Browsing by Author "Simic, Vladimir"

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Citation - WoS: 9
Citation - Scopus: 11
Evaluation of Shared Micro-Mobility Systems for Sustainable Cities by Using a Consensus-Based Fermatean Fuzzy Multiple Objective Optimization and Full Multiplicative Form
(Pergamon-elsevier Science Ltd, 2024) Saha, Abhijit; Gorcun, Omer Faruk; Pamucar, Dragan; Arya, Leena; Simic, Vladimir
In Turkey, the transportation industry's greenhouse gas (GHG) emissions increased by 147.1% between 1990 and 2019. Today, this transportation industry (i.e., freight and passenger) is among the significant contributors to greenhouse gas emissions in Turkey's megacities. Moreover, 65.43% of short-distance trips between home to work and home to school have been made by private automobiles in Istanbul and increasing concerns about environmental pollution have led practitioners to seek practical, robust, and effective solutions to reduce GHG emissions. Shared electric scooters have rapidly become popular for end-users and practitioners in megacities, depending on their valuable advantages. However, the rapid spread of micro-mobility, characterized by escooters, has also raised questions about this system's sustainability, suitability, and applicability. Thus, there are some critical and noteworthy gaps in this issue. This study investigates the factors affecting the suitable e-scooter selection for a sustainable urban transport system. Besides, it aims to develop a methodological framework for assessing the available e-scooter alternatives. For this purpose, a novel negotiation approach, a new form of the Delphi technique, was developed with the help of Fermatean fuzzy sets to identify the influential criteria. Also, the current paper presents a consensus-based MULTIMOORA (Multiple Objective Optimization on the basis of Ratio Analysis plus Full Multiplicative Form) decision-making model based on Fermatean fuzzy sets to address the appraisal problem concerning e-scooter selection. The current paper indicated that economic measures such as acquisition price and upkeep costs affect the e-scooter selection processes. In addition, an optimization model based on cross-entropy and dispersion measures is utilized to compute criteria weights. It highlighted that the costs of e-scooters are still high, and operators consider these criteria instead of the technical and operational features of the e-scooters. Finally, the validity check executed to test the robustness and trustworthiness of the model affirms the model's firmness and trustworthiness.
Citation - WoS: 8
Citation - Scopus: 7
Evaluating the Deep Learning Software Tools for Large-Scale Enterprises Using a Novel Todiffa-Mcdm Framework
(Elsevier, 2024) Gligoric, Zoran; Gorcun, Omer Faruk; Gligoric, Milos; Pamucar, Dragan; Simic, Vladimir; Kucukonder, Hande
Deep learning (DL) is one of the most promising technological developments emerging in the fourth industrial revolution era for businesses to improve processes, increase efficiency, and reduce errors. Accordingly, hierarchical learning software selection is one of the most critical decision-making problems in integrating neural network applications into business models. However, selecting appropriate reinforcement learning software for integrating deep learning applications into enterprises' business models takes much work for decision-makers. There are several reasons for this: first, practitioners' limited knowledge and experience of DL makes it difficult for decision-makers to adapt this technology into their enterprises' business model and significantly increases complex uncertainties. Secondly, according to the authors' knowledge, no study in the literature addresses deep structured learning solutions with the help of MCDM approaches. Consequently, making inferences concerning criteria that should be considered in an evaluation process is impossible by considering the studies in the relevant literature. Considering these gaps, this study presents a novel decision-making approach developed by the authors. It involves the combination of two new decision-making approaches, MAXC (MAXimum of Criterion) and TODIFFA (the total differential of alternative), which were developed to solve current decision-making problems. When the most important advantages of this model are considered, it associates objective and subjective approaches and eliminates some critical limitations of these methodologies. Besides, it has an easily followable algorithm without the need for advanced mathematical knowledge for practitioners and provides highly stable and reliable results in solving complex decision-making problems. Another novelty of the study is that the criteria are determined with a long-term negotiation process that is part of comprehensive fieldwork with specialists. When the conclusions obtained using this model are briefly reviewed, the C2 "Data Availability and Quality" criterion is the most influential in selecting deep learning software. The C7 "Time Constraints" criterion follows the most influential factor. Remarkably, prior research has overlooked the correlation between the performance of Deep Learning (DL) platforms and the quality and accessibility of data. The findings of this study underscore the necessity for DL platform developers to devise solutions to enable DL platforms to operate effectively, notwithstanding the availability of clean, high-quality, and adequate data. Finally, the robustness check carried out to test the validity of the proposed model confirms the accuracy and robustness of the results obtained by implementing the suggested model.
