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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 Assessing the viability of blockchain technology in renewable energy supply chains: A consolidation framework(Elsevier Ltd., 2025) Babaei, Ardavan; Babaee Tirkolaee, Erfan; Ali, Sadia SamarThe growing focus on sustainability has propelled heightened scrutiny toward energy supply chains. Blockchain technology stands poised to revolutionize these chains, introducing stability-enhancing factors. Nevertheless, like other nascent technologies, blockchain's integration, while laden with advantages, poses challenges across technical, stakeholder, environmental, and governmental domains within the energy supply chain. Consequently, a thorough assessment of blockchain's applicability in supply chains is imperative for informed decision-making among supply chain managers. However, disparate decision-making methodologies often yield divergent outcomes. Given the substantial costs involved, particularly in capital, adopting blockchain technology in the energy supply chain demands precision. To address this complexity, this study develops a consolidation framework grounded in efficiency, risk, and uncertainty considerations to harmonize the diverse evaluations of blockchain adoption viability in renewable energy supply chains. Employing a data-driven optimization model in conjunction with an importance determination model, we assess and prioritize the impact of blockchain challenges individually. Subsequently, a fuzzy tri-objective optimization model is utilized to amalgamate findings from preceding evaluations, determining the ultimate significance of blockchain challenges. These models are then validated through a case study focusing on the electricity supply chain, affirming the pivotal role of investment costs and blockchain deployment as primary hurdles in utilizing blockchain within renewable energy supply chains. © 2025 Elsevier LtdÖğe Country-level assessment of COVID-19 performance: A cluster-based MACONT-CRITIC analysis(Elsevier ltd, 2025) Amiri, Amirreza Salehi; Babaei, Ardavan; Khedmati, MajidCOVID-19, a highly contagious respiratory virus, emerged in Wuhan in December 2019, leading to a global health emergency and subsequent pandemic declaration. Despite preventive measures, millions have been diagnosed and millions more have lost their lives, highlighting the urgent need for efficient diagnostics and effective interventions. This study presents a comprehensive framework based on integrated machine learningdecision making (ML-MCDM) to assess and compare the performance of countries during the COVID-19. The aim is to evaluate the performance of countries and identify the effective strategies for controlling the pandemic. The framework introduces a new criterion entitled 'Resilience' which aims to assess a country's capability to address peak diseases by identifying the occurrence of peaks and calculating the duration between the peak and the return to a normal state. Then, it employs K-Means clustering to group countries based on their performance indicators. The countries are then ranked within each cluster using the CRITIC-MACONT framework. The present study introduces a novel approach by integrating MACONT and CRITIC methodologies, marking the first instance of such integration. Additionally, the incorporation of machine learning techniques enhances their proficiency in effectively ranking the alternatives. The results of the analysis, conducted until March 2023, using the COVID-19 dataset, demonstrate that four clusters effectively evaluate the performance of countries and, the 'Resilience' criterion emerges as the most significant among the evaluated criteria. Based on the results, the proposed framework effectively ranks the countries and provides valuable insights for pandemic control strategies.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 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 A decision support framework to evaluate the sustainability performance of urban road transportation(Springer Heidelberg, 2023) Babaei, Ardavan; Khedmati, Majid; Jokar, Mohammad Reza Akbari; Tirkolaee, Erfan BabaeeThis study proposes a decision support framework (DSF) based on two data envelopment analysis (DEA) models in order to evaluate the urban road transportation of countries for sustainable performance management during different years. The first model considers different years independently while the second model, which is a type of network model, takes into account all the years integrated. A multi-objective programming model under two types of uncertainties is then developed to solve the proposed DEA models based on a revised multi-choice goal programming (GP) approach. The efficiency scores are measured based on the data related to several major European countries and the factors including the level of freight and passenger transportation, level of greenhouse gas emissions, level of energy consumption, and road accidents which are addressed as the main evaluation factors. Eventually, the two proposed models are compared in terms of interpretation and final achievements. The