Journal Articles
Permanent URI for this collectionhttp://10.0.100.92:4000/handle/123456789/21
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Item Evaluation of sustainable cold chain suppliers using a combined multi-criteria group decision-making framework under fuzzy ZE-numbers(Expert Systems with Applications, 2024-07) Ecer, Fatih; Haseli, Gholamreza; Krishankumar, Raghunathan; Hajiaghaei-Keshteli, MostafaHealth 4.0 actively focuses on cold supply chains (CSCs) due to digital transformation and technological advancements in refrigeration. With the ill effects of climate change and greenhouse gas emissions, countries globally seek sustainable supply chain management. In this context, integrating sustainability with CSCs becomes crucial. Health sectors actively consider CSC for their activities. In this context, to gain potential suppliers for healthcare management, the present work focuses on grading suppliers from the CSC with sustainability factors. Previous related studies on sustainable cold chain supplier selection (CSSS) (i) have not modeled uncertainty effectively, and (ii) have yet to use any approach under fuzzy Z-numbers information. Therefore, this paper developed an integrated framework, including the Logarithm Methodology of Additive Weights (LMAW) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods based on extended fuzzy Z-numbers. The practicality of the framework is exemplified via a case example within the Indian health sector, and results indicate that job creation, value price, and productivity via renewable resources are the most critical factors. Moreover, regarding the findings, the economic dimension is the foremost pillar for effective selection. Sensitivity and comparison analyses are performed to understand the efficacy of the integrated framework as well. The proposed framework could contribute to the relevant literature due to its capacity to cope with complex decision-making problems and find many application areas in engineering.Item Selection of Cloud Vendors for Medical Centers Using Personalized Ranking With Evidence-Based Fuzzy Decision-Making Algorithm(IEEE Transactions on Engineering Management, 2023-09-07) Krishankumar, Raghunathan; Ecer, Fatih; Yilmaz, Merve Kilinc; Deveci, MuhammetCloud is becoming an attractive buzzword in information technology due to its on-demand pay-as-you-go mechanism. Many service providers emerge in the market with attractive services/offers. In this study, a new integrated decision approach is developed for the cloud vendor (CV) selection problem. First, a double hierarchy structure is adopted to model natural language preferences from experts. Second, the reliability values of experts are determined by using the criteria importance through the intercriteria correlation approach by effectively capturing interactions among experts. Third, the evidence-driven Bayesian technique is presented to calculate the criteria weights that aid in rating CVs. Fourth, a personalized ranking algorithm with the compromise ranking of alternatives from distance to ideal solution approach is proposed to resemble close to human decision-making. A practical example of CV selection for a private medical center in Tamil Nadu is testified to demonstrate the usefulness of the developed framework. Finally, sensitivity analysis and comparison reveal the promising strengths of the developed framework. The results present that assurance, accountability, agility, and usability are the foremost drivers for CV selection. This work can enrich the theory and application of the evaluation issue of information and communication technologies and multicriteria analysis.Item Evaluating smart grid investment drivers and creating effective policies via a fuzzy multi-criteria approach(Renewable and Sustainable Energy Reviews, 2025-02) Dinçer, Hasan; Krishankumar, Raghunathan; Yüksel, Serhat; Ecer, FatihIt is critical to determine which factors impact more smart grid investments and which smart grid investment policy is more suitable for renewable energy projects. Nonetheless, a limited amount of research has focused on this topic, meaning a new study is needed to fill this gap and aid in making decisions under ambiguities. Thus, this research proposes a novel fuzzy group decision-making framework. Twelve drivers are examined through the fuzzy weighted decision-making trial and evaluation laboratory (F–DEMATEL–W) methodology. Subsequently, four smart grid investment policies are ranked using fuzzy weighted aggregated sum product assessment (F–WASPAS). Hence, one of the novelties of this research is the proposal of a robust decision-making tool named F–DEMATEL–W–WASPAS. Other novelties are: (i) the importance of the indicators/criteria is methodically determined by considering pairwise interactions and weights of experts; (ii) both individualistic expert-driven weight vector and cumulative weight vector of indicators are determined; (iii) alternative policies are ranked with minimum decision parameters; (iv) drivers that are crucial for the effectiveness of smart grid investment are determined with their causal relationship, and (v) smart grid investment policies are ranked reliably. The findings demonstrate that cyber security, sufficient legal procedures, and financial viability are the foremost drivers to increase the effectiveness of smart grid investments. Moreover, encouraging sustainable energy production using financial incentives is the foremost policy, followed by exchanging surplus electricity for the system owners. The work may contribute to the ongoing discussion on designing smart grid investment policies for renewable energy projects.Item TRIZ-driven assessment of sector-wise investment decisions in renewable energy projects through a novel integrated q-ROF-DEMATEL-SRP model(Energy, 2025-01-01) Yüksel, Serhat; Ecer, Fatih; Krishankumar, Raghunathan; Dinçer, Hasan; Gökalp, YaşarNecessary actions should be taken to improve renewable energy investments to minimize the carbon emission problem. In this process, the most significant determinants should be identified for some reasons, such as using human and financial resources more effectively. However, there are limited studies in the literature that prioritize the analysis of these items. This situation can be accepted as a missing gap in the literature. Accordingly, this study evaluates sector-wise investment decisions in renewable energy projects. To do so, a novel integrated q-rung orthopair fuzzy set (q-ROFS) decision-making model has been generated. Firstly, the weights of the theory of the solution of inventive problems (TRIZ)-driven criteria are computed via the q-ROF decision-making trial and evaluation laboratory (DEMATEL) methodology. The second stage of the proposed model consists of selecting the most appropriate investment alternatives with the help of the q-ROFS-based simple ranking process (q-ROF SRP). The main contribution of this study is that key sector-wise investment decisions in renewable energy projects can be identified by establishing a novel decision-making model. The main superiority of the proposed model is that the DEMATEL method is extended to the q-ROFS context to determine the weights of the factors. With the help of this issue, uncertainties and subjective randomness in the decision-making process can be minimized. In addition to this situation, causal directions between these indicators can be taken into consideration for this condition. The findings indicate that possible extension with modularity is the most critical indicator for this situation. Similarly, resource efficiency is also found to be the most influencing item. In addition to them, the ranking results demonstrate that waste-to-energy technologies and energy storage systems are the most critical investment alternatives.