Yazar "Tirkolaee, Erfan Babaee" seçeneğine göre listele
Listeleniyor 1 - 20 / 113
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe Agile supply chain management based on critical success factors and most ideal risk reduction strategy in the era of industry 4.0: application to plastic industry(Springer, 2023) Korucuk, Selcuk; Tirkolaee, Erfan Babaee; Aytekin, Ahmet; Karabasevic, Darjan; Karamasa, CaglarIn the global trade economy, the degree of competition and differentiation among organizations working in the sector of Supply Chain Management (SCM) is expanding gradually. For companies, SCM operations are becoming more important than manufacturing, sales, and marketing activities, and new ways and methods are preferred for efficiency and performance. The agility (or agility phenomenon) is one of the applications that aids effective SCM operations in this context. Agile Supply Chain Management (ASCM) and the emergence of Industry 4.0 practices, in addition to offering a cost advantage and business performance improvement, are crucial in providing flexibility to companies and ensuring their survival in the era of Industry 4.0. Meanwhile, companies aim to deal with uncertainty and implement various risk-reduction strategies as part of effective SCM. In this context, the study lists the critical success factors of ASCM and selects the most appropriate risk reduction strategy for companies that manufacture and market rubber and plastic products with a corporate identity and conduct import and export business in Istanbul. For this purpose, the criteria and alternatives specified in accordance with the literature review and experts' opinions are examined using Bipolar Neutrosophic Stepwise Weight Assessment Ratio Analysis (BN-SWARA) and Bipolar Neutrosophic Technique for Order of Preference by Similarity to Ideal Solution (BN-TOPSIS) methods. The ranking results are then discussed in line with the practical implications to provide managerial insights and decision aids. Finally, the main limitations are expressed in order to delineate useful future research directions.Öğe Analyzing failures in adoption of smart technologies for medical waste management systems: a type-2 neutrosophic-based approach(Springer Link, 2021) Torkayesh, Ali Ebadi; Deveci, Muhammet; Torkayesh, Sajjad Ebadi; Tirkolaee, Erfan BabaeeMedical waste management (MWM) systems are considered among the most important urban systems nowadays. Cities in different countries prefer to transform their infrastructure based on sustainability guidelines and practices. Meanwhile, smart technologies such as Internet of Things (IoT) and blockchain are being recently used in different urban systems of cities that aim to transform into smart cities. MWM systems are one of the main targets of integrating such smart technologies to maximize economic and social profits and minimize environmental issues. However, the transformation of traditional MWM systems into smart MWM systems and the adoption of such technologies can be a very resource-consuming task. One of the possible tasks in this process can be the identification of factors that cause failure in the adoption of smart technologies. Therefore, this study proposes a multi-criteria evaluation model based on type-2 neutrosophic numbers (T2NNs) to identify factors contributing to failure in the adoption of IoT and blockchain in smart MWM systems in Istanbul, Turkey. Results of the case study indicate that training for different stakeholders, market acceptance, transparency, and professional personnel are the main factors that lead to failure in the adoption of smart technologies. Training for different stakeholders, market acceptance, transparency, and professional personnel factors obtained distance values of 0.494, 0.381, 0.375, and 0.278, respectively, against the best factor which is security and privacy. In order to validate the results of the proposed approach, a sensitivity analysis test is performed. Results of this study can be useful for governmental and private MWM and green companies that are planning to adopt IoT and blockchain within their waste management (WM) system.Öğe Answers to Comments on An integrated methodology for green human resource management in construction industry by Masoud Alimardi (https://doi.org/10.1007/s11356-023-26217-9)(Springer Heidelberg, 2023) Darvazeh, Saeid Sadeghi; Mooseloo, Farzaneh Mansoori; Aeini, Samira; Vandchali, Hadi Rezaei; Tirkolaee, Erfan Babaee[Abstract Not Available]Öğe Application of machine learning in supply chain management: a comprehensive overview of the main areas(HINDAWI LTD, 2021) Tirkolaee, Erfan Babaee; Sadeghi, Saeid; Mooseloo, Farzaneh Mansoori; Vandchali, Hadi Rezaei; Aeini, SamiraIn today's complex and ever-changing world, concerns about the lack of enough data have been replaced by concerns about too much data for supply chain management (SCM). The volume of data generated from all parts