results reveal that the efficiency scores of countries are different under deterministic/uncertain conditions and according to the structure of the evaluation model. Furthermore, efficiency changes are not necessarily the same as productivity changes. The high interpretability (up to 99.6%) of the models demonstrates the reliability of DSF for decision-making stakeholders in the transport sector. Furthermore, a set of managerial analyses is conducted based on different parameters of the performance evaluation measures for these countries including the productivity changes during the period under consideration, resilience of the countries, detection of the benchmark countries, ranking of different countries, and detection of the patterns for improving the transportation system.Öğe Designing an integrated blockchain-enabled supply chain network under uncertainty(Nature Portfolio, 2023) Babaei, Ardavan; Khedmati, Majid; Jokar, Mohammad Reza Akbari; Tirkolaee, Erfan BabaeeWith the development of communication infrastructure, the design of supply chains has changed significantly. Blockchain technology, as one of the most cutting-edge technologies, can promote transparency among members of the supply chain network. To the best of our knowledge, this is the first study that tries to develop a novel bi-objective optimization model to integrate the transparency resulting from the use of blockchain for designing a three-level supply chain network. The first objective function is to minimize total cost while the second objective function seeks to maximize transparency based on the application of blockchain technology. Moreover, it is worth noting that it is the first attempt to investigate the role of a blockchain model under stochastic conditions. The bi-objectiveness and stochastic nature of the proposed model are then treated using Fuzzy Goal Programming (FGP) and Chance-Constrained programming (CCP) approaches, respectively. To tackle the problem, an improved Branch and Efficiency (B&E) algorithm is developed by incorporating transparency along with cost and service. The impacts of blockchain exclusively through transparency (Case 1) or through transparency, cost, and benefits (Case 2) in Supply Chain Design (SCD) are compared. The results demonstrated that the first case has less computational complexity and better scalability, while the second case has more transparency, less congestion, and more security. As one of the main implications, supply chain managers who are focused on cost minimization as well as transparency maximization are advised to take into account the trade-off between featuring costs and benefits of blockchain technology.Öğe Efficiency-sustainability models to assess blockchain adoption strategies with uncertainty in the oil and gas sector(Springer Science and Business Media B.V., 2024) Babaei, Ardavan; Tirkolaee, Erfan Babaee; Anka, FerzatThe Oil and Gas (O&G) supply chain, vital for energy delivery, faces challenges such as excessive paperwork, limited transparency, and sustainability issues due to conventional governance methods. This study develops a decision support framework for evaluating blockchain deployment strategies in the sector, focusing on cost, profit, and income. The framework evaluates blockchain strategies under both deterministic and non-deterministic conditions using Data Envelopment Analysis (DEA) models to assess cost-efficiency, profit-efficiency, and income-efficiency. The analysis provides a comprehensive view of the evaluation landscape, aimed at enhancing supply chain managers' decision-making. Application of the framework to the Norwegian O&G industry revealed notable differences in outcomes between cost-efficiency and profit-efficiency models. The cost-efficiency model favored a single-use strategy, while the profit-efficiency model preferred a substitution strategy. Additionally, the study found that strategies' effectiveness varied under deterministic versus uncertain conditions, with a single-use strategy being more effective in deterministic conditions and a localization strategy in mixed conditions. Statistical analysis indicated significant variance between the cost and profit approaches, highlighting that the developed framework offers a more nuanced perspective for supply chain managers to make informed decisions. To the best of our knowledge, this research is the first attempt to simultaneously consider blockchain adoption strategies under profit, cost, income, uncertainty, and optimization models. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.