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Öğe Accelerating the integration of the metaverse into urban transportation using fuzzy trigonometric based decision making(Pergamon-Elsevier Science Ltd, 2024) Deveci, Muhammet; Pamucar, Dragan; Gokasar, Ilgin; Martinez, Luis; Koppen, Mario; Pedrycz, WitoldMetaverse is defined as a fictional universe that could serve as a simulation environment of reality. Beginning in the past with games, it becomes increasingly integrated into human life as time passes. Metaverse usage is inevitable in every aspect of life. One of its potential application areas could be urban transportation. A novel fuzzy trigonometric based on the combination of the Full Consistency Method (FUCOM) and Combined Compromise Solution (CoCoSo) is proposed to rank three alternatives with twelve criteria under four major aspects: managerial, safety, user, and urban mobility. In the first stage, fuzzy FUCOM methods are used to calculate the weights of the criteria. In the second stage, the fuzzy trigonometric based CoCoSo method is applied to evaluate and rank the alternatives. The proposed model enables the nonlinear processing of complex and uncertain information using fuzzy trigonometric functions. The findings demonstrate focusing on a particular age group can make it easier to integrate the metaverse with urban transportation. The findings of this study have the potential to serve as a guide for decision-makers. The metaverse-based applications could be started by policymakers, which is a promising opportunity with potential boundaries beyond human comprehension making this statement weaker.Öğe Advantage prioritization of digital carbon footprint awareness in optimized urban mobility using fuzzy Aczel Alsina based decision making(Elsevier, 2024) Deveci, Muhammet; Gokasar, Ilgin; Pamucar, Dragan; Zaidan, Aws Alaa; Wei, Wei; Pedrycz, WitoldCity governments prioritize mobility in urban planning and policy. Greater mobility in a city leads to happier citizens. Although enhanced urban mobility is helpful, it comes with costs, notably in terms of climate change. Transportation systems that enable urban mobility often emit greenhouse gases. Cities must prioritize digital carbon footprint awareness. Cities may reduce the environmental impact of urban mobility while keeping its benefits by close monitoring and reducing the carbon footprint of digital technologies like transportation applications, ride-sharing platforms, and smart traffic control systems. The aim is to advantage prioritize three alternatives, namely doing nothing, upgrading and optimizing data centers and networks, and using renewable energy sources for data centers and networks to minimize the digital carbon footprint using the proposed decision making tool. This study consists of two stages. In the first stage, fuzzy Aczel-Alsina functions (fuzzy Aczel-Alsina weighted assessment - ALWAS method) based Ordinal Priority Approach (OPA) is proposed to find the weights of criteria. Secondly, fuzzy ALWAS Combined Compromise Solution (CoCoSo) model improved to evaluate and choose the best alternative among the three alternatives. The improved ALWAS-CoCoSo model enables flexible nonlinear processing of uncertain information and simulation of different risk levels. Besides, we proposed the improved fuzzy OPA algorithm for processing uncertain and incomplete information. The case study is provided to the decision-makers to advantage prioritize the alternatives based twelve criteria organized into four aspects, including digital carbon footprint, externalities, technical capability, and economics. The ranking results reveal that A(3) = 2.445 is the best among the three alternative, while A(1) = 1.705 is the worst alternative. The results show that the best way to reduce the digital carbon footprint is to use renewable energy sources to power data centers and networks (A(3)).Öğe An analytics approach to decision alternative prioritization for zero-emission zone logistics(Elsevier Inc., 2022) Deveci, Muhammet; Pamucar, Dragan; Gökaşar, Ilgın; Delen, Dursun; Wu, Qun; Simic, VladimirUrban freight transportation requires wise management considerations since it is one of the most challenging issues cities face to attain sustainability. To help with the challenging decision process, an integrated two-stage decision analysis approach is proposed. In the first stage, the Defining Interrelationships Between Ranked criteria (DIBR) method is used to consolidate the experts’ opinions to compute the weights of the predetermined decision criteria. In the second stage, a novel approach that integrates Combined