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Öğe Addressing barriers to big data implementation in sustainable smart cities: Improved zero-sum grey game and grey best-worst method(Elsevier B.V., 2024) Razavian, Behnam; Hamed, S.Masoud; Fayyaz, Maryam; Ghasemi, Peiman; Özkul, Seçkin; Tirkolaee, Erfan BabaeeThe optimization of sustainable smart cities is an essential endeavor in modern urban development, aiming to enhance the quality of life for citizens while minimizing environmental impacts. Big data plays a critical role in achieving these goals by enabling the collection, analysis, and utilization of vast amounts of information to make informed decisions. However, implementing big data in smart cities faces significant barriers, including data-sharing challenges, technical limitations, and organizational non-cooperation. Addressing these barriers is crucial for the successful deployment of smart city initiatives. We propose a novel approach to tackle these challenges using the Improved Zero-Sum Grey Game (IZSGG) theory and the Grey Best-Worst Method (G-BWM). This method comprehensively analyzes the risks and uncertainties associated with big data implementation in smart cities. By modeling the interactions between different stakeholders and their competing interests, IZSGG theory provides a framework to identify optimal strategies for data management. The G-BWM further refines these strategies by evaluating and prioritizing the various factors influencing big data utilization. Our findings reveal that the worst-case scenario for a smart city involves the simultaneous occurrence of several risks, all of which have positive values, indicating their potential to significantly disrupt smart city operations. The specific risks identified include: the sharing of data and information, the collection and recording of data, technical limitations and challenges associated with technology, the non-cooperation of organizations, and issues related to the interpretation of complex information. The technical barrier is the most significant with a weight of w(T)=0.6152, indicating its critical role compared to other barriers. Within this category, the sub-barrier of technical and technological constraints is particularly critical, with a weight of 0.39267375. © 2024 The AuthorsÖğe DeepEMPR: coffee leaf disease detection with deep learning and enhanced multivariance product representation(PeerJ Inc., 2024) Topal, Ahmet; Tunga, Burcu; Tirkolaee, Erfan BabaeePlant diseases threaten agricultural sustainability by reducing crop yields. Rapid and accurate disease identification is crucial for effective management. Recent advancements in artificial intelligence (AI) have facilitated the development of automated systems for disease detection. This study focuses on enhancing the classification of diseases and estimating their severity in coffee leaf images. To do so, we propose a novel approach as the preprocessing step for the classification in which enhanced multivariance product representation (EMPR) is used to decompose the considered image into components, a new image is constructed using some of those components, and the contrast of the new image is enhanced by applying high-dimensional model representation(HDMR)to highlight the diseased parts of the leaves. Popular convolutional neural network (CNN) architectures, including AlexNet, VGG16, and ResNet50, are evaluated. Results show that VGG16 achieves the highest classification accuracy of approximately 96%, while all models perform well in predicting disease severity levels, with accuracies exceeding 85%. Notably, the ResNet50 model achieves accuracy levels surpassing 90%. This research contributes to the advancement of automated crop health management systems. © 2024 Topal et al.Öğe A variant-informed decision support system for tackling COVID-19: a transfer learning and multi-attribute decision-making approach(PeerJ Inc., 2024) Amiri, Amirreza Salehi; Babaei, Ardavan; Simic, Vladimir; Tirkolaee, Erfan BabaeeThe global impact of the COVID-19 pandemic, characterized by its extensive societal, economic, and environmental challenges, escalated with the emergence of variants of concern (VOCs) in 2020. Governments, grappling with the unpredictable evolution of VOCs, faced the need for agile decision support systems to safeguard nations effectively. This article introduces the Variant-Informed Decision Support System (VIDSS), designed to dynamically adapt to each variant of concern’s unique characteristics. Utilizing multi-attribute decision-making (MADM) techniques, VIDSS assesses a country’s performance by considering improvements relative to its past state and comparing it with others. The study incorporates transfer learning, leveraging insights from forecast models of previous VOCs to enhance predictions for future variants. This proactive approach harnesses historical data, contributing to more accurate forecasting amid evolving COVID-19 challenges. Results reveal that the VIDSS framework, through rigorous K-fold cross-validation, achieves robust predictive accuracy, with neural network models significantly benefiting from transfer learning. The proposed hybrid MADM approach integrated approaches yield insightful scores for each country, highlighting positive and negative