University of QomJournal of Engineering Management and Soft Computing3116-015811120250821E-Healthcare Improvement through the Design of a Cloud-Based Blood Supply Chain: A System Dynamics Approachطراحی و تبیین مدل شبیهسازی زنجیره تأمین خون در بستر شبکه ابری با رویکرد پویاییشناسی سیستم471366610.22091/jemsc.2025.11210.1201ENSaeedAbdolhossein ZadehDepartment of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, Tehran, Iran0009-0009-2974-9860MostafaZandiehDepartment of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, Tehran, Iran0000-0003-1209-9514AkbarAlam-TabrizDepartment of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, Tehran, IranJournal Article20240819In this paper, we adopt a system dynamics approach to examine the behavior of the blood supply chain (BSC), focusing on the key performance indicator "deviation from optimal stock coverage" and the key factors "donation utility," "donation queue," and "the number of established blood collection centers." Tracking both positive and negative deviations from the ideal inventory level is crucial for minimizing blood wastage and shortages. To achieve our objectives, we first identify the key variables, construct a causal loop diagram, and validate the model’s structure. Next, we develop a stock-and-flow diagram, run simulations, and validate the model’s behavior. Finally, we propose adopting cloud computing to enhance information sharing within the BSC, thereby improving system performance. Our findings indicate that a cloud-based BSC outperforms the conventional model in terms of the evaluated criteria.https://jemsc.qom.ac.ir/article_3666_7ebe4cc75e93d53f3a7360b0aa1ab091.pdfUniversity of QomJournal of Engineering Management and Soft Computing3116-015811120250821Improved maintenance planning of railway tracks based on onboard vibration measurements using smartphonesرویکرد بهبود یافته برنامهریزی نگهداری و تعمیرات خطوط ریلی بر اساس لرزهنگاری در داخل کابین قطار با دستگاههای تلفن همراه8148366510.22091/jemsc.2025.11229.1204ENBabakJavadiDepartment of Industrial Engineering, Faculty of Engineering, College of Farabi, University of Tehran, Iran0000-0002-5553-2501SaeedKiaahmadi. Department of Industrial Engineering, Faculty of Engineering, College of Farabi, University of Tehran, IranAmirhoseinAbasiDepartment of Industrial Engineering, Faculty of Engineering, College of Farabi, University of Tehran, IranMohammadrezaAbdaliDepartment of Industrial Engineering, Faculty of Engineering, College of Farabi, University of Tehran, IranJournal Article20240901With the increase in train speed, railway traffic has grown significantly in terms of both passenger numbers and cargo weight. Speed, weight, and rail traffic collectively impact rail track quality. The expanding rail network has accelerated the rate of track geometry changes, leading to deteriorating track conditions. Severe track geometry deviations pose significant risks, including train derailment. Consequently, railway companies invest substantial resources in regular track inspections to mitigate these risks. This article proposes a cost-effective and reliable method for continuous track geometry monitoring during every journey. By utilizing the widespread availability of smartphones, we aim to record wagon vibrations to predict rail conditions in both vertical and horizontal dimensions. This approach can drastically reduce maintenance costs by efficiently identifying areas requiring urgent attention.https://jemsc.qom.ac.ir/article_3665_bd238514a3d25e39f039291645fb7c6a.pdfUniversity of QomJournal of Engineering Management and Soft Computing3116-015811120250821Enhancing Service Recommendations in the Social Internet of Things: An Adaptive Collaborative Filtering Approach Using Friendship-Based Similarityبهبود پیشنهادهای سرویس در اینترنت اشیاء اجتماعی: یک رویکرد پالایش همخوان تطبیقی با استفاده از شباهت مبتنی بر دوستی11482366410.22091/jemsc.2025.11431.1210ENMohammadMahdianPhD Student. Department of Computer Engineering, Yazd University, Yazd, Iran.S. MojtabaMatinkhahAssistant prof. Department of Computer Engineering, Yazd University, Yazd, Iran0000-0002-3800-8396Journal Article20241005The Social Internet of Things (SIoT) integrates social interactions with IoT technology to create intelligent, connected environments. In this network, people form friendships, and objects owned by them provide services to others. As the SIoT network grows, offering personalized services tailored to individual interests becomes increasingly important. This paper examines real-world data from the city of Santander, analyzing the number of users, friendship degree distribution, and its patterns. An adaptive consonance filter algorithm based on friendship communities (A-CFA-FC) is proposed, which uses friendship relations and individual preferences to identify friendship communities based on a similarity index. The algorithm ranks and recommends services according to user interests within the SIoT environment. Results from the Santander dataset show that the proposed algorithm, using the Jaccard similarity index, detects more communities with lower time complexity and higher compactness. Compared to the baseline algorithm, it reduces root mean square error by about 17% and improves the F1 score by approximately 21%.https://jemsc.qom.ac.ir/article_3664_634482cd0777482b0f671eaf45008fdc.pdfUniversity of QomJournal of Engineering Management and Soft Computing3116-015811120250821Improving Security in Power Line Communication Networks Using OFDMA Technique in the Presence of Multiple Users and Eavesdroppersبهبود امنیت در شبکههای ارتباطی خطوط انتقال قدرت با استفاده از تکنیک دسترسی چندگانه تقسیم فرکانسی متعامد