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Öğe Sustainable route selection of petroleum transportation using a type-2 neutrosophic number based ITARA-EDAS model(Elsevier, 2023) Simi?, Vladimir; Milovanovi?, Branko; Panteli?, Strahinja; Pamu?ar, Dragan; Tirkolaee, Erfan BabaeeAccidents occurring in the process of petroleum transportation have dire consequences for the population and environment. In this study, the sustainable route selection problem has been addressed from the multi-criteria decision-making (MCDM) perspective for the first time. The study aims to help planning authorities find the most sustainable route for petroleum transportation by introducing both practical and methodological evaluation frameworks for solving this global problem. Firstly, twelve key decision-making criteria were elaborated to provide a practical framework for the authorities. Secondly, we introduced an advanced decision-making tool based on the integration of the Indifference Threshold-based Attribute Ratio Analysis (ITARA) method and the Evaluation based on Distance from Average Solution (EDAS) method under the type-2 neutrosophic number (T2NN) environment. The T2NN-ITARA method was formulated to determine the semi-objective importance of the key decision-making criteria. The T2NN-EDAS method was developed to rank alternative routes and reveal the most sustainable solution. This research offers real-world guidelines for selecting the most sustainable transportation route for petroleum derivatives using a case study of the city of Belgrade. It is recommended to direct dangerous goods vehicles towards the outskirts of the city. Finally, the introduced T2NN-ITARA-EDAS model can be used to solve other complex MCDM problems. © 2022 Elsevier Inc.Öğe Brain tumor segmentation of MRI images: a comprehensive review on the application of artificial intelligence tools(Elsevier, 2023) Ranjbarzadeh, Ramin; Caputo, Annalina; Tirkolaee, Erfan Babaee; Jafarzadeh Ghoushchi, Saeid; Bendechache, MalikaBackground: Brain cancer is a destructive and life-threatening disease that imposes immense negative effects on patients’ lives. Therefore, the detection of brain tumors at an early stage improves the impact of treatments and increases the patients survival rates. However, detecting brain tumors in their initial stages is a demanding task and an unmet need. Methods: The present study presents a comprehensive review of the recent Artificial Intelligence (AI) methods of diagnosing brain tumors using MRI images. These AI techniques can be divided into Supervised, Unsupervised, and Deep Learning (DL) methods. Results: Diagnosing and segmenting brain tumors usually begin with Magnetic Resonance Imaging (MRI) on the brain since MRI is a noninvasive imaging technique. Another existing challenge is that the growth of technology is faster than the rate of increase in the number of medical staff who can employ these technologies. It has resulted in an increased risk of diagnostic misinterpretation. Therefore, developing robust automated brain tumor detection techniques has been studied widely over the past years. Conclusion: The current review provides an analysis of the performance of modern methods in this area. Moreover, various image segmentation methods in addition to the recent efforts of researchers are summarized. Finally, the paper discusses open questions and suggests directions for future research. © 2022 Elsevier LtdÖğe Preface to the special issue on computational performance analysis based on novel Intelligent methods: exploration and future directions in production and logistics(SCIENDO, 2022) Goli, Alireza; Tirkolaee, Erfan Babaee; Weber, Gerhard-WilhelmThis special issue of the Foundations of Computing and Decision Sciences, titled "Computational Performance Analysis based on Novel Intelligent Methods: Exploration and Future Directions in Production and Logistics", is devoted to the application of Computational Performance Analysis (CPA) for real-life phenomena. The special issue and its editorial present novel intelligent methods as they meet with various research topics in production and logistics, especially in terms of challenges, limitations and future trends. This special issue aims to bring together current progress on the CPA, organization management, and novel models and solution techniques that can contribute to a better understanding of the CPA systems and delineate useful practical strategies. Methodologically interesting and well-documented case studies are highly recommended. Additionally, the special issue covers innovative cutting-edge research methodologies and applications in the related research field.Öğe An integrated multi-criteria decision-making approach to optimize the number of leagile-sustainable suppliers in supply chains (Jun, 10.1007/s11356-022-20214-0, 2022)(SPRINGER HEIDELBERG, 2022) Darvazeh, Saeid Sadeghi; Mooseloo, Farzaneh Mansoori; Vandchali, Hadi Rezaei; Tomaskova, Hana; Tirkolaee, Erfan BabaeeNo Abstract Available.Öğe A systematic review of aggregate production planning literature with an outlook for sustainability and circularity(SPRINGER, 2022) Aydın, Nadi Serhan; Tirkolaee, Erfan BabaeeAggregate production planning (APP) is the process of determining production, inventory, and labor levels to meet demand requirements over a planning window up to 1 year. As an emerging field, sustainable APP deals with the accommodation of environmental, economic as well as social sustainability criteria into the planning period which in turn can be achieved by making use of Circular Economy principles in real-world production activities. Different types of models and solution methods have been proposed by many researchers to study the APP problem with applications to a broad spectrum of industries. Yet, there are few studies in the literature that performs a comprehensive review of existing approaches. To the best of our knowledge, this is the first study that offers a systematic review of APP research output spanning the last 50 years. We review about 200 APP papers and systematically classify them with respect to some basic properties (year, type, publisher, publication name and citations) as well as more technical aspects such as model type/structure, solution method, handling of uncertainty and extra features. The main purpose is to find out the current research landscape to link APP with Sustainable Development using digital technologies. Limitations of existing research as well as recommendations for overcoming these shortcomings in the Industry 4.0 era are also discussed. Finally, more insight is given into the APP literature focusing on the sustainability and circularity concepts as well as an outlook for future research directions.Öğe 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 WilhemThis 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.