criteria influencing COVID-19 spread. Additionally, feature importance, illustrated through SHAP plots, varies across variants, underscoring the evolving nature of the pandemic. Notably, vaccination rates, intensive care unit (ICU) patient numbers, and weekly hospital admissions consistently emerge as critical features, guiding effective pandemic responses. These findings demonstrate that leveraging past VOC data significantly improves future variant predictions, offering valuable insights for policymakers to optimize strategies and allocate resources effectively. VIDSS thus stands as a pivotal tool in navigating the complexities of COVID-19, providing dynamic, data-driven decision support in a continually evolving landscape. Copyright 2024 Salehi Amiri et al. Distributed under Creative Commons CC-BY 4.0Öğe Cybersecurity Risks Analysis in the Hospitality Industry: A Stakeholder Perspective on Sustainable Service Systems(Multidisciplinary Digital Publishing Institute (MDPI), 2024) Karadayı Usta, SalihaThe digital transformation age introduces cybersecurity threats into the hospitality industry by increasing the exposure and vulnerability of hospitality firms’ data and systems to hackers. The hospitality industry is a diverse segment of the service sector dedicated to the provision of services in areas such as accommodation, food and beverage, travel and tourism, and recreation, including hotels, restaurants, bars, travel agencies, and theme parks. Cybersecurity risks in the hospitality industry affect the data and systems of businesses such as accommodation, food, travel, and entertainment, primarily enabled by the industry’s increasing digitization. This study aims to map the principal cybersecurity risks to the main stakeholders by proposing a novel Picture Fuzzy Sets (PFSs)-based Matrix of Alliances and Conflicts: Tactics, Objectives, and Recommendations (MACTOR) approach. The purpose here is to examine each stakeholder’s position towards handling cybersecurity attacks and estimate the uncertain nature of personal judgments of industry representatives when stating their point of view. The research aimed to extract the triggering positions of the defined cybercrime risks to reach the root cause of these risks, as the point to try to mitigate first. Thus, this paper contributes to the literature in both theoretical and practical ways by proposing a new approach and by providing real industry officials’ perspectives to solve the challenges. A hospitality practitioner can easily understand their position in this service network and take action to prevent such cybercrimes. © 2024 by the author.Öğe Crafting efficient blockchain adoption strategies under risk and uncertain environments(Elsevier, 2024) Babaei, Ardavan; Tirkolaee, Erfan Babaee; Amjadian, AlirezaRisk and uncertainty are crucial factors in decision-making processes, especially when integrating emerging technologies into essential systems like supply chains. Failing to adequately consider significant risks can disrupt supply chain operations, leading to a loss of competitive edge and causing financial and reputational damage. On the other hand, the complex nature of new technology environments, differing viewpoints among stakeholders, and the challenges of interpreting data introduce a variety of uncertainties in decision-making. In this study, we conduct a thorough examination of how blockchain strategies can be applied within supply chain frameworks. Our analysis utilizes data-driven network decision-making models that are refined to effectively manage uncertainty and risk. These models take into account aspects such as supply chain dynamics and technological factors. Importantly, we meld risk considerations with our models to tackle efficiency shortfalls, while also accounting for uncertainty caused by ambiguous and stochastic data environments. By applying and assessing these models in a real-world case study of the oil and gas industry, our research uncovers insightful observations. Specifically, we find that adopting a localization strategy presents specific risks, while a single-use strategy yields significant efficiency improvements.Öğe Lagrangian relaxation method for solving a new time-dependent production - distribution planning model(Pergamon-elsevier science, 2024) Rezaali, Zahra; Ghodratnama, Ali; Amiri-Aref, Mehdi; Tavakkoli-Moghaddam, Reza; Wassan, NiazIn today 's competitive business environment, organizations must decide how to handle the processing of their logistics equipment economically. One of the vital logistical concerns is distribution planning that is especially crucial depending on the facilities and goods being used. When it comes to perishable goods, this problem assumes double the significance. The position of the warehouse and the route of the vehicles make up the distribution planning problem. These two problems are considered concurrently and solved in the location-routing mathematical model. This paper aims to provide a production and distribution strategy to serve clients and consumers better. This research attempts to produce as efficiently as possible while providing prompt customer service, which is crucial in today 's corporate environment. This study uses three-level supply chains for perishable goods to create a