Karahan Kara, G. C. Yalçın, E. G. Kaygısız, Sercan Edinsel
Research and reporting on university rankings serve as valuable tools for students in evaluating universities and understanding their current performance status. Within academic literature, university rankings are established using diverse criteria across various domains, each carrying varying degrees of importance. This study adopts a multi-criteria decision-making (MCDM) perspective to analyze the academic performance ranking of Turkish Universities in 2023. Data sourced from the 2023 reports of sixty-one universities from Times Higher Education (THE) serve as the basis for this research, with THE indicators—teaching, research, citations, industry income, and international outlook—considered as primary research criteria. The Method based on the Removal Effects of Criteria (MEREC) method is employed to ascertain criterion weights, while the Weighted Euclidean Distance-Based Approach (WEDBA) method is utilized for university ranking. The study identifies "citations" as the criterion of highest significance. Notably, the top-performing universities in the ranking include Çankaya University, Fırat University, and Bahçeşehir University. Furthermore, by comparing the rankings from this study with THE university rankings, the research offers tailored suggestions for universities. This study underscores the importance of deriving criterion weights from university performance datasets rather than relying on fixed weights, facilitating a more nuanced approach to university rankings. Moreover, it presents THE performance rankings for sixty-one Turkish universities, offering valuable insights for strategic planning within the university sector.
有关大学排名的研究和报告是学生评估大学和了解其当前表现状况的重要工具。在学术文献中，大学排名是根据不同领域的不同标准确定的，每个标准的重要性各不相同。本研究采用多标准决策（MCDM）视角，分析 2023 年土耳其大学的学术表现排名。数据来源于《泰晤士高等教育》（THE）发布的六十一所大学的 2023 年报告，作为本研究的基础，THE 指标--教学、研究、论文引用、行业收入和国际视野--被视为主要研究标准。在确定标准权重时，采用了基于标准去除效应的方法（MEREC），而在大学排名时则采用了基于加权欧氏距离的方法（WEDBA）。研究发现，"引用率 "是最重要的标准。值得注意的是，排名靠前的大学包括恰尔卡亚大学、费拉特大学和巴切谢希尔大学。此外，通过将本研究的排名与 THE 大学排名进行比较，本研究还为大学提供了有针对性的建议。本研究强调了从大学绩效数据集中得出标准权重而不是依赖于固定权重的重要性，这有利于采用更加细致的方法进行大学排名。此外，本研究还介绍了土耳其六十一所大学的绩效排名，为大学部门的战略规划提供了宝贵的见解。
{"title":"Assessing the Academic Performance of Turkish Universities in 2023: A MEREC-WEDBA Hybrid Methodology Approach","authors":"Karahan Kara, G. C. Yalçın, E. G. Kaygısız, Sercan Edinsel","doi":"10.31181/jopi21202422","DOIUrl":"https://doi.org/10.31181/jopi21202422","url":null,"abstract":"Research and reporting on university rankings serve as valuable tools for students in evaluating universities and understanding their current performance status. Within academic literature, university rankings are established using diverse criteria across various domains, each carrying varying degrees of importance. This study adopts a multi-criteria decision-making (MCDM) perspective to analyze the academic performance ranking of Turkish Universities in 2023. Data sourced from the 2023 reports of sixty-one universities from Times Higher Education (THE) serve as the basis for this research, with THE indicators—teaching, research, citations, industry income, and international outlook—considered as primary research criteria. The Method based on the Removal Effects of Criteria (MEREC) method is employed to ascertain criterion weights, while the Weighted Euclidean Distance-Based Approach (WEDBA) method is utilized for university ranking. The study identifies \"citations\" as the criterion of highest significance. Notably, the top-performing universities in the ranking include Çankaya University, Fırat University, and Bahçeşehir University. Furthermore, by comparing the rankings from this study with THE university rankings, the research offers tailored suggestions for universities. This study underscores the importance of deriving criterion weights from university performance datasets rather than relying on fixed weights, facilitating a more nuanced approach to university rankings. Moreover, it presents THE performance rankings for sixty-one Turkish universities, offering valuable insights for strategic planning within the university sector.","PeriodicalId":515345,"journal":{"name":"Journal of Operations Intelligence","volume":"44 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141123191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mohammad Maydanchi, Mehrbod Ziaei, Mehrdad Mohammadi, Armin Ziaei, Mina Basiry, Fatemeh Haji, Kazhal Gharibi
Diabetes can lead to various health problems and complications, such as cardiovascular disease, kidney damage (nephropathy), eye issues, neuropathy, and foot ailments. Therefore, early diagnosis of diabetes can be immensely beneficial in preventing the development of these conditions. Utilizing machine learning is one method to detect diabetes in individuals at an early stage. In this study, we compare the performance of nine machine-learning classification models in predicting diabetes. These models include XGBoost, gradient boosting, AdaBoost, logistic regression, decision tree, KNN, perceptron, random forest, and naïve bayes. We utilize several evaluation metrics, focusing on the f1-score, area under the curve (AUC), and computational runtime. Our comparison reveals that complex tree-based models exhibit the highest f1-score and AUC, albeit with longer execution times.