Citation - WoS: 4
Citation - Scopus: 6
An Interval Rough Improved Ordinal Priority Approach-Based Decision Support System To Redesign Postal and Logistics Networks
(Elsevier Sci Ltd, 2025) Pamucar, Dragan; Dobrodolac, Momcilo; Simic, Vladimir; Lazarevic, Dragan; Gorcun, Omer Faruk
Postal and logistics companies are essential subjects in the economy, providing services of the corresponding assortment for a wide range of business and private users. Service providers strive to meet the needs of users and, at the same time, make as much profit as possible. The efficiency of each of the subsystems in companies from this area significantly impacts the sustainability of postal and logistics systems. Rural areas, which are characterized by a smaller number of users and services and a low level of system efficiency, can have an additional negative impact on sustainability. As a result, optimization tasks become complex but also necessary to solve. The paper proposes an interval rough improved Ordinal Priority Approach - Power Schweiyer-Sklar Combined Compromise Solution (I-OPA - PSS'CoCoSo) methodology for prioritizing different models of solving the problem of inefficient network units. Methodological novelties are: a) A new approach for defining the lower and upper limits of interval rough numbers is proposed, which is based on nonlinear Bonferroni functions; b) The classic OPA linear model is improved through the implementation of a new concept for defining relational relationships between criteria; c) The CoCoSo method is improved through the implementation of nonlinear PSS and implementation of a novel function for the integration of aggregate strategies. The application of the interval rough IOPA - SSP'CoCoSo methodology is demonstrated through a case study on the example of a public postal operator operating in the territory of the Republic of Serbia. Since this is a system with a highly developed infrastructure and network throughout the entire country, this further implies the applicability of the methodology to smaller systems or sectors within larger companies that deal with parcel deliveries and other logistics activities. A new aggregation function is introduced to define the compromise index of the alternatives as well as eliminate the anomaly of the original function. The simulation of different scenarios is enabled depending on the degree of risk. The proposed methodology enables decision-making in conditions of incomplete and imprecise criteria values. In accordance with the aforementioned, this approach contributes to improving the accuracy of modeling expert opinions, and consequently, in making the final decision.
Citation - WoS: 1
Citation - Scopus: 2
Electric Vehicle Selection for Industrial Users Using an Interval-Valued Intuitionistic Fuzzy Copras-Based Model
(Springer, 2024) Gorcun, Omer Faruk; Simic, Vladimir; Kundu, Pradip; Ozbek, Asir; Kucukonder, Hande
According to reports from international bodies such as the World Health Organization and the United Nations, transportation is one of the leading contributors to environmental pollution and climate change. Electric vehicles present a practical solution to reducing emissions, particularly for industrial users. However, industrial users' selection of electric vehicles involves different dynamics than individual users, making it a more complex process for companies. This paper aims to evaluate the selection criteria for electric vehicle fleets among industrial users using a novel multi-criteria decision-making framework based on interval-valued intuitionistic fuzzy sets. The model assesses various factors influencing industrial users' decisions and ranks the available electric vehicle options accordingly. The results indicate that driving range, purchase price, and charging time are the most influential factors in the decision-making process. Furthermore, the findings confirm that the Tesla Model S P100D is the most suitable option for industrial users, given its superior performance in these critical criteria.
Citation - WoS: 39
Citation - Scopus: 44
Selection of the best Big Data platform using COBRAC-ARTASI methodology with adaptive standardized intervals
(Pergamon-elsevier Science Ltd, 2024) Pamucar, Dragan; Simic, Vladimir; Gorcun, Omer Faruk; Kucukonder, Hande
The advanced technologies emerging in Industry 4.0 are forcing companies in different industries to review their business models and become more compatible with advanced technological practices. While traditional business models are increasingly inadequate in the face of increasing competition, business models developed thanks to advanced technologies such as deep learning and machine learning have begun to replace them. However, developing business intelligence and intelligent applications using these technologies requires more data processing. In this context, Big Data technology is a unique instrument in providing the data businesses need to design more intelligent systems. In conclusion, the Big Data platform can significantly speed up the processes of structuring and processing the data and information generated and increase businesses' efficiency, performance, and agility. However, being a relatively new concept, the knowledge about the Big Data concept is limited, leading to several challenges for decision-makers concerning choosing the appropriate platform. Also, the number of studies on this subject is highly scarce. Hence, practitioners in various industries lack sufficient support from the research society on this issue. We could not find crisp and definite values to evaluate the BD alternatives despite comprehensive investigation. In that regard, as data, we addressed appraisals and opinions of IT professionals with vast knowledge and experience in assessment, selection, installation, and operation. We developed a novel decision-making model to evaluate and select the most proper BD platforms by processing these data. In this connection, the current investigation suggests a novel, robust, practical decision-making model for defining the combination of the weight of criteria based on pairwise comparisons of adjacently ranked criteria (COmparisons Between RAnked Criteria- COBRAC) and the ARTASI (Alternative ranking technique based on adaptive standardized intervals -ARTASI). It can handle complex ambiguities encountered in appraisal processes to address the Big Data platform selection problem. In addition, the current work developed a negotiation process quantitificated to determine the influential criteria affecting the selection of the Big Data platform. When we evaluate the outcomes of the suggested model, the most influential criterion affecting the selection of the Big Data platform is C12 "Ease of Use." in addition, the most suitable Big Data platform for large-scale enterprises has been identified as A3 Microsoft SQL Server. The proposed model and its results have been validated based on extensive sensitivity and comparison analysis. These results also offer practical and managerial implications for the industry. Although many studies indicate that installation cost and speed are the most critical factors, this research found that, unlike these studies, ease of use is the most critical factor in choosing a BD platform. In this context, the BD alternative that provides the highest ease of use can produce more efficient results and reduce complexities in collecting and processing high volumes of structured and unstructured data.
