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    Application of industry 4.0 in the procurement processes of supply chains: a systematic literature review
    (MDPI AG, 2021) Jahani, Niloofar; Sepehri, Arash; Vandchali, Hadi Rezaei; Babaee Tirkolaee, Erfan
    Abstract Author keywords Abstract The fourth industrial revolution has significantly changed the traditional way of managing supply chains. The applications of Industry 4.0 (I4.0) technologies such as the Internet of Things (IoT) and Artificial Intelligence (AI) in different processes of supply chains have assisted companies to improve their performance. Procurement can be considered a critical process in supply chain management since it can provide novel opportunities for supply chains to improve their efficiency and effectiveness. However, I4.0 applications can be costly and may not be reasonably affordable. Therefore, the benefits of implementing these technologies should be clarified for procurement managers before investing in the digitalization of the procurement process. Despite the importance of this issue, few papers have attempted to address the effects of I4.0 technologies and smart systems in procurement. To fill this gap, a Systematic Literature Review (SLR) on the applications of I4.0 technologies in procurement has been used in this study. By reviewing 70 papers through appropriate keywords, a conceptual framework is developed to classify different value propositions provided by the different applications of I4.0 technologies in procurement processes. Results reveal nine value propositions that can provide a better understanding for the procurement department to analyze the benefits of implementing the related I4.0 technologies in different activities. Finally, findings and future study opportunities are concluded.
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    Assessing the viability of blockchain technology in renewable energy supply chains: A consolidation framework
    (Elsevier Ltd., 2025) Babaei, Ardavan; Babaee Tirkolaee, Erfan; Ali, Sadia Samar
    The 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 Ltd
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    A deep learning approach for robust, multi-oriented, and curved text detection
    (SPRINGER, 2022) Ranjbarzadeh, Ramin; Jafarzadeh Ghoushchi, Saeid; Anari, Shokofeh; Safavi, Sadaf; Tataei Sarshar, Nazanin; Babaee Tirkolaee, Erfan; Bendechache, Malika
    Automatic text localization and segmentation in a normal environment with vertical or curved texts are core elements of numerous tasks comprising the identification of vehicles and self-driving cars, and preparing significant information from real scenes to visually impaired people. Nevertheless, texts in the real environment can be discovered with a high level of angles, profiles, dimensions, and colors which is an arduous process to detect. In this paper, a new framework based on a convolutional neural network (CNN) is introduced to obtain high efficiency in detecting text even in the presence of a complex background. Due to using a new inception layer and an improved ReLU layer, an excellent result is gained to detect text even in the presence of complex backgrounds. At first, four new m.ReLU layers are employed to explore low-level visual features. The new m.ReLU building block and inception layer are optimized to detect vital information maximally. The effect of stacking up inception layers (kernels with the dimension of 3 x 3 or bigger) is explored and it is demonstrated that this strategy is capable of obtaining mostly varying-sized texts further successfully than a linear chain of convolution layers (Conv layers). The suggested text detection algorithm is conducted in four well-known databases, namely ICDAR 2013, ICDAR 2015, ICDAR 2017, and ICDAR 2019. Text detection results on all mentioned databases with the highest recall of 94.2%, precision of 95.6%, and F-score of 94.8% illustrate that the developed strategy outperforms the state-of-the-art frameworks.
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    A novel two-echelon hierarchical location-allocation-routing optimization for green energy-efficient logistics systems
    (Springer, 2021) Babaee Tirkolaee, Erfan; Goli, Alireza; Mardani, Abbas
    The present paper addresses a novel two-echelon multi-product Location-Allocation-Routing problem (LARP). It also considers the integration of issues such as disruption, environmental pollution, and energy-efficient vehicles as currently critical issues in a Supply Chain Network (SCN) that includes production plants, central warehouses, and retailers. The aim of this study is to minimize the total cost, which involves costs related to the establishment, shipment processes, environmental pollution, travelling, vehicle usage, and fuel consumption, in a way to cover the total demand of retailers. The problem is NP-hard; thus, to solve it approximately, we developed Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms. The numerical analysis showed that the proposed algorithms yielded high-quality results in a short computational time where the average gaps of GWO and PSO against CPLEX are 0.78% and 0.9%, respectively. Then, a case study of a dairy factory in Iran is conducted to evaluate the applicability of the proposed methodology and find the optimal policy. Finally, a set of sensitivity analyses is carried out to suggest managerial insights and decision aids.