糖尿病可导致各种健康问题和并发症，如心血管疾病、肾损伤（肾病）、眼部问题、神经病变和足部疾病。因此，早期诊断糖尿病对预防这些病症的发生大有裨益。利用机器学习是早期检测糖尿病的一种方法。在本研究中，我们比较了九种机器学习分类模型在预测糖尿病方面的性能。这些模型包括 XGBoost、梯度提升、AdaBoost、逻辑回归、决策树、KNN、感知器、随机森林和天真贝叶斯。我们采用了多个评估指标，重点关注 f1 分数、曲线下面积（AUC）和计算运行时间。比较结果表明，基于复杂树的模型表现出最高的 f1 分数和 AUC，尽管执行时间较长。
{"title":"A Comparative Analysis of the Machine Learning Methods for Predicting Diabetes","authors":"Mohammad Maydanchi, Mehrbod Ziaei, Mehrdad Mohammadi, Armin Ziaei, Mina Basiry, Fatemeh Haji, Kazhal Gharibi","doi":"10.31181/jopi21202421","DOIUrl":"https://doi.org/10.31181/jopi21202421","url":null,"abstract":"Diabetes can lead to various health problems and complications, such as cardiovascular disease, kidney damage (nephropathy), eye issues, neuropathy, and foot ailments. Therefore, early diagnosis of diabetes can be immensely beneficial in preventing the development of these conditions. Utilizing machine learning is one method to detect diabetes in individuals at an early stage. In this study, we compare the performance of nine machine-learning classification models in predicting diabetes. These models include XGBoost, gradient boosting, AdaBoost, logistic regression, decision tree, KNN, perceptron, random forest, and naïve bayes. We utilize several evaluation metrics, focusing on the f1-score, area under the curve (AUC), and computational runtime. Our comparison reveals that complex tree-based models exhibit the highest f1-score and AUC, albeit with longer execution times.","PeriodicalId":515345,"journal":{"name":"Journal of Operations Intelligence","volume":"105 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141124946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fermatean fuzzy sets (FFSs), as one of the representative variants of fuzzy sets, have broad application prospects. FFSs have advantages in modeling uncertain information and therefore have been widely applied. However, how to perfectly quantify the differences between FFS remains an open question. This paper introduces a new distance measure for FFSs, utilizing triangular divergence. The proposed measure is designed to rectify the limitations in the current measure, offering a more effective solution for analyzing FFSs. Moreover, we demonstrate that the proposed distance measure satisfies some basic properties and further show its effectiveness through several numerical examples. Finally, we explore the performance of the proposed distance measure in a medical diagnosis application, and the results show that the proposed distance measure can well overcome the limitations of the current measure.
{"title":"A Distance Measure of Fermatean Fuzzy Sets Based on Triangular Divergence and its Application in Medical Diagnosis","authors":"Zhe Liu","doi":"10.31181/jopi21202415","DOIUrl":"https://doi.org/10.31181/jopi21202415","url":null,"abstract":"Fermatean fuzzy sets (FFSs), as one of the representative variants of fuzzy sets, have broad application prospects. FFSs have advantages in modeling uncertain information and therefore have been widely applied. However, how to perfectly quantify the differences between FFS remains an open question. This paper introduces a new distance measure for FFSs, utilizing triangular divergence. The proposed measure is designed to rectify the limitations in the current measure, offering a more effective solution for analyzing FFSs. Moreover, we demonstrate that the proposed distance measure satisfies some basic properties and further show its effectiveness through several numerical examples. Finally, we explore the performance of the proposed distance measure in a medical diagnosis application, and the results show that the proposed distance measure can well overcome the limitations of the current measure.","PeriodicalId":515345,"journal":{"name":"Journal of Operations Intelligence","volume":"23 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140449580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To evaluate the performance of the logistics safety standard system, we propose an evaluation framework based on the performance evaluation theory. First, we construct the performance evaluation indicator of the safety standard system for logistics enterprises. It is based on the existing performance evaluation indicator and combined with the construction goal of the logistics safety standard system. Second, we combine the triangular fuzzy number and prospect theory to determine the indicator state according to the characteristics of performance evaluation indicators. Then, we use the Choquet integral, fuzzy method, and Shapley value methods to evaluate the information, which considers the interaction of indicators. Third, we use the entropy and fuzzy analytic hierarchy process to determine the expert weight. The performance evaluation information of the logistic enterprise’s safety standard system is aggregated to obtain the assessment results. Finally, the proposed framework is validated by an example analysis. The results show that the proposed framework can be used to evaluate the performance of logistics enterprise safety standard systems.