Citation - WoS: 4
Citation - Scopus: 4
Blockchain-Enabled Healthcare Supply Chain Management: Identification and Analysis of Barriers and Solutions Based on Improved Zero-Sum Hesitant Fuzzy Game Theory
(Pergamon-Elsevier Science Ltd, 2025) Razavian, Seyed Behnam; Tirkolaee, Erfan Babaee; Simic, Vladimir; Ali, Sadia Samar; Gorcun, Omer Faruk
Blockchain technology has emerged as a transformative approach in the health sector, enhancing efficiency, transparency, and security in Healthcare Supply Chain Management (HSCM). It addresses critical issues such as data privacy, traceability, and fraud reduction, providing a secure and reliable platform. However, significant barriers to its implementation must be overcome to ensure effective healthcare supply chain operations. This study proposes a two-stage decision-making model for identifying barriers and optimizing blockchain adoption solutions in HSCM under uncertainty. The first stage employs the Hesitant Fuzzy Best-Worst Method (HFBWM) to prioritize barriers. Compared to traditional methods such as Analytic Hierarchy Process (AHP), HFBWM achieves high accuracy with fewer pairwise comparisons. In the second stage, the Improved Zero-Sum Hesitant Fuzzy Game Theory (IZSHFG) model, based on the Weighted Sum Operator (WSO) under Hesitant Fuzzy Sets (HFSs), determines the optimal combination of strategies for blockchain application in HSCM. The challenges are modeled as one player and the solutions as another, with the decision matrix established using WSO under HFS. The obtained results indicate the worst-case scenario involves the simultaneous occurrence of four critical barriers: "Lack of Sufficient Knowledge about Blockchain in HSCM" (0.011217), "Lack of Access to Skilled Technical Personnel" (0.025457), "High Maintenance and Support Costs" (0.056076), and "Security Risks of Patients' Data" (0.069367). These findings highlight the need for targeted strategies to address these barriers, ensuring blockchain's successful integration into HSCM.
Evaluation of Railway Intelligent Transportation Systems to Construct Safer Railway Transport Systems with a Novel Decision-Making Model
(Elsevier Sci Ltd, 2026) Gorcun, Omer Faruk; Hussain, Abrar; Ullah, Kifayat; Pamucar, Dragan; Simic, Vladimir
While end users typically perceive rail transport as safer than other forms of transportation, it still confronts substantial threats and risks that demand meticulous management. One of the most crucial challenges in rail transport is the management of dense railway traffic on limited infrastructure. The effectiveness of this management is critical to ensuring safety and reliability. To address these challenges, integrating and adapting Railway Intelligent Transportation Systems (RITS) into railway transport systems has become essential for creating a safer and more reliable railway system. A railway system that is poorly structured and does not use advanced technology appropriately struggles to manage these risks effectively. Therefore, the integration of RITS is crucial. Decision-makers must carefully evaluate and select the most suitable RITS to ensure safety and reliability. However, since many conflicting criteria and decision factors affect the evaluation process, selecting the most appropriate RITS is a complex decision problem. This study proposes a new decision-making model by considering these requirements. In this context, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method, enhanced with Intuitionistic Fuzzy Sets and reinforced by integrating Schweizer-Sklar Hamy Mean Operators, was developed as a practical solution to address the decision-making problem. According to the research results, reliability and the use of the most advanced technology are the effective criteria that influence the selection of appropriate RITSs. In addition, A3 Aselsan, one of the key players in the intelligent transport system manufacturing industry, has been determined to be the most suitable alternative for railway transportation systems. Ultimately, extensive reality tests involving sensitivity and comparative analysis were conducted to check the robustness of the model. The analysis proves the model's soundness and practicality.