supply chain network that minimizes costs. In this case, time-dependent demands refer to requests that may be made when the vehicle will arrive. Places and routes in this area are designed to meet all needs. In general, it is desirable to have factories and distribution centers in known locations, know the service 's opening and closing hours, and know how to manage the flow of materials and goods as they are stored in distribution centers and for retailers (clients). Additionally, it is desirable to route vehicle that connects the various levels of the supply chain and ensures that vehicles travel on schedule overall. First, the supply chain model represented as non-linear programming is transformed into linear programming to solve it using the CPLEX solver of GAMS commercial software and the Lagrangian relaxation (LR) method. Then, this model is verified using numerical examples and related parameters to see how it impacts the variables and the objective function 's result. The results show the capability of the LR method.Öğe Designing a Model for Optimizing Operation of Production Processes in Industry 4.0(Materials and Energy Research Center, 2024) Lajevardi M.; Nikbakht M.; Boyer O.; Tavakkoli Moghaddam, RezaIndustry 4.0 represents a shift from centralized to decentralized production, involving cyber-physical systems. This transformation aims to reduce costs and wastes by implementing timely preventive measures. Our study proposes an optimized model for production processes in Industry 4.0's cellular manufacturing systems. We employ a combination of failure mode and effects analysis (FMEA), prospect theory (PT), and Shannon entropy for this purpose. The critical failure modes are identified through a comparison with reference points. Shannon entropy is used to determine the importance of each risk factor in the FMEA method. By calculating the utility value through the prospecting method, the critical failure modes are prioritized. Specifically, FM7 and FM14 are assigned the first and second priority, respectively. To validate our approach, we evaluate its effectiveness in a real world scenario an auto parts manufacturing factory. This proposed framework empowers managers to enhance risk analysis, control factors and the impact of failure modes, and elevate safety and reliability levels in production systems. ©2024 The author(s).Öğe Crafting efficient blockchain adoption strategies under risk and uncertain environments(Elsevier B.V., 2024) Babaei, Ardavan; Tirkolaee, Erfan Babaee; Amjadian, AlirezaRisk and uncertainty are crucial factors in decision-making processes, especially when integrating emerging technologies into essential systems like supply chains. Failing to adequately consider significant risks can disrupt supply chain operations, leading to a loss of competitive edge and causing financial and reputational damage. On the other hand, the complex nature of new technology environments, differing viewpoints among stakeholders, and the challenges of interpreting data introduce a variety of uncertainties in decision-making. In this study, we conduct a thorough examination of how blockchain strategies can be applied within supply chain frameworks. Our analysis utilizes data-driven network decision-making models that are refined to effectively manage uncertainty and risk. These models take into account aspects such as supply chain dynamics and technological factors. Importantly, we meld risk considerations with our models to tackle efficiency shortfalls, while also accounting for uncertainty caused by ambiguous and stochastic data environments. By applying and assessing these models in a real-world case study of the oil and gas industry, our research uncovers insightful observations. Specifically, we find that adopting a localization strategy presents specific risks, while a single-use strategy yields significant efficiency improvements. © 2024 The AuthorsÖğe Emerging patents versus brain eating amoebae, Naegleria fowleri(Taylor and Francis Ltd., 2025) Siddiqui, Ruqaiyyah; Lloyd, David; Khan, Naveed AhmedPrimary Amoebic Meningoencephalitis (PAM) is a severe and often fatal infection caused by the free-living amoebae Naegleria fowleri. This condition typically results from exposure to contaminated warm freshwater/inadequately treated recreational water/or ablution/nasal irrigation with contaminated water. The management of PAM is hindered by the absence of effective treatment coupled with challenges in early diagnosis. This review explores emerging patents that could be utilized for the treatment, diagnosis of PAM, as well as water treatment. Recent patents from the past five years, along with research and innovations are reviewed and categorized into therapeutic agents, water treatment technologies, and diagnostic methods. It is hoped that collaboration and awareness between pharmaceutical companies, water industries, and academic institutions is essential for advancing effective strategies against this severe central nervous system pathogen. © 2025 Informa UK Limited, trading as Taylor & Francis Group.