Öğe Evaluating the performance of countries in COVID-19 management: A data-driven decision-making and clustering(Elsevier ltd, 2025) Meraji, Hamed; Rahimi, Danial; Babaei, Ardavan; Tirkolaee, Erfan BabaeeThe COVID-19 outbreak, first reported in Wuhan, China, spread rapidly and endangered human lives and livelihoods globally. Researchers have utilized available tools and facilities to mitigate its impact across dimensions. In this study, we propose a comprehensive, data-driven framework to evaluate periodically 168 countries' performance, considering four distinct variable categories since the advent of COVID-19. We assess and leverage four clustering methods of K-means, Gaussian Mixture Model (GMM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Spectral, as well as three Multi-Criteria Decision-Making (MCDM) approaches, including Combined Compromise Solution (COCOSO), Grey Relational Analysis (GRA), and Evaluation Based on Distance from Average Solution (EDAS) for ranking the countries. The results are analyzed thoroughly-among the examined factors, "Total Recovered", "GDP Per capita", and "Hospital Beds / 1 K" most critically impacted evaluating outcomes, while" Male Smokers", "Diabetes Prevalence", and "Cardiovascular Death Rate" are least influential. The novel metric "Medical Waste" also demonstrates more vital than 86 % of existing indicators. Moreover, the findings reveal associations between countries' development levels and their corresponding cluster assignments. For more precise analysis, we investigate the intra-cluster and inter-cluster approaches, each of which revealed countries' promotion or degradation regarding rankings within a cluster or transitions between clusters. Finally, appropriate policy-making and management strategies are presented to enhance countries' preparedness for potential future outbreaks based on the results.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 Innovative supply chain network design with two-step authentication and environmentally-friendly blockchain technology(Springer, 2024) Babaei, Ardavan; Tirkolaee, Erfan Babaee; Ali, Sadia SamarBlockchain Technology (BT) has the potential to revolutionize supply chain management by providing transparency, but it also poses significant environmental and security challenges. BT consumes energy and emits carbon gases, affecting its adoption in Supply Chains (SCs). The substantial energy demand of blockchain networks contributes to carbon emissions and sustainability risks. Moreover, for secure and reliable transactions, mutual authentication needs to be established to address security concerns raised by SC managers. This paper proposes a tri-objective optimization model for the simultaneous design of the SC-BT network, considering a two-step authentication process. The model considers transparency caused by BT members, emissions of BT, and costs related to BT and SC design. It also takes into account uncertainty conditions for participating BT members in the SC and the range of transparency, cost, and emission targets. To solve the model, a Branch and Efficiency (B&E) algorithm equipped with BT-related criteria is developed. The algorithm is implemented in a three-level SC and produces cost-effective and environmentally friendly outcomes. However, the adoption of BT in the SC can be costly and harmful to the environment under uncertain conditions. It is worth mentioning that implementing the proposed algorithm from our article in a three-level SC case study can result in a significant cost reduction of over 16% and an emission reduction of over 13%. The iterative nature of this algorithm plays a vital role in achieving these positive outcomes.Öğe An integrated decision support system to achieve sustainable development in transportation routes with traffic flow(Springer Heidelberg, 2023) Babaei, Ardavan; Khedmati, Majid; Jokar, Mohammad Reza Akbari; Tirkolaee, Erfan BabaeeDue to the growing population and demand, transportation planning has received special importance in the context of supply chain management. One of the major challenges in transportation planning is the traffic problem. This challenge affects the safety, environmental, and efficiency factors of transportation systems. Accordingly, in this study, the routes, which are important pillars of transportation planning, are examined from the perspective of sustainability. In this regard, a novel decision support system is developed, wherein at first, some decision-making methods including Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), entropy technique, Nash equilibrium point (NEP), and data envelopment analysis (DEA) are employed to analyze and determine unstable routes. Then, a bi-level leader-follower multi-objective optimization model is developed, based on the vehicle types, to evaluate the routes at different time intervals and identify the most efficient time intervals as a traffic pattern. Finally, the proposed models are implemented in a real case study based on the freeways in Tehran. According to the main finding, it is revealed that heavier and bulkier vehicles have a greater impact on road instability.