of the supply chain has changed the nature of SCM analysis. By increasing the volume of data, the efficiency and effectiveness of the traditional methods have decreased. Limitations of these methods in analyzing and interpreting a large amount of data have led scholars to generate some methods that have high capability to analyze and interpret big data. Therefore, the main purpose of this paper is to identify the applications of machine learning (ML) in SCM as one of the most well-known artificial intelligence (AI) techniques. By developing a conceptual framework, this paper identifies the contributions of ML techniques in selecting and segmenting suppliers, predicting supply chain risks, and estimating demand and sales, production, inventory management, transportation and distribution, sustainable development (SD), and circular economy (CE). Finally, the implications of the study on the main limitations and challenges are discussed, and then managerial insights and future research directions are given.Öğe Appointment scheduling problem under fairness policy in healthcare services: fuzzy ant lion optimizer(Elsevier, 2022) Ala, Ali; Simic, Vladimir; Pamucar, Dragan; Tirkolaee, Erfan BabaeeThis study addresses the application of the Integer Linear Programming technique for the patient Appointment Scheduling Problem (ASP). In this research, we propose a Mixed-Integer Linear Programming (MILP) model to formulate the problem and treat patients admitted to hospitals and stay in a queue based on their general health status (urgent or regular patients). Moreover, the ASP has two main objectives that often provide early patient admissions. The first objective is based on fairness policy as an essential factor in the healthcare service to help minimize patient waiting time. The second one is to maximize the efficiency of healthcare services in line with patients’ satisfaction. Moreover, we have addressed the Fuzzy Ant Lion Optimization (FALO) strategy and Non-dominated Sorting Genetic Algorithm II (NSGA-II) are utilized to compare and solve the resulting multi-objective ASP. As the application of the model, fairness policy is analyzed in scenario 1 using FALO, and in scenario 2, NSGA-II is applied. The performances of the solution algorithms are then tested using datasets of a big regional hospital in Shanghai. The outcomes indicate potential advantages of implementing the presented approach. In particular, the suggested FALO increases the fairness and patients’ satisfaction by more than 80% while reducing the waiting times by 50% within the basic appointment scheduling system. © 2022 Elsevier LtdÖğe Assessing and selecting sustainable refrigerated road vehicles in food logistics using a novel multi-criteria group decision-making model(Elsevier Science Inc, 2024) Gorcun, Omer Faruk; Tirkolaee, Erfan Babaee; Kucukonder, Hande; Gargf, Chandra PrakashIn recent years, food loss and waste (FLW) have become an essential issue at the top of the international community's agenda. Since more people are afflicted by this problem every day, the global population would be forced into poverty and starvation without finding an immediate solution. Therefore, in order to decrease FLW, well-designed and sustainable food and cold supply chains (FCSCs) are needed. Additionally, refrigerated transportation systems can be crucial in developing sustainable supply chains. According to some empirical research, the technological capabilities of reefer vehicles or trailers differ significantly. Thus, selecting the reefer vehicle is a complex decision-making problem and selecting appropriate reefer vehicles may have a critical role in constructing successful supply chain systems and reducing food waste and loss. The current research proposes an efficient, robust and practical decision-making framework that can overcome uncertainties to tackle this decision-making problem. The managerial and strategic implications of the study also aid in decreasing FLW and restructuring FSC for industrial context and support to the UN's sustainable development goals (SDGs). Later, an exhaustive sensitivity analysis was conducted to examine the developed model's validity and application, confirming the model's robustness and dependability.