{"title":"A Framework for Assessment of Logistics Enterprises’ Safety Standardization Performance Based on Prospect Theory","authors":"Yushuo Cao, Ding Ling","doi":"10.31181/jopi21202418","DOIUrl":"https://doi.org/10.31181/jopi21202418","url":null,"abstract":"To evaluate the performance of the logistics safety standard system, we propose an evaluation framework based on the performance evaluation theory. First, we construct the performance evaluation indicator of the safety standard system for logistics enterprises. It is based on the existing performance evaluation indicator and combined with the construction goal of the logistics safety standard system. Second, we combine the triangular fuzzy number and prospect theory to determine the indicator state according to the characteristics of performance evaluation indicators. Then, we use the Choquet integral, fuzzy method, and Shapley value methods to evaluate the information, which considers the interaction of indicators. Third, we use the entropy and fuzzy analytic hierarchy process to determine the expert weight. The performance evaluation information of the logistic enterprise’s safety standard system is aggregated to obtain the assessment results. Finally, the proposed framework is validated by an example analysis. The results show that the proposed framework can be used to evaluate the performance of logistics enterprise safety standard systems.","PeriodicalId":515345,"journal":{"name":"Journal of Operations Intelligence","volume":"47 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139777948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To evaluate the performance of the logistics safety standard system, we propose an evaluation framework based on the performance evaluation theory. First, we construct the performance evaluation indicator of the safety standard system for logistics enterprises. It is based on the existing performance evaluation indicator and combined with the construction goal of the logistics safety standard system. Second, we combine the triangular fuzzy number and prospect theory to determine the indicator state according to the characteristics of performance evaluation indicators. Then, we use the Choquet integral, fuzzy method, and Shapley value methods to evaluate the information, which considers the interaction of indicators. Third, we use the entropy and fuzzy analytic hierarchy process to determine the expert weight. The performance evaluation information of the logistic enterprise’s safety standard system is aggregated to obtain the assessment results. Finally, the proposed framework is validated by an example analysis. The results show that the proposed framework can be used to evaluate the performance of logistics enterprise safety standard systems.
{"title":"A Framework for Assessment of Logistics Enterprises’ Safety Standardization Performance Based on Prospect Theory","authors":"Yushuo Cao, Ding Ling","doi":"10.31181/jopi21202418","DOIUrl":"https://doi.org/10.31181/jopi21202418","url":null,"abstract":"To evaluate the performance of the logistics safety standard system, we propose an evaluation framework based on the performance evaluation theory. First, we construct the performance evaluation indicator of the safety standard system for logistics enterprises. It is based on the existing performance evaluation indicator and combined with the construction goal of the logistics safety standard system. Second, we combine the triangular fuzzy number and prospect theory to determine the indicator state according to the characteristics of performance evaluation indicators. Then, we use the Choquet integral, fuzzy method, and Shapley value methods to evaluate the information, which considers the interaction of indicators. Third, we use the entropy and fuzzy analytic hierarchy process to determine the expert weight. The performance evaluation information of the logistic enterprise’s safety standard system is aggregated to obtain the assessment results. Finally, the proposed framework is validated by an example analysis. The results show that the proposed framework can be used to evaluate the performance of logistics enterprise safety standard systems.","PeriodicalId":515345,"journal":{"name":"Journal of Operations Intelligence","volume":"74 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139837802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Spherical fuzzy sets are an enhanced framework of the fuzzy set (FS), intuitionistic fuzzy set (IFS), Pythagorean fuzzy set (PyFS), and picture fuzzy set (PFS) with the restriction that the total square sum of the membership, indeterminacy, and non-membership degrees must be in 0 and 1. In contrast, the Z-number, a revolutionary idea that captures both the restriction and the reliability of evaluation, is more significant than fuzzy numbers in the fields of decision-making (DM), risk assessment, etc. However, there are still few and insufficient discussions of how to effectively deal with the limitations and reliability of the literature currently in existence. To address this, we first introduced the spherical fuzzy Einstein Z-numbers (SFEZNs), those elements are pairwise comparisons of the decision-makers options. It can be used effectively to make truly ambiguous judgments, reflecting the fuzzy nature, flexibility, and applicability of decision-making data. We present the spherical fuzzy Einstein Z-number weighted aggregation operators and the spherical fuzzy Einstein Z-number weighted geometric operators. We develop a model for spherical fuzzy Einstein Z-number aggregation operators. The main focus of this study is on a technique for handling the issue of multi-attribute decision-making (MADM) effectively and based on one's preferences. We also developed the algorithms for ranking the best options. Finally, we developed the relative comparison and discussion analysis to show the practicability and efficacy of the suggested operators and approaches. The study's findings and implications are discussed.
