pass rate.

First, Shannon entropy-based approach is applied for the weight assignment to different parameters. Then, integrated VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) technique for order preference by similarity to an ideal solution (TOPSIS)-based methodology is devised to measure the input-level performance of a school. Finally, multiple linear regression (MLR) analysis is incorporated to study the effect of input-level performance and above-mentioned contextual factors on the school's output-level performance.

Proposed methodology is applied to assess the input-level performance of 82,930 primary and secondary schools of West Bengal, India. All the factors have a significant impact on boys' pass rate, whereas only input-level performance and location of the school have a significant influence on the girls' pass rate.

The entropy-based approach highlights the importance of scientific weight assignment. Integrated MCDM demonstrates the significance of aggregation due to the variation in scores related to input-level performance across the methods. Regression analysis facilitates the exploration of determinants influencing the output-level performance of the schools.

This work depicts a holistic picture of the performance measurement system of the schools. It encompasses scientific weight assignment to the evaluation criteria, integrated input-level performance assessment of the schools and investigation into the effect of this performance, as well as other contextual factors on the output level performance.

Supplier selection along with continuous evaluation of their performance is a crucial activity in healthcare supply chain management for effective utilization of scarce resources while providing quality service at an affordable price, and minimizing chances of stock-out, avoiding serious consequences on the illness or fatality of the patients. Presence of both qualitative and quantitative evaluation criteria, set of potential suppliers and participation of different stakeholders with varying interest make healthcare supplier selection a challenging task which can be effectively solved using any of the multi-criteria decision making (MCDM) methods.

To deal with various qualitative criteria, like cost, quality, delivery performance, reliability, responsiveness and flexibility, this paper proposes integration of grey system theory with a newly developed MCDM tool, i.e. mixed aggregation by comprehensive normalization technique (MACONT) to identify the best performing supplier for pharmaceutical items in a healthcare unit from a pool of six competing alternatives based on the opinions of three healthcare professionals.

While assessing importance of the six evaluation criteria and performance of the alternative healthcare suppliers against those criteria using grey numbers, and exploring use of three normalization procedures and two aggregation operations of MACONT method, this integrated approach singles out S5 as the most compromised healthcare supplier for the considered problem. A sensitivity analysis of its ranking performance against varying values of both balance parameters and preference parameters also validates its solution accuracy and robustness.

This integrated approach can thus efficiently solve healthcare supplier selection problems based on qualitative evaluation criteria in uncertain group decision making environment. It can also be deployed to deal with other decision making problems in the healthcare sector, like supplier selection for healthcare devices, performance evaluation of healthcare units, ranking of physicians etc.

The objective of the study is to identify and analyse drivers of smart manufacturing using integrated grey-based approaches. The analysis facilitates industry practitioners in the identification of preference of drivers through which smart manufacturing can be implemented. These drivers are explored based on existing literature and expert opinion.

Modern manufacturing firms have been adopting smart manufacturing concepts to sustain in the global competitive landscape. Smart manufacturing incorporates integrated technologies with a flexible workforce to interlink the cyber and physical world. In order to facilitate the effective deployment of smart manufacturing, key drivers need to be analysed. This article presents a study in which 25 drivers of smart manufacturing and 8 criteria are analysed. Integrated grey Technique for Order Preference by Similarity to Ideal Solution (grey TOPSIS) is applied to rank the drivers. The derived ranking is validated using “Complex Proportional Assessment – Grey” (COPRAS-G) approach.

In total, 25 drivers with 8 criteria are being considered and an integrated grey TOPSIS approach is applied. The ranking order of drivers is obtained and further sensitivity analysis is also done.

In the present study, 25 drivers of smart manufacturing are analysed. In the future, additional drivers could be considered.

The study presented has been done with inputs from industry experts, and hence the inferences have practical relevance. Industry practitioners need to focus on these drivers in order to implement smart manufacturing in industry.

The analysis of drivers of smart manufacturing is the original contribution of the authors.

The purpose of this paper is to propose a holistic approach for supplier evaluation and purchasing order allocation among the ranked suppliers who meet acceptable levels of economic, environmental and social measures.

A mixed research method of case study and analytical approach is adopted in this research. A fuzzy analytical hierarchical process (FAHP) is applied for ranking of suppliers. Supplier ranks are validated using judgements from multiple decision makers. Purchasing order allocation among the ranked suppliers is determined using cost minimization subject to multiple criteria of economic, environmental and social conditions. A cement manufacturing case example demonstrates and validates the proposed approach.

The research shows that both economic and environmental considerations are significant when suppliers are evaluated for sustainable procurement within the best practice of supply management process. Ranking of suppliers, based on experts’ opinions, indicates varying degrees of importance for each criterion. Adoption of sustainable procurement criteria for evaluating supplier in a cement manufacturing organization is explained by three organizational theories including resource-based, institutional and dynamic capabilities theories. Preferred suppliers from FAHP method are confirmed by judgements from multiple decision-makers. The analysis reveals that purchasing order allocation is different when suppliers are evaluated based on their relative importance and overall ranking.

Currently, individual performance measures and decision-makers are selected from a limited set. The purchasing allocation among ranked suppliers, subjected to cost minimization, incorporates environmental objective of acceptable carbon dioxide emission and social perspective of health and safety of workers, and provides a new approach for dual supplier evaluation and purchasing allocation problem in cement industry. Adopting the proposed supplier evaluation and order allocation approach in practice needs to be guided by the operational principles and an overall methodology which is appropriate for the specific industry with sustainability objectives.

This research enables decision-makers to incorporate sustainability analysis in the supplier evaluation as the basis for best practice with an industry-friendly holistic approach. Using organizational theories, the research re-enforces the importance of not only the energy consumption and environmental management systems of environmental dimension as driving forces/factors from Institutional theory perspective, but also pollution controls and prevention as purchasing capabilities from resource-based theory perspective. The proposed approach is expected to motivate decision-makers to consider sustainable perspectives in supplier evaluation and order allocation processes in a global supply chain and can become a benchmarking tool.

Suppliers’ information on health and safety of their truck drivers are used in order allocation, thus emphasizing the importance of social dimension and encouraging better conditions and benchmarking for delivery drivers.

This paper extends the contribution to the literature by providing guidelines for managers to set strategies, benchmarks and policies within broader sustainable supply chain practices and demonstrates the applicability of the approach using a cement-manufacturing scenario in an emerging economy.