Öğe An augmented Tabu search algorithm for the green inventory-routing problem with time windows(Elsevier B.V., 2021) Alinaghian, Mahdi; Tirkolaee, Erfan Babaee; Dezaki, Zahra Kaviani; Hejazi, Seyed Reza; Ding, WeipingTransportation allocates a significant proportion of Gross Domestic Product (GDP) to each country, and it is one of the largest consumers of petroleum products. On the other hand, many efforts have been made recently to reduce Greenhouse Gas (GHG) emissions by vehicles through redesigning and planning transportation processes. This paper proposes a novel Mixed-Integer Linear Programming (MILP) mathematical model for Green Inventory-Routing Problem with Time Windows (GIRP-TW) using a piecewise linearization method. The objective is to minimize the total cost including fuel consumption cost, driver cost, inventory cost and usage cost of vehicles taking into account factors such as the volume of vehicle load, vehicle speed and road slope. To solve the problem, three meta-heuristic algorithms are designed including the original and augmented Tabu Search (TS) algorithms and Differential Evolution (DE) algorithm. In these algorithms, three heuristic methods of improved Clarke-Wright algorithm, improved Push-Forward Insertion Heuristic (PFIH) algorithm and heuristic speed optimization algorithm are also applied to deal with the routing structure of the problem. The performance of the proposed solution techniques is analyzed using some well-known test problems and algorithms in the literature. Furthermore, a statistical test is conducted to efficiently provide the required comparisons for large-sized problems. The obtained results demonstrate that the augmented TS algorithm is the best method to yield high-quality solutions. Finally, a sensitivity analysis is performed to investigate the variability of the objective function.Öğe A bi-level decision-making system to optimize a robust-resilient-sustainable aggregate production planning problem(Pergamon-Elsevier Science Ltd, 2023) Tirkolaee, Erfan Babaee; Aydin, Nadi Serhan; Mahdavi, IrajThis study introduces a sustainable-robust aggregate production planning (APP) problem taking into account workforce productivity, outsourcing option and supplier resilience. In this regard, a bi-level decision-making system is designed using multi-attribute decision-making (MADM) and multi-objective decision-making (MODM) models, respectively. In the MADM section, a hybrid method called BWM-WASPAS-Neutrosophic based on bestworst method (BWM) and weighted aggregated sum-product assessment (WASPAS) under type-2 neutrosophic number (T2NN) is utilized to investigate resilient supplier selection. To design the MODM section, a multiobjective mixed-integer linear programming (MILP) model is suggested which is then treated with the help of weighted goal programming (WGP) method. The multi-objective model honors sustainable development goals as it aims not only to minimize total cost and maximize weighted purchasing from suppliers, but also to minimize negative environmental impacts simultaneously. Robust optimization (RO) technique is then implemented to the MODM model to address the demand uncertainty. In order to validate the suggested methodology, a real case study from the literature is examined. The obtained results reveal the efficiency of our decision-making system in finding the optimal policy in less than 1 s where sensitivity analyses also contribute to practical managerial implications. Finally, it is revealed that the total weighted purchase from suppliers has the highest sensitivity to the conservatism levels, which determine the extent to which our uncertain demand parameter can deviate from its nominal value.Öğe Big data-driven cognitive computing system for optimization of social media analytics(Ieee-Inst Electrical Electronics Engineers Inc, 2020) Sangaiah, Arun Kumar; Goli, Alireza; Tirkolaee, Erfan Babaee; Ranjbar-Bourani, Mehdi; Pandey, Hari Mohan; Zhang, WeizheThe integration of big data analytics and cognitive computing results in a new model that can provide the utilization of the most complicated advances in industry and its relevant decision-making processes as well as resolving failures faced during big data analytics. In E-projects portfolio selection (EPPS) problem, big data-driven decision-making has a great importance in web development environments. EPPS problem deals with choosing a set of the best investment projects on social media such that maximum return with minimum risk is achieved. To optimize the EPPS problem on social media, this study aims to develop a hybrid fuzzy multi-objective optimization algorithm, named as NSGA-III-MOIWO encompassing the non-dominated sorting genetic algorithm III (NSGA-III) and multi-objective invasive weed optimization (MOIWO) algorithms. The objectives are to simultaneously minimize variance, skewness and kurtosis as the risk measures and maximize the total expected return. To evaluate the performance of the proposed hybrid algorithm, the data derived from 125 active E-projects in an Iranian web development company are analyzed and employed over the period 2014-2018. Finally, the obtained experimental results provide the optimal policy based on the main limitations of the system and it is demonstrated that the NSGA-III-MOIWO outperforms the NSGA-III and MOIWO in finding efficient investment boundaries in EPPS problems. Finally, an efficient statistical-comparative analysis is performed to test the performance of NSGA-III-MOIWO against some well-known multi-objective algorithms.