球形模糊集是模糊集（FS）、直觉模糊集（IFS）、毕达哥拉斯模糊集（PyFS）和图象模糊集（PFS）的增强框架，其限制条件是成员度、不确定度和非成员度的总平方和必须在 0 和 1 之间。相比之下，在决策（DM）、风险评估等领域，Z 数这一既能体现限制性又能体现评价可靠性的革命性思想比模糊数更有意义。然而，对于如何有效处理目前存在的文献中的限制性和可靠性问题，讨论仍然很少，也不够充分。针对这一问题，我们首先介绍了球形模糊爱因斯坦 Z 数（SFEZNs），这些元素是决策者选项的成对比较。它可以有效地用于做出真正模糊的判断，体现了决策数据的模糊性、灵活性和适用性。我们提出了球形模糊爱因斯坦 Z 数加权聚合算子和球形模糊爱因斯坦 Z 数加权几何算子。我们建立了球形模糊爱因斯坦 Z 数聚合算子模型。本研究的重点是根据个人偏好有效处理多属性决策（MADM）问题的技术。我们还开发了对最佳选项进行排序的算法。最后，我们进行了相对比较和讨论分析，以说明所建议的运算符和方法的实用性和有效性。我们还讨论了研究结果和影响。
{"title":"Approach to Multi-Attribute Decision Making Based on Spherical Fuzzy Einstein Z-Number Aggregation Information","authors":"Adan Fatima, Shahzaib Ashraf, C. Jana","doi":"10.31181/jopi21202411","DOIUrl":"https://doi.org/10.31181/jopi21202411","url":null,"abstract":"Spherical fuzzy sets are an enhanced framework of the fuzzy set (FS), intuitionistic fuzzy set (IFS), Pythagorean fuzzy set (PyFS), and picture fuzzy set (PFS) with the restriction that the total square sum of the membership, indeterminacy, and non-membership degrees must be in 0 and 1. In contrast, the Z-number, a revolutionary idea that captures both the restriction and the reliability of evaluation, is more significant than fuzzy numbers in the fields of decision-making (DM), risk assessment, etc. However, there are still few and insufficient discussions of how to effectively deal with the limitations and reliability of the literature currently in existence. To address this, we first introduced the spherical fuzzy Einstein Z-numbers (SFEZNs), those elements are pairwise comparisons of the decision-makers options. It can be used effectively to make truly ambiguous judgments, reflecting the fuzzy nature, flexibility, and applicability of decision-making data. We present the spherical fuzzy Einstein Z-number weighted aggregation operators and the spherical fuzzy Einstein Z-number weighted geometric operators. We develop a model for spherical fuzzy Einstein Z-number aggregation operators. The main focus of this study is on a technique for handling the issue of multi-attribute decision-making (MADM) effectively and based on one's preferences. We also developed the algorithms for ranking the best options. Finally, we developed the relative comparison and discussion analysis to show the practicability and efficacy of the suggested operators and approaches. The study's findings and implications are discussed.","PeriodicalId":515345,"journal":{"name":"Journal of Operations Intelligence","volume":"1533 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140466717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the rapid development of technological informatization, competition among enterprises is gradually transitioning from being "production-centered" to being "customer-centric," making service-oriented enterprises increasingly important. In addition to this, as global manufacturing advances in the process of intelligent manufacturing (IM), there is growing attention on the integration of manufacturing and the service industry, which has garnered the interest of numerous experts and scholars in the field of intelligent manufacturing services (IMS). This article combines intelligent manufacturing enterprises, intelligent service nodes, and consumers. Based on the background of intelligent manufacturing services, it collected risk factors within the smart supply chain (SSC) that connect different service nodes. These factors were evaluated by experts using a proposed linguistic q-rung orthopair fuzzy weighted averaging (Lq-ROFWA) operator in combination with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method for aggregation operations. Finally, we obtain the conclusions that the most influential factor affecting other risk factors is the inadequate identification of core customer needs; and the most important risk factor for smart supply chains oriented to intelligent manufacturing services is the leakage of customer information. After analyzing the relevant data, we will provide some theoretical and managerial implications for IM enterprises.