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    Preface
    (Springer Science and Business Media Deutschland GmbH, 2021) Molamohamadi, Zohreh; Babaee Tirkolaee, Erfan; Mirzazadeh, A.; Weber, Gerhard Wilhelm
    [No Abstract Available]
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    A robust two-echelon periodic multi-commodity RFID-based location routing problem to design petroleum logistics networks: a case study
    (Springer Science and Business Media Deutschland GmbH, 2021) Babaee Tirkolaee, Erfan; Goli, Alireza; Weber, Gerhard Wilhem
    This study proposes a robust two-echelon periodic multi-commodity Location Routing Problem (LRP) by the use of RFID which is one of the most useful utilities in the field of Internet of Things (IoT). Moreover, uncertain demands are considered as the main part to design multi-level petroleum logistics networks. The different levels of this chain contain plants, warehouse facilities, and customers, respectively. The locational and routing decisions are made on two echelons. To do so, a novel mixed-integer linear programming (MILP) model is presented to determine the best locations for the plants and warehouses and also to find the optimal routes between plant level and warehouse facilities level, for the vehicles and between warehouse facilities level and customers’ level in order to satisfy all the uncertain demands. To validate the proposed model, the CPLEX solver/GAMS software is employed to solve several problem instances. These problems are analyzed with different uncertain conditions based on the applied robust optimization technique. Finally, a case study is evaluated in Farasakou Assaluyeh Company to demonstrate the applicability of our methodology and find the optimal policy.
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    Uncovering pathways towards sustainable transportation: investigating factors influencing societal acceptance of end-of-life vehicle management in Indonesia
    (SAGE publications ltd, 2024) Sitinjak, Charli; Simic, Vladimir; Babaee Tirkolaee, Erfan; Bacanin, Nebojsa; Simanullang, Wiyanti Fransisca; Fajar, Rizqon
    Addressing the critical environmental challenge of end-of-life vehicle (ELV) management in Indonesia's transportation industry, this study investigates the complex interplay between societal factors and technical adoption. We use a comprehensive survey and path analysis to investigate the relationships between demographic characteristics (gender, age, income and education) and ELV acceptance, revealing complex preferences and concerns across several population groups. Comparative analyses with previous research reveal gender-specific inequities and age-related problems, emphasizing the importance of customized measures. Our findings indicate that environmental concerns exhibit a significant positive relationship with community acceptance (path coefficient = 0.426, p < 0.001). Moreover, technological familiarity (path coefficient = 0.352, p < 0.001) and infrastructure availability (path coefficient = 0.518, p < 0.001) demonstrate noteworthy positive associations, emphasizing the role of knowledge and accessible infrastructure in promoting acceptance. Conversely, the cost of adoption exhibits a negative relationship with societal acceptance (path coefficient = -0.269, p < 0.001), suggesting potential challenges that must be addressed. Mediation analysis uncovers the mediating roles of information exposure, perceived safety, as well as convenience and accessibility. Total effects analysis validates the collective influence of crucial factors while acknowledging the potential hindrance posed by the cost of adoption. Our findings contribute to inclusive policies and initiatives for sustainable ELV practices, offering insights to address a critical environmental issue in Indonesia. Although acknowledging limitations in scope and methodology, our research advances the discourse on sustainable transportation transitions and guides strategies to promote responsible ELV management in pursuing a greener and more socially equitable future.