Öğe A Bioinspired Test Generation Method Using Discretized and Modified Bat Optimization Algorithm(Mdpi, 2024) Arasteh, Bahman; Arasteh, Keyvan; Kiani, Farzad; Sefati, Seyed Salar; Fratu, Octavian; Halunga, Simona; Tirkolaee, Erfan BabaeeThe process of software development is incomplete without software testing. Software testing expenses account for almost half of all development expenses. The automation of the testing process is seen to be a technique for reducing the cost of software testing. An NP-complete optimization challenge is to generate the test data with the highest branch coverage in the shortest time. The primary goal of this research is to provide test data that covers all branches of a software unit. Increasing the convergence speed, the success rate, and the stability of the outcomes are other goals of this study. An efficient bioinspired technique is suggested in this study to automatically generate test data utilizing the discretized Bat Optimization Algorithm (BOA). Modifying and discretizing the BOA and adapting it to the test generation problem are the main contributions of this study. In the first stage of the proposed method, the source code of the input program is statistically analyzed to identify the branches and their predicates. Then, the developed discretized BOA iteratively generates effective test data. The fitness function was developed based on the program's branch coverage. The proposed method was implemented along with the previous one. The experiments' results indicated that the suggested method could generate test data with about 99.95% branch coverage with a limited amount of time (16 times lower than the time of similar algorithms); its success rate was 99.85% and the average number of required iterations to cover all branches is 4.70. Higher coverage, higher speed, and higher stability make the proposed method suitable as an efficient test generation method for real-world large software.Öğe Blood supply chain network design with lateral freight: A robust possibilistic optimization model(Pergamon-Elsevier Science Ltd, 2024) Ala, Ali; Simic, Vladimir; Bacanin, Nebojsa; Tirkolaee, Erfan BabaeeThe blood supply chain stands out as a crucial component within a healthcare system, which can significantly improve efficiency and save the health system's costs. This paper presents a multi-objective blood supply chain network design problem that aims to reduce the cost of establishing fixed and temporary facilities, transferring blood products, and the amount of shortage. In order to address the shortfall and boost adaptability, lateral freight across hospitals is suggested due to the uncertainty in supply and demand. A novel robust possibilistic mixed-integer linear programming method is proposed in this work in order to deal with distribution and locational decisions. Two well-known solution approaches of lexicographic and Torabi-Hassini methods are then utilized to treat the multi-objectiveness of the robust possibilistic optimization model. Lateral freight between various blood supply chain demands significantly affects load balancing, declining both delivery time and costs. According to the obtained outcomes, the overall delivery time and total cost decrease by 10% and 15%, respectively. Moreover, it is revealed that the lexicographic approach outperforms the Torabi-Hassini method in this research.Öğe Brain tumor segmentation based on optimized convolutional neural network and improved chimp optimization algorithm(Pergamon-Elsevier Science Ltd, 2024) Ranjbarzadeh, Ramin; Zarbakhsh, Payam; Caputo, Annalina; Tirkolaee, Erfan Babaee; Bendechache, MalikaReliable and accurate brain tumor segmentation is a challenging task even with the appropriate acquisition of brain images. Tumor grading and segmentation utilizing Magnetic Resonance Imaging (MRI) are necessary steps for correct diagnosis and treatment planning. There are different MRI sequence images (T1, Flair, T1ce, T2, etc.) for identifying different parts of the tumor. Due to the diversity in the illumination of each brain imaging modality, different information and details can be obtained from each input modality. Therefore, by using various MRI modalities, the diagnosis system is capable of finding more unique details that lead to a better segmentation result, especially in fuzzy borders. In this study, to achieve an automatic and robust brain tumor segmentation framework using four MRI sequence images, an optimized Convolutional Neural Network (CNN) is proposed. All weight and bias values of the CNN model are adjusted using an Improved Chimp Optimization Algorithm (IChOA). In the first step, all four input images are normalized to find some potential areas of the existing tumor. Next, by employing the IChOA, the best features are selected using a Support Vector Machine (SVM) classifier. Finally, the best-extracted features are fed to the optimized CNN model to classify each object for brain tumor segmentation. Accordingly, the proposed IChOA is utilized for feature selection and optimizing Hyperparameters in the CNN model. The experimental outcomes conducted on the BRATS 2018 dataset demonstrate superior performance (Precision of 97.41 %, Recall of 95.78 %, and Dice Score of 97.04 %) compared to the existing frameworks.