{"title":"Risk Factors Assessment of Smart Supply Chain in Intelligent Manufacturing Services Using DEMATEL Method With LINGUISTIC q-ROF Information","authors":"Tingjun Xu, Haolun Wang, Liangqing Feng, Yanping Zhu","doi":"10.31181/jopi21202417","DOIUrl":"https://doi.org/10.31181/jopi21202417","url":null,"abstract":"With the rapid development of technological informatization, competition among enterprises is gradually transitioning from being \"production-centered\" to being \"customer-centric,\" making service-oriented enterprises increasingly important. In addition to this, as global manufacturing advances in the process of intelligent manufacturing (IM), there is growing attention on the integration of manufacturing and the service industry, which has garnered the interest of numerous experts and scholars in the field of intelligent manufacturing services (IMS). This article combines intelligent manufacturing enterprises, intelligent service nodes, and consumers. Based on the background of intelligent manufacturing services, it collected risk factors within the smart supply chain (SSC) that connect different service nodes. These factors were evaluated by experts using a proposed linguistic q-rung orthopair fuzzy weighted averaging (Lq-ROFWA) operator in combination with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method for aggregation operations. Finally, we obtain the conclusions that the most influential factor affecting other risk factors is the inadequate identification of core customer needs; and the most important risk factor for smart supply chains oriented to intelligent manufacturing services is the leakage of customer information. After analyzing the relevant data, we will provide some theoretical and managerial implications for IM enterprises.","PeriodicalId":515345,"journal":{"name":"Journal of Operations Intelligence","volume":"16 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139592059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mouhamed Bayane Bouraima, Badi Ibrahim, Yanjun Qiu, L. J. Muhammad, Marko Radovanović
Renewable energy sources, particularly solar energy, play a vital role in achieving the 2030 agenda and sustainable development goals (SDGs) in Africa. While not explicitly addressed in the millennium development goals (MDGs), renewable energy, including solar energy technologies, indirectly contributed to MDGs targets in Africa. Nevertheless, the absence of quantifiable assessments regarding the impacts of solar energy technologies, potentially attributed to limited implementation in the developing world, motivates this paper to evaluate their potential impact in Africa. The analysis, based on a two-level criteria framework and utilizing the interval-valued Fermatean fuzzy analytical hierarchy process methodology, identifies the top three potential impacts as the reduction of CO2 emissions, monetary savings, and the diminishment of air pollution.
{"title":"Assessment of Solar Energy Technologies in Meeting the 2030 Agenda and Sustainable Development Goals Under an Interval-Valued Fermatean Fuzzy Environment","authors":"Mouhamed Bayane Bouraima, Badi Ibrahim, Yanjun Qiu, L. J. Muhammad, Marko Radovanović","doi":"10.31181/jopi21202412","DOIUrl":"https://doi.org/10.31181/jopi21202412","url":null,"abstract":"Renewable energy sources, particularly solar energy, play a vital role in achieving the 2030 agenda and sustainable development goals (SDGs) in Africa. While not explicitly addressed in the millennium development goals (MDGs), renewable energy, including solar energy technologies, indirectly contributed to MDGs targets in Africa. Nevertheless, the absence of quantifiable assessments regarding the impacts of solar energy technologies, potentially attributed to limited implementation in the developing world, motivates this paper to evaluate their potential impact in Africa. The analysis, based on a two-level criteria framework and utilizing the interval-valued Fermatean fuzzy analytical hierarchy process methodology, identifies the top three potential impacts as the reduction of CO2 emissions, monetary savings, and the diminishment of air pollution.","PeriodicalId":515345,"journal":{"name":"Journal of Operations Intelligence","volume":"5 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139597277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The maturation of the Industry 4.0 concept has brought numerous benefits to human society. However, it is not without its challenges, including neglect of worker welfare, vulnerability of global supply chains, and environmental degradation. To enhance the adaptability of the Industry 4.0 concept, Industry 5.0 has been developed. As of now, the practical implementation of Industry 5.0 has not yet been fully realized. This paper presents a novel conceptual framwork to analyze and evaluate the complex interrelationships of development factors in Industrial 5.0. Through extensive literature review and prolonged interviews with experts, three critical dimensions and their 18 key factors for the development of Industry 5.0 have been identified. Herein, a combination of Fermatean Fuzzy sets (FFs) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) has been employed to discern the interrelationships among these factors, and an Influential Network Relationship Map (INRM) has been constructed to aid decision-makers in formulating improvement strategies. The results indicate that “Sustainable Development” is the most influential dimension, with factors “Renewable Energy,” “Data-Driven Analysis Technologies,” and “Distributed Control” emerging as the most significant factors within their respective dimensions.