Öğe Evaluating the performance of intercity road freight transport: Double-frontier parallel network cross-efficiency model(Elsevier, 2024) Ganji, S. S.; Tirkolaee, Erfan Babaee; Jahed, RasulPerformance assessment of Intercity Road Freight Transport (IRFT) involve a complex set of considerations to ensure the efficient transportation of goods between their origins and destinations. Notably, Data Envelopment Analysis (DEA) models are often the preferred approach for conducting assessment studies that involve multiple inputs and outputs. Even though the conventional Cross-Efficiency (CE) technique addresses the limitations of DEA in producing comparable results, it still fails to consider the evaluation of internal system operations. The use of Network CE leads to more realistic results in assessing problems. It addresses not only the limitations of the DEA model in producing comparable results but also considers the performance of internal operations and system efficiency. Incorporating CE into a Parallel Network System (PNS), which is known as CE-PNS, has also a significant drawback as it neglects both the optimistic and pessimistic viewpoints based on the efficient and antiefficient frontiers simultaneously, and may therefore fail to provide a comprehensive analysis. The primary objective of this study is to propose a new double-frontier network CE that takes into account two perspectives simultaneously. Indeed, this study is the first to incorporate the concepts of CE and double-frontier DEA into a PNS to provide an effective tool for assessing Iranian IRFT. For this purpose, the Cross-Inefficiency (CIE) technique is first incorporated into the PNS, called CIE-PNS, which explores the relationship between system inefficiency and internal divisions. The CE-PNS and CIE-PNS may yield different results. Therefore, Double-Frontier CE for a PNS (DFCE-PNS) is derived by combining the efficiency results obtained from CE-PNS and CIE-PNS. This study also examines different inputs and outputs to assess the performance of Iranian IRFT. The results demonstrate that DFCE-PNS yields more comprehensive and reliable results.Öğe Evaluation and benchmarking of research-based microgrid systems using FWZIC-VIKOR approach for sustainable energy management(Elsevier Ltd, 2024) Talal, Mohammed; Tan, Michael Loong Peng; Pamucar, Dragan; Delen, Dursun; Pedrycz, Witold; Simic, VladimirMicrogrid (MG) is one of the technologies considered in the direction of providing green and sustainable energy resources for local communities. Ensuring the best performance of these MG technologies requires extensive research to provide the most efficient system. Research-based microgrids (RB-MGs) play a vital role in the development of green energy platforms, as microgrid applications vary according to different scenarios and locations. Selecting the best research-based microgrid ensures providing local communities and stakeholders with well-tested and examined MG systems. Assessing research-based microgrid systems (RB-MG) for sustainable green applications poses a challenging multi-attribute decision-making (MADM) problem. These complexities encompass the consideration of several evaluation criteria, the relative importance of these criteria, variations in data, and the inherent trade-offs and conflicts between these factors. Crisp and definite values to evaluate the research-based microgrids could not be found despite a comprehensive investigation. In this regard, appraisals and opinions of experts and professionals in providing sustainable energy with vast knowledge and experience in assessment, selection, installation, and operation were addressed as data. A novel decision-making model was developed to evaluate and select the most proper RB-MG system by processing these data. This study proposes an integrated MADM modelling approach using Fuzzy Weighted with Zero Inconsistency (FWZIC) method in conjunction with the Vlse-kriterijumska Optimizcija I Kaompromisno Resenje (VIKOR) method. The underlying process starts with constructing a decision matrix (evaluation criteria intersectioned with RB-MGs). Then, evaluation criteria are weighted using FWZIC, and the RB-MGs are ranked for each category using VIKOR. The results derived from FWZIC weights provide valuable insights. Key criteria such as 'installed power (KW)' and 'storage capacity (C3)' show notable values of 0.159 and 0.151, respectively, underscoring their importance in identifying optimal RB-MGs. These weights and alternatives were used to rank the highest RB-MG, which is LIER-CIRCE of the average PV power group, and Ormazabal from the 'Highest PV Power' group. Ormazabal obtained the lowest Qi (0.426). For the average PV power group, alternative number 7 (Atenea Centre) ranked as the best alternative with the lowest Qi among other RB-MGs in the same group (0.158107). Comparative assessments with various MCDM methods reveal strong correlations with TOPSIS and MABAC, but negative correlations with MAIRA. Additionally, there is a statistical difference in grouping by PV installed power (KW) or MPC. © 2024 Elsevier B.V.