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 Breast tumor localization and segmentation using machine learning techniques: overview of datasets, findings, and methods(Elsevier, 2023) Ranjbarzadeh, Ramin; Dorosti, Shadi; Jafarzadeh Ghoushchi, Saeid; Caputo, Annalina; Tirkolaee, Erfan Babaee; Ali, Sadia Samar; Arshadi, Zahra; Bendechache, MalikaThe Global Cancer Statistics 2020 reported breast cancer (BC) as the most common diagnosis of cancer type. Therefore, early detection of such type of cancer would reduce the risk of death from it. Breast imaging techniques are one of the most frequently used techniques to detect the position of cancerous cells or suspicious lesions. Computer-aided diagnosis (CAD) is a particular generation of computer systems that assist experts in detecting medical image abnormalities. In the last decades, CAD has applied deep learning (DL) and machine learning approaches to perform complex medical tasks in the computer vision area and improve the ability to make decisions for doctors and radiologists. The most popular and widely used technique of image processing in CAD systems is segmentation which consists of extracting the region of interest (ROI) through various techniques. This research provides a detailed description of the main categories of segmentation procedures which are classified into three classes: supervised, unsupervised, and DL. The main aim of this work is to provide an overview of each of these techniques and discuss their pros and cons. This will help researchers better understand these techniques and assist them in choosing the appropriate method for a given use case. © 2022 Elsevier LtdÖğe Business process optimization for trauma planning(Elsevier Science Inc, 2023) Tomaskova, Hana; Tirkolaee, Erfan Babaee; Raut, Rakesh DulichandManagers play a critical role in considering their organization's needs, goals, and objectives when making decisions. However, most written material is produced before or after the issue it addresses, requiring managers to have prior knowledge to apply it correctly. We contend that fundamental business process analysis and straightforward reengineering can be used to the procedures outlined in the documentation to improve decision-making processes. This approach is illustrated through a study focusing on trauma planning. Process models were created from the trauma planning documentation to assess the effectiveness of the time process. Procedures were then redesigned, increasing efficiency while maintaining the time for partial activities. By using this approach, managers can ensure that their organization's objectives are met while improving efficiency and productivity. Our study emphasizes the importance of considering the effectiveness of existing procedures and reengineering them where necessary.Öğe Cell formation and layout design using genetic algorithm and TOPSIS: A case study of Hydraulic Industries State Company(Public Library Science, 2024) Dhayef, Dhulfiqar Hakeem; Al-Zubaidi, Sawsan S. A.; Al-Kindi, Luma A. H.; Tirkolaee, Erfan BabaeeCell formation (CF) and machine cell layout are two critical issues in the design of a cellular manufacturing system (CMS). The complexity of the problem has an exponential impact on the time required to compute a solution, making it an NP-hard (complex and non-deterministic polynomial-time hard) problem. Therefore, it has been widely solved using effective meta-heuristics. The paper introduces a novel meta-heuristic strategy that utilizes the Genetic Algorithm (GA) and the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) to identify the most favorable solution for both flexible CF and machine layout within each cell. GA is employed to identify machine cells and part families based on Grouping Efficiency (GE) as a fitness function. In contrast to previous research, which considered grouping efficiency with a weight factor (q = 0.5), this study utilizes various weight factor values (0.1, 0.3, 0.7, 0.5, and 0.9). The proposed solution suggests using the TOPSIS technique to determine the most suitable value for the weighting factor. This factor is critical in enabling CMS to design the necessary flexibility to control the cell size. The proposed approach aims to arrange machines to enhance GE, System Utilization (SU), and System Flexibility (SF) while minimizing the cost of material handling between machines as well as inter- and intracellular movements (TC). The results of the proposed approach presented here show either better or comparable performance to the benchmark instances collected from existing literature.