{"title":"Evaluating the Interrelationships of Industrial 5.0 Development Factors Using an Integration Approach of Fermatean Fuzzy Logic","authors":"Huai-Wei Lo, H. Chan, Jhe-Wei Lin, Sheng-Wei Lin","doi":"10.31181/jopi21202416","DOIUrl":"https://doi.org/10.31181/jopi21202416","url":null,"abstract":"The maturation of the Industry 4.0 concept has brought numerous benefits to human society. However, it is not without its challenges, including neglect of worker welfare, vulnerability of global supply chains, and environmental degradation. To enhance the adaptability of the Industry 4.0 concept, Industry 5.0 has been developed. As of now, the practical implementation of Industry 5.0 has not yet been fully realized. This paper presents a novel conceptual framwork to analyze and evaluate the complex interrelationships of development factors in Industrial 5.0. Through extensive literature review and prolonged interviews with experts, three critical dimensions and their 18 key factors for the development of Industry 5.0 have been identified. Herein, a combination of Fermatean Fuzzy sets (FFs) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) has been employed to discern the interrelationships among these factors, and an Influential Network Relationship Map (INRM) has been constructed to aid decision-makers in formulating improvement strategies. The results indicate that “Sustainable Development” is the most influential dimension, with factors “Renewable Energy,” “Data-Driven Analysis Technologies,” and “Distributed Control” emerging as the most significant factors within their respective dimensions.","PeriodicalId":515345,"journal":{"name":"Journal of Operations Intelligence","volume":"103 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139605843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To study the resilience and driving factors of key players in the food supply chain, this paper applies a decision model based on the Fermatean fuzzy set and improved ORESTE method. Firstly, based on the existing research on food supply chain resilience, the risk influencing factors affecting food supply chain resilience is established through a literature review. Secondly, Fermatean fuzzy sets are used to express and integrate uncertain information, calculate the membership and non-membership degrees of the factors affecting the resilience risk of the food supply chain, and then calculate the score function to obtain the weight of the influencing factors and the risk weight of alternatives. Finally, the improved ORESTE method is used to rank key players, thereby identifying key players in the food supply chain that affect resilience. The results show that transportation and logistics failures, government regulation, and diseases are the three important risk factors with the highest weight coefficient, while water system failure is the least important risk factor. Among the key players, farmers and food processors are considered the most vulnerable key players in the food supply chain, while the most resilient key players are supermarkets and food wholesalers.
{"title":"A Fermatean Fuzzy ORESTE Method For Evaluating The Resilience of the Food Supply Chain","authors":"Yi Wang, Xiao Han, Weizhong Wang","doi":"10.31181/jopi2120249","DOIUrl":"https://doi.org/10.31181/jopi2120249","url":null,"abstract":"To study the resilience and driving factors of key players in the food supply chain, this paper applies a decision model based on the Fermatean fuzzy set and improved ORESTE method. Firstly, based on the existing research on food supply chain resilience, the risk influencing factors affecting food supply chain resilience is established through a literature review. Secondly, Fermatean fuzzy sets are used to express and integrate uncertain information, calculate the membership and non-membership degrees of the factors affecting the resilience risk of the food supply chain, and then calculate the score function to obtain the weight of the influencing factors and the risk weight of alternatives. Finally, the improved ORESTE method is used to rank key players, thereby identifying key players in the food supply chain that affect resilience. The results show that transportation and logistics failures, government regulation, and diseases are the three important risk factors with the highest weight coefficient, while water system failure is the least important risk factor. Among the key players, farmers and food processors are considered the most vulnerable key players in the food supply chain, while the most resilient key players are supermarkets and food wholesalers.","PeriodicalId":515345,"journal":{"name":"Journal of Operations Intelligence","volume":" 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139624490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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