Öğe Incorporation of prospect theory into double-frontier cross-efficiency: a case study of Iranian airline(Taylor and Francis Ltd., 2025) Ganji, Seyedreza Seyedalizadeh; Tirkolaee, Erfan Babaee; Jamshidi Bandari, Samaneh; Fathi Ajirlu, ShahruzCEM is a widely recognised approach that utilises DEA to assess the efficiency of DMUs, providing policymakers with a valuable tool. However, there are certain limitations associated with conventional CEM. First, it fails to consider DMs’ subjective preferences, thereby disregarding the significance of cross-evaluations. Secondly, traditional CEM overlooks the significance of a pessimistic standpoint in making decisions. To overcome these drawbacks, the current study aims to incorporate the prospect theory into the double-frontier CEM. This theory allows for the incorporation of DMs’ preferences regarding gains and losses in cross-evaluation aggregation. The paper utilises a novel cross-evaluation aggregation approach (referred to as APC) to address DMs’ subjectivity. As a real case study, the performance of 17 Iranian airlines is examined using the novel Double-Frontier CEM based on APC, referred to as DAPC. This study evaluates Iranian airlines according to three inputs and four outputs. The results of the study revealed that DMs’ preferences had a significant impact on both viewpoints. Interestingly, the ranking outcomes for more than half of the airlines differed significantly when considering the two viewpoints. © 2025 Informa UK Limited, trading as Taylor & Francis Group.Öğe Incident duration prediction through integration of uncertainty and risk factor evaluation: A San Francisco incidents case study(Public Library of Science, 2025) Salehi, Amirreza; Babaei, Ardavan; Khedmati, MajidPredicting incident duration and understanding incident types are essential in traffic management for resource optimization and disruption minimization. Precise predictions enable the efficient deployment of response teams and strategic traffic rerouting, leading to reduced congestion and enhanced safety. Furthermore, an in-depth understanding of incident types helps in implementing preventive measures and formulating strategies to alleviate their influence on road networks. In this paper, we present a comprehensive framework for accurately predicting incident duration, with a particular emphasis on the critical role of street conditions and locations as major incident triggers. To demonstrate the effectiveness of our framework, we performed an in-depth case study using a dataset from San Francisco. We introduce a novel feature called "Risk" derived from the Risk Priority Number (RPN) concept, highlighting the significance of the incident location in both incident occurrence and prediction. Additionally, we propose a refined incident categorization through fuzzy clustering methods, delineating a unique policy for identifying boundary clusters that necessitate further modeling and testing under varying scenarios. Each cluster undergoes a Multiple Criteria Decision-Making (MCDM) process to gain deeper insights into their distinctions and provide valuable managerial insights. Finally, we employ both traditional Machine Learning (ML) and Deep Learning (DL) models to perform classification and regression tasks. Specifically, incidents residing in boundary clusters are predicted utilizing the scenarios outlined in this study. Through a rigorous analysis of feature importance using top-performing predictive models, we identify the "Risk" factor as a critical determinant of incident duration. Moreover, variables such as distance, humidity, and hour demonstrate significant influence, further enhancing the predictive power of the proposed model. © 2025 Salehi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Öğe Investigating supply chain resilience in digital car sharing enterprises: a case study from Turkey(Emerald Publishing, 2024) Karadayı Usta, Saliha; Kadaifçi, ÇiğdemPurpose: The purpose of this study is to extract factors enabling the digital car sharing enterprises' supply chain resilience (SCR), to interpret different factor prioritizations in terms of industry representatives’ assessments and specialties, and to discuss the results by applying and comparing different ranking techniques. Design/methodology/approach: To achieve the purpose, the factors were identified via an in-depth systematic literature review, and next, these factors were examined by industry representatives to gather the decision matrices, then analytic hierarchy process (AHP) and measuring attractiveness by a categorical based evaluation technique (MACBETH) were applied separately to model the decision problem, and finally the findings were interpreted with different participants’ perspectives. Findings: The findings revealed that the AHP and MACBETH provide nearly identical rankings in terms of main factors by implying the significance of the triple bottom line of sustainability. Therefore, the economic, social, and environmental dimensions of sustainability should be accomplished to obtain a resilient digital car sharing enterprise supply chain. In addition, readiness and agility are the other important factors affecting the enterprises’ resilience, and finally, although digitalization seemed to be the least important one, its sub-factor emerged at the top of the ranking list. Originality/value: Up to the authors’ knowledge, this is the first study in the literature that focuses on the SCR of car sharing companies, a particular type of digital enterprise, and uses AHP and MACBETH to examine the important factors that might affect the SCR of these companies. Practitioners should take the findings of both methods into account when evaluating the results and determine the short- and long-term strategies accordingly. © 2024, Emerald Publishing Limited.