Öğe Circular economy application in designing sustainable medical waste management systems(Springer, 2022) Tirkolaee, Erfan Babaee; Goli, Alireza; Mirjalili, SeyedaliNo Abstract AvailableÖğe Circular Economy Practices in the Context of Emerging Economies(Mdpi, 2024) Ali, Sadia Samar; Weber, Gerhard-Wilhelm; Tirkolaee, Erfan Babaee; Goli, Alireza[Abstract Not Available]Öğe A closed-loop supply chain configuration considering environmental impacts: a self-adaptive NSGA-II algorithm(Springer Link, 2022) Babaeinesami, A.; Tohidi, H.; Ghasemi, P.; Goodarzian, F.; Tirkolaee, Erfan BabaeeConfiguration of a supply chain network is a critical issue that contributes to choose the best combination for a set of facilities in order to attain an effective and efficient supply chain management (SCM). Designing a closed-loop distribution network of products is an important field in supply chain network design, which offers a potential factor for reducing costs and improving service quality. In this research, the question concerns a closed-loop supply chain (CLSC) network design considering suppliers, assembly centers, retailers, customers, collection centers, refurbishing centers, disassembly centers and disposal centers. It aims to design a distribution network based on customers’ needs in order to simultaneously minimize the total cost and total CO2 emission. To tackle the complexity of the problem, a self-adaptive non-dominated sorting genetic algorithm II (NSGA-II) algorithm is designed, which is then evaluated against the ?-constraint method. Furthermore, the performance of the algorithm is then enhanced using the Taguchi design method to tune its parameters. The results indicate that the solution time of the self-adaptive NSGA-II approach performs better than the epsilon constraint method. In terms of the self-adaptive NSGA-II algorithm, the average number of Pareto solutions (NPS) for small and medium-sized problems is 6.2 and 11, respectively. The average mean ideal distance (MID) for small and medium-sized problems is 2.54 and 5.01, respectively. Finally, the average maximum spread (MS) for small and medium-sized problems is 3100.19 and 3692.446, respectively. The findings demonstrate that the proposed self-adaptive NSGA-II is capable of generating efficient Pareto solutions. Moreover, according to the results obtained from sensitivity analysis, it is revealed that with increasing the capacity of distribution centers, the amount of shortage of products decreases. Moreover, as the demand increases, the number of established retailers rises. The number of retailers is increasing to some extent to establish 7 retailers. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Öğe A cluster-based stratified hybrid decision support model under uncertainty: sustainable healthcare landfill location selection(Springer, 2022) Tirkolaee, Erfan Babaee; Torkayesh, A.E.Nowadays, healthcare waste management has become one of the significant environmental, health, and social problems. Due to population and urbanization growth and an increase in healthcare waste disposals according to the growing number of diseases and pandemics like COVID-19, disposal of healthcare waste has become a critical issue. Authorities in big cities require reliable decision support systems to empower them to make strategic decisions to provide safe disposal methods with a prospective vision. Since inappropriate healthcare waste management systems would definitely bring up dangerous environmental, social, health, and economic issues for every city. Therefore, this paper attempts to address the landfill location selection problem for healthcare waste using a novel decision support system. Novel decision support model integrates K-means algorithms with Stratified Best-Worst Method (SBWM) and a novel hybrid MARCOS-CoCoSo under grey interval numbers. The proposed decision support system considers waste generate rate in medical centers, future unforeseen but potential events, and uncertainty in experts’ opinion to optimally locate required landfills for safe and economical disposal of dangerous healthcare waste. To investigate the feasibility and applicability of the proposed methodology, a real case study is performed for Mazandaran province in Iran. Our proposed methodology could efficiently deal with 79 medical centers within 4 clusters addressing 9 criteria to prioritize candidate locations. Moreover, the sensitivity analysis of weight coefficients is carried out to evaluate the results. Finally, the efficiency of the methodology is compared with several well-known methods and its high efficiency is demonstrated. Results recommend adherence to local rules and regulations, and future expansion potential as the top two criteria with importance values of 0.173 and 0.164, respectively. Later, best location alternatives are determined for each cluster of medical centers. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