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 Performance evaluation of omni-channel distribution network configurations considering green and transparent criteria under uncertainty(MDPI, 2022) Babaei, Ardavan; Khedmati, Majid; Jokar, Mohammad Reza Akbari; Tirkolaee, Erfan BabaeeSatisfying customer demand is one of the growing concerns of supply chain managers. On the other hand, the development of internet communications has increased online demand. In addition, the COVID-19 pandemic has increased the demand for online shopping. One of the useful concepts that help to address this concern is the omni-channel strategy, which integrates online and traditional channels with the aim of improving customer service level. For this purpose, this paper proposes an algorithm for evaluating Omni-channel Distribution Network Configurations (OCDNCs). The algorithm applies an extended Data Envelopment Analysis (DEA) model to evaluate OCDNCs based on cost, service, transparency, and environmental criteria; and then, forms a consensus on the evaluation results generated according to different criteria by utilizing an uncertain optimization model. To the best of our knowledge, this is the first attempt in which such an algorithm has been employed to take into account the mentioned criteria in a model to evaluate OCDNCs. The application of the proposed models was investigated in a case study in relation to the Indian retail industry. The results show that the configuration with the most connections among its members was the most stable, robust, and efficient.Öğe Product tracing or component tracing? Blockchain adoption in a two-echelon supply chain management(Elsevier ltd, 2025) Babaei, Ardavan; Khedmati, Majid; Akbari Jokar, Mohammad Reza; Tirkolaee, Erfan BabaeeConsumer awareness of product authenticity and carbon footprint tracing are among the most remarkable reasons for adopting blockchain in the supply chain in today's world. However, the research literature has not yet examined specific ways to adopt blockchain in the supply chain. This study aims to develop a decision support tool to deal with the adoption of blockchain technology to design a two-echelon supply chain. In this regard, four specific cases for integrating supply chain and blockchain are developed based on types of tracing and block generation authority. In product tracing, green products are investigated throughout the supply chain, while in component tracing, green products are examined between the components of the supply chain. As it is necessary to record and verify the supply chain information by authorities in the blockchain network, in this work, such authorities are taken into account for both links and members of the supply chain. As far as we know, this is the first attempt to classify the various methods of adopting blockchain in Green Supply Chain Management (GSCM) and propose mathematical optimization models related to them. In this line, four Mixed-Integer Linear Programming (MILP) models with the aim of minimizing the costs related to the physical supply chain and blockchain deployment are developed for the integration of the supply chain with blockchain technology. They are treated by the Branch and Efficiency (B&E) algorithm and Simultaneous Data Envelopment Analysis (SDEA) model considering common (cost and service) and innovative (blockchain) criteria. The results showed that linkbased and component tracing models are cost-effective. In addition, the cost objective function of green product tracing is more sensitive to the number of blocks than that of component tracing. Eventually, the study provides great opportunities for decision-makers and managers to understand how to adopt blockchain in terms of supply chain network characteristics, cost, transparency, and service.Öğe Sustainable transportation planning considering traffic congestion and uncertain conditions(Pergamon-Elsevier Science Ltd, 2023) Babaei, Ardavan; Khedmati, Majid; Jokar, Mohammad Reza Akbari; Tirkolaee, Erfan BabaeeTransportation activities, especially road transportation, have a great impact on economic growth. On the other hand, sustainability is a major concern for transportation planning. In this work, a data-oriented network is developed to evaluate the sustainability of vehicle types. Then, this network is integrated with a multi-objective optimization model in order to provide the planning of a three-stage transportation problem, according to traffic congestion. Some criteria including total profit, efficiency of different vehicle types, relationship among the customers supplied by a specified retailer, risk of underestimating unmet demand, and selling price are used to determine the objective functions. The Chance-Constrained Programming (CCP) and Chebyshev Goal Pro-gramming (CGP) approaches are applied to solve the proposed integrated model. To the best of the authors' knowledge, it is the first time that traffic congestion under the conditions of simultaneous fuzzy and stochastic uncertainty has been integrated into sustainable transportation planning. In addition, the applicability and validity of the developed model are assessed on a case study. The results are then analyzed and appraised by Data Envelopment Analysis (DEA) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods. The findings prove that the components of the proposed model have a very beneficial effect on the solution, and also perform much better than the competing approaches in the literature. Two important points from the results of this paper are that (a) traffic congestion is more effective in the initial levels of the supply chain, and (b) transportation planning using efficient vehicles may reduce the desirability of the objective function values.