Öğe Relationships Between Clinical Psychological Depression and Employee Absenteeism(Elsevier Inc, 2025) Grifno, Kenny; Bao, Chenzhang; Russell, Craig J.; Delen, DursunPsychological depression has emerged as a global concern, leading to increased employee absenteeism and reduced productivity. Rising healthcare expenditures compound the issue, negatively impacting employees’ healthcare benefits and treatment options. Therefore, it becomes imperative for organizations to understand the efficacy of alternative healthcare benefits in addressing employee absences. Conservation of resources theory provides an analytical framework integrating diverse data sources to investigate this matter. We explored the relationship of no therapy, medication, and psychological therapies with employee absenteeism over time. Findings revealed psychological therapy exhibited greater efficacy in reducing absenteeism for depression, yielding sustained benefits throughout the episode. Conversely, the effectiveness of depression medications overall had a small short-term and no long-term relationship to absenteeism. These findings have significant implications for employers and employees, potentially leading to improved healthcare benefit offerings with concomitant reductions in employee absenteeism. © 2025 Elsevier Inc.Öğe Preemptive and non-preemptive multi-skill multi-mode resource-constrained project scheduling problems considering sustainability and energy consumption: a comprehensive mathematical model(Academic press, 2024) Shahabi-Shahmiri, Reza; Tavakkoli-Moghaddam, Reza; Dolgui, Alexandre; Mirnezami, Seyed-Ali; Ghasemi, Mohammad; Ahmadi, MahsaModern project managers cope with significant challenges to schedule and control projects considering dynamic environments, frequent uncertainties, strict project deadlines, and stricter sustainable requirements above all. Sustainability taking into account resource utilization has been recently associated with project management. Hence, this paper presents a new mixed-integer linear programming (MILP) model with two objectives for a resource-constrained project scheduling problem (RCPSP) with multiple skills and multiple modes, assuming preemptive and non-preemptive activities in an uncertain environment. Given the importance of sustainable developments in projects, the considered objectives are to maximize job opportunities and minimize project duration, resource costs, and total energy consumption. To deal with the model, an AUGNMECON2VIKOR algorithm is utilized to create Pareto solutions. In this model, project activities can be crashed by allocating extra resources. Furthermore, multi-skill resources are used to perform project activities. This study also investigates the impact of these resources on project scheduling. To deal with uncertain circumstances, a fuzzy chance-constrained programming method is employed to develop a robust possibilistic programming model. With respect to the increasing significance of sustainability in project management, this study pioneers the examination of the impact of sustainable factors on project scheduling. Finally, the proposed formulation is validated using instances from the well-known PSPLIB and MMLIB test sets. Finally, a comparison is drawn between the presented solution method considering AUGMECON2VIKOR and AUGMECON2.Öğe Benchmarking of industrial wastewater treatment processes using a complex probabilistic hesitant fuzzy soft Schweizer-Sklar prioritized-based framework(Elsevier, 2024) Saqib, Muhammad; Ashraf, Shahzaib; Farid, Hafiz Muhammad Athar; Simic, Vladimir; Kousar, MuneebaThe objective of this research work is to effectively handle the intricate and uncertain nature of decisionmaking in the context of industrial wastewater treatment. The prioritization of wastewater treatment is of utmost importance in order to save the environment, promote human health, ensure adherence to legal regulations, conserve resources, and foster overall sustainability. The demand for efficient and sustainable wastewater treatment processes is increasing globally, but selecting appropriate treatment technologies for industrial effluents is challenging due to its complex nature. Our study aims to provide a systematic framework for evaluating and selecting treatments based on efficacy, affordability, and environmental impact. Aggregation operators are one of the fundamental ideas in the framework of information fusion for addressing reallife problems. Numerous scholars have made significant contributions to the development of aggregation operators specifically designed for multi -parameter decision -making (MPDM) whenever circumstances are characterized by uncertainty. Regrettably, the current operators can only be employed within strict limits and constraints. Therefore, this research formulates novel prioritized aggregation operators that alleviate the restrictive constraints associated with the current operators. After this, we present a new methodology that combines the Schweizer-Sklar prioritized aggregation operators with a complex probabilistic hesitant fuzzy soft framework. For this purpose, we develop the averaging aggregation operators and geometric aggregation operators. Following that, we proceed to examine the theorems and properties associated with them by providing rigorous proofs. Then, we develop a novel decision -making technique with a numerical example based on the wastewater treatment process. Through this novel technique, the best process is the activated sludge process. We perform the validity tests to evaluate the feasibility and effectiveness of MPDM techniques.Öğe Optimizing energy consumption for blockchain adoption through renewable energy sources(Elsevier Ltd., 2025) Babaei, Ardavan; Tirkolaee, Erfan Babaee; Boz, EsraThe adoption of blockchain technology across various industries and systems has garnered significant attention due to its myriad benefits, leading to widespread popularity today. However, the energy-intensive nature of blockchain, attributed to extensive computations and data mining, poses substantial operational and environmental challenges, hindering its widespread acceptance. To mitigate these limitations, leveraging renewable energy sources emerges as a viable and crucial solution. These options are assessed across various dimensions including sustainable energy transfer, physical attributes, legal regulations, energy supply costs, technological infrastructure, and climatic constraints. To achieve this, we present four optimization models. Initially, three optimization models, rooted in risk aversion, fairness, and weighted sum principles, are meticulously solved. Subsequently, leveraging the insights garnered from these models, a multi-objective optimization model is developed based on Percentage Multi-Choice Goal Programming (PMCGP) method. This framework facilitates the scoring and ranking of renewable energy sources, culminating in informed decision-making. Our investigation, anchored by a case study, underscores the significant potential of utilizing blockchain technology in conjunction with wind energy. In the initial step, our models grounded in risk, optimization, and fairness concepts establish targets for the subsequent stage. Consequently, the proposed methodology offers diverse analytical capabilities tailored for supply chain managers and decision-makers. © 2024 Elsevier LtdÖğe Designing a sustainable water supply strategy through biobjective mixed-integer linear programming: a case study on gaza(ASCE-Amer soc civil engineers ASCE, 2024) Aydın, Nadi Serhan; Dawoud, Osama; Tirkolaee, Erfan BabaeePalestine is water-stressed and prone to possible shocks in water supply. However, about one-third to one-half of the delivered water in the Palestinian territories is lost in the distribution network, highlighting the necessity of a cost-effective supply policy that also minimizes corrosion and health-related risks by achieving a maximum level of water quality. There must be a decision-making system to help managers review and measure the status quo and develop optimal allocation decisions to resolve the potential problems with supply and quality as much as possible. This research presents optimal resource allocation for the drinking water supply system (DWSS) to minimize the unit cost of supply in the system and the chloride concentration of the water supplied simultaneously. A bespoke biobjective mixed-integer linear programming (BOMILP) model was developed. The weighted sum method (WSM) based on the positive ideal solution (PIS) approach is utilized to tackle the biobjectiveness of the model in a Pareto sense. The model was implemented and validated using a case study of Gaza. The results suggest that the share of desalinated water resources in the supply network will increase by about 50% in the 2030-2035 period. Furthermore, we found that the unit cost of supply is highly sensitive to a decrease in resource capacities. Finally, the average quality of water supplied can deteriorate rapidly if demand surges. The model can successfully handle the complexity of effective utilization of resources and yield optimal decisions for policymakers.Öğe Evaluating the performance of metaheuristic-tuned weight agnostic neural networks for crop yield prediction(Springer Science and Business Media Deutschland GmbH, 2024) Jovanovic, Luka; Zivkovic, Miodrag; Bacanin, Nebojsa; Dobrojevic, Milos; Simic, Vladimir; Sadasivuni, Kishor Kumar; Tirkolaee, Erfan BabaeeThis study explores crop yield forecasting through weight agnostic neural networks (WANN) optimized by a modified metaheuristic. WANNs offer the potential for lighter networks with shared weights, utilizing a two-layer cooperative framework to optimize network architecture and shared weights. The proposed metaheuristic is tested on real-world crop datasets and benchmarked against state-of-the-art algorithms using standard regression metrics. While not claiming WANN as the definitive solution, the model demonstrates significant potential in crop forecasting with lightweight architectures. The optimized WANN models achieve a mean absolute error (MAE) of 0.017698 and an R-squared (R2) score of 0.886555, indicating promising forecasting performance. Statistical analysis and Simulator for Autonomy and Generality Evaluation (SAGE) validate the improvement significance and feature importance of the proposed approach. © The Author(s) 2024.