Compromise Solution (CoCoSo) with the context of type-2 neutrosophic numbers is used to identify the most optimal management decision alternative. A case study is developed to show the viability and practicability of the proposed methodology. The results indicated that “building a logistics center (for fast and cheap delivery)” is the highest-ranked decision alternative, followed by “optimized and integrated operation of urban logistics,” and “zero-emission zone implementation,” respectively. The proposed methodology can be used as a decision analysis framework for urban city authorities while selecting the most optimal policies and related solution alternatives towards achieving and sustaining low-emission urban freight transportation. © 2022 Elsevier Inc.Öğe Appointment scheduling problem under fairness policy in healthcare services: fuzzy ant lion optimizer(Elsevier, 2022) Ala, Ali; Simic, Vladimir; Pamucar, Dragan; Tirkolaee, Erfan BabaeeThis study addresses the application of the Integer Linear Programming technique for the patient Appointment Scheduling Problem (ASP). In this research, we propose a Mixed-Integer Linear Programming (MILP) model to formulate the problem and treat patients admitted to hospitals and stay in a queue based on their general health status (urgent or regular patients). Moreover, the ASP has two main objectives that often provide early patient admissions. The first objective is based on fairness policy as an essential factor in the healthcare service to help minimize patient waiting time. The second one is to maximize the efficiency of healthcare services in line with patients’ satisfaction. Moreover, we have addressed the Fuzzy Ant Lion Optimization (FALO) strategy and Non-dominated Sorting Genetic Algorithm II (NSGA-II) are utilized to compare and solve the resulting multi-objective ASP. As the application of the model, fairness policy is analyzed in scenario 1 using FALO, and in scenario 2, NSGA-II is applied. The performances of the solution algorithms are then tested using datasets of a big regional hospital in Shanghai. The outcomes indicate potential advantages of implementing the presented approach. In particular, the suggested FALO increases the fairness and patients’ satisfaction by more than 80% while reducing the waiting times by 50% within the basic appointment scheduling system. © 2022 Elsevier LtdÖğe Assessing alternatives of including social robots in urban transport using fuzzy trigonometric operators based decision-making model(Elsevier Science Inc, 2023) Deveci, Muhammet; Pamucar, Dragan; Gokasar, Ilgin; Zaidan, Bilal Bahaa; Martinez, Luis; Pedrycz, WitoldCurrent trends point to a not-too-distant future with qualitatively advanced interactions between humans and social robots. It is critical to consider the possibility of forming meaningful social relationships with robots when defining the future of human-robot interactions, as well as studying how these interactions will evolve to the point where humans are unable to distinguish between humans and robots in urban transportation. In this study, the advantages of using social robots in urban transportation are prioritized by using a multi-criteria decisionmaking tool, which consists of two consecutive stages, namely: i) a novel fuzzy sine trigonometry based on the logarithmic method of additive weights (fuzzy ST-LMAW) that is proposed to calculate the criteria weights; ii) a nonlinear fuzzy Aczel-Alsina function based the weighted aggregate sum product assessment (fuzzy ALWASWASPAS) that is developed to select and rank the alternatives. The proposed model enables flexible nonlinear processing of complex and uncertain information encountered in real applications. A case study is developed to rank three alternatives with twelve sub-criteria grouped into four aspects using the proposed method. The results show that the most advantageous alternative is to replace people with social robots as safety drivers in level four autonomous vehicles due to their possible impact on transportation.Öğe Developing deep transfer and machine learning models of chest X-ray for diagnosing COVID-19 cases using probabilistic single-valued neutrosophic hesitant fuzzy(Pergamon-Elsevier Science Ltd, 2024) Alsattar, Hassan A.; Qahtan, Sarah; Zaidan, Aws Alaa; Deveci, Muhammet; Martinez, Luis; Pamucar, Dragan; Pedrycz, WitoldThis study presents a novel dynamic localisation-based decision (DLBD) with fuzzy weighting with zero inconsistency (FWZIC) under a probabilistic single-valued neutrosophic hesitant fuzzy set (PSVNHFS) environment to benchmark Hybrid Multi Deep Transfer and Machine Learning (HMDTML) models. The novel DLBD method is proposed to generate a dynamic localisation decision matrix based on the upper and lower boundaries and the length of the scale. The superiority of DLBD derives from its ability to manage dynamic changes with boundary value consequences. In addition, the utilization of PSVNHFS in conjunction with DLBD and FWZIC has proven to effectively address the challenges posed by vagueness, uncertainty and hesitancy in the benchmarking procedure. The proposed methodology consists of three primary three steps: i) the adaptation of 48 HMDTML models, including 4 deep transfer learning models and 12 machine learning models trained on a dataset of 936 chest Xray images obtained from both COVID-19 patients and individuals without the disease. Then, these models were evaluated based on seven evaluation criteria, and a decision matrix was proposed. ii) The development of a PSVNH-FWZIC to assign weights to the evaluation criteria. iii) The formulation of a PSVNH-DLBD for the purpose of benchmarking HMDTML models. Results of the PSVNH-FWZIC revealed that AUC and time were the most important evaluation criteria, while precision was the least important. Furthermore, the results from PSVNH-DLBD, reveal that Model M24 (Painters-Decision Tree) earned the highest rank when & lambda; = 2,3,4, 5and6, followed by Model M25 (SqueezeNet-AdaBoost) and Model M34 (DeepLoc-kNN), while Model M39 (DeepLocSVM) had the lowest rank (rank = 48) across all & lambda; values. The proposed method underwent sensitivity and comparison analyses to confirm its reliability and robustness.Öğe Evaluation of agriculture-food 4.0 supply chain approaches using Fermatean probabilistic hesitant-fuzzy sets based decision making model(Elsevier, 2023) Qahtan, Sarah; Alsattar, Hassan A.; Zaidan, A. A.; Deveci, Muhammet; Pamucar, Dragan; Delen, Dursun; Pedrycz, WitoldThe benchmarking of agri-food 4.0 supply chain (Agri4SC) falls under the multiple criteria problem in supply chain visibility (SCV) and supply chain resource integration (SCRI) for improving data analytics capabilities and achieving sustainable performance (SP). It is considered a multiple criteria decision -making (MCDM) problem due to three main concerns, namely, multiple Agri4SC evaluation criteria including the SCV, SCRI and SP criteria. These criteria have relative importance and trade-offs. Despite the tremendous efforts over the last years, none of the developed Agri4SCs have met all of the essential Agri4SC evaluation criteria. Another concern raised in the evaluation and benchmarking of the Agri4SC is the uncertainty of experts. Thus, the main contribution of this research is to propose an Agri4SC benchmarking framework in SCV and SCRI for improving data analytics capabilities and achieving SP based on an extension of the proposed Fermatean probabilistic hesitant fuzzy sets (FPHFSs) and MCDM methods. The methodology process is divided into six main parts. Firstly, an Agri4SC decision matrix is formulated based on the intersection of the Agri4SC alternatives and criteria to cover multiple Agri4SC evaluation criteria issues. Secondly, novel FPHFSs are proposed along with their operational laws, score function, accuracy function, Fermatean probabilistic hesitant fuzzy average mean operator and Fermatean probabilistic hesitant fuzzy weighted average operator. The FPHFS can encompass more sophisticated and uncertain evaluation information. Thirdly, Fermatean probabilistic hesitant fuzzy weighted zero inconsistency is formulated to assign weights to the evaluation criteria. Fourthly, the Fermatean probabilistic hesitant fuzzy decision by opinion score method (FPH-FDOSM) is formulated and used to score the alternatives that were evaluated subjectively based on SCV criteria. Fifthly, the FPH-FDOSM-based multi attributive ideal-real comparative analysis (MAIRCA) scoring method with equal probabilities is proposed to score Agri4SC alternatives that were evaluated subjectively based on weighted economic, environmental and social factors. Lastly, the MAIRCA ranking method with unequal probabilities is introduced to benchmark Agri4SC alternatives that were evaluated objectively based on the weighted subcriteria of SP and the trade-offs amongst the identified criteria. The robustness and reliability of the results are tested via sensitivity analysis and Spearman's correlation coefficient.& COPY; 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Öğe Evaluation of metaverse integration alternatives of sharing economy in transportation using fuzzy Schweizer-Sklar based ordinal priority approach(Elsevier, 2023) Pamucar, Dragan; Deveci, Muhammet; Gokasar, Ilgin; Delen, Dursun; Koppen, Mario; Pedrycz, WitoldSharing economy transportation applications reduce car ownership and single-vehicle occupancy, contributing to the region's environmental sustainability. Metaverse is a promising new technology that combines sharing economy applications with transportation networks. By combining these two approaches, authorities can improve the sustainability of sharing economy applications. This study aims to assist decision-makers and authorities by developing a multi-criterion decision-making (MCDM) model that prioritizes three sharing economybased metaverse integration alternatives, namely integrating safety measures, payment systems, and the optimization of operations in the metaverse. A novel multi-criteria framework, including fuzzy Schweizer-Sklar norms based on the Ordinal Priority Approach (OPA) to assess the metaverse integration alternatives, is developed. To rank the alternatives, non-linear processing of information based on the fuzzy Schweizer-Sklar weight assessment method (SWAS) is proposed. A case study is developed to provide a foundation for the experts' evaluations using twelve criteria, which are organized into four aspects namely, economic, user, operational, and advancement. Finally, the results indicate that the most favorable approach is optimized operations via the integration of the sharing economy into the metaverse.Öğe A fuzzy Einstein-based decision support system for public transportation management at times of pandemic(Elsevier, 2022) Deveci, Muhammet; Pamucar, Dragan; Gokaşar, Ilgın; Delen, Dursun; Martínez, LuisOptimal decision-making has become increasingly more difficult due to their inherent complexity exacerbated by uncertain and rapidly changing environmental conditions in which they are defined. Hence, with the aim of improving the uncertainty management and facilitating the weighting criteria, this paper introduces an improved fuzzy Einstein Combined Compromise Solution (CoCoSo) method- ology. Such a CoCoSo model improves previous CoCoSo proposals by using nonlinear fuzzy weighted Einstein functions for defining weighted sequences. In addition, it proposes a novel algorithm for determining the criteria weights based on the fuzzy logarithmic function, therefore it allows decision- makers a better perception of the relationship between the criteria, as it considers the relationships between adjacent criteria; high consistency of expert comparisons; and enables the definition of weighting coefficients of a larger set of criteria, without the need to cluster (group) the criteria. Nonlinear fuzzy Einstein functions implemented in the fuzzy Einstein CoCoSo methodology enable the processing of complex and uncertain information. Such characteristics contribute to the rational definition of compromise strategies and enable objective reasoning when solving real-world decision problems. The efficiency, effectiveness, and robustness of the proposed fuzzy Einstein CoCoSo model are illustrated by a case study to create a conceptual framework to evaluate and rank the prioritization of public transportation management at the time of the COVID-19 pandemic. The results reveal its good performance in determining the transportation management systems strategy.Öğe A new rough ordinal priority-based decision support system for purchasing electric vehicles(Elsevier Science Inc, 2023) Kucuksari, Sadik; Pamucar, Dragan; Deveci, Muhammet; Erdogan, Nuh; Delen, DursunThis study proposes a novel multi-criteria decision-making (MCDM) model based on a rough extension of the Ordinal Priority Approach (OPA) to determine the order of importance of users' perspectives on Electric Vehicle (EV) purchases. Unlike conventional methods that rely on predefined ranks for criteria weighting coefficients, the proposed rough OPA method employs an aggregated rough linguistic matrix, enabling a more precise and unbiased calculation of interval values. Moreover, the model addresses inherent uncertainties by incorporating nonlinear aggregation functions, accommodating decision makers' risk attitudes for flexible decision -making. To validate the model's efficacy, a large-scale post-EV test drive survey is conducted, enabling the determination of relative criterion importance. Sensitivity analysis confirms the robustness of the model, demonstrating that marginal changes in parameters do not alter the ranking order. The results unveil the significance of the reliability criterion and reveal that vehicle-related characteristics outweigh economic and environmental attributes in the decision -making process. Overall, this innovative MCDM model contributes to a more accurate and objective analysis, enhancing the understanding of users' preferences and supporting informed decision-making in EV purchases.Öğe Performance assessment of sustainable transportation in the shipping industry using a q-rung orthopair fuzzy rough sets-based decision making methodology(Pergamon-Elsevier Science Ltd, 2023) Qahtan, Sarah; Alsattar, Hassan A.; Zaidan, A. A.; Deveci, Muhammet; Pamucar, Dragan; Delen, DursunThis paper proposes a novel ship energy systems (SESs) benchmarking model for performance measurement of sustainable transportation based on the extension of q-rung orthopair fuzzy rough sets (q-ROFRS) and multi-criteria decision-making (MCDM) methods. The underlying research methodology consists of two main stages: (i) Formulation of the SES decision matrix between SESs and the sustainability, (ii) Development of a q-ROFRS and fuzzy-weighted zero-inconsistency (q-ROFRS-FWZIC) model to determine the weights of each criterion. The integrated model of the q-ROFRS and fuzzy decision by the opinion score method (q-ROFRS-FDOSM) is offered as a tool for benchmarking the SESs. Sixty-two SESs are evaluated and benchmarked according to the three layers of criteria concerning the five design alternatives. The analysis of the proposed q-ROFRS-FWZIC methodology revealed that decision support methods (C2) is the most important criterion with a weight of 0.4174, followed by gas emissions (C1.1.2) and economic criterion (C1.1.1) with weights of 0.1661 and 0.1498, respectively; and energy efficiency design index (C1.2.1) is the least important. Furthermore, the results from q-ROFRS-FDOSM reveal that SES62 is the most suitable SES followed by SES60, whereas SES37 is the least suitable. Finally, the robustness of the proposed method is assessed by conducting a sensitivity analysis.Öğe Sustainable regional rail system pricing using a machine learning-based optimization approach(Springer, 2023) Gokasar, Ilgin; Karakurt, Ahmet; Kuvvetli, Yusuf; Deveci, Muhammet; Delen, Dursun; Pamucar, DraganRegional transport pricing is indeed very vital in urban settings where the transportation network is spread out across large areas and can influence travel behavior and the sustainability of cities. Therefore, in addition to existing pricing systems, such as flat fare, distance-based fare, and zonal pricing, this study proposes a sustainable approach to regional rail system pricing using rent prices and a transportation affordability index. The proposed model aims to reduce commuters' overall travel distance in order to reduce air pollution and maintenance costs for public transportation vehicles. Rent-based pricing encourages people to rent houses in regions that shorten their travel distances and fill a gap in the literature on regional rail system pricing by dealing with the decentralization of the cities. A two-step clustering and non-linear optimization modeling approach are proposed based on face-to-face surveys with regional rail system passengers. For various clusters of stations, rent per income rates and rental-based ticket prices were obtained. Furthermore, a sensitivity analysis is conducted to evaluate different conditions of the affordability index and rent prices in the studied regions. Compared to the current pricing system, ticket revenues increased by 3.88% and 1.68% in rent-based pricing.Öğe Sustainable regional rail system pricing using a machine learning-based optimization approach (25 OCT, 10.1007/s10479-023-05603-z, 2023)(Springer, 2024) Gokasar, Ilgin; Karakurt, Ahmet; Kuvvetli, Yusuf; Deveci, Muhammet; Delen, Dursun; Pamucar, Dragan[Abstract Not Available]Öğe A Systematic Literature Review of MABAC Method and Applications: An Outlook for Sustainability and Circularity(Inst Mathematics & Informatics, 2023) Torkayesh, Ali Ebadi; Tirkolaee, Erfan Babaee; Bahrini, Aram; Pamucar, Dragan; Khakbaz, AmirMultiple Criteria Decision-Making (MCDM) is one of the most reliable and applicable decision-making tools to address real-life complex and multi-dimensional problems in accordance with the concepts of sustainable development and circular economy. Although there have been sev-eral literature reviews on several MCDM methods, there is a research gap in conducting a litera-ture review on the Multi-Attributive Border Approximation area Comparison (MABAC) as a useful technique to deal with intelligent decision-making systems. This study attempts to present a com-prehensive literature review of 117 articles on recent developments and applications of MABAC. Future outlook is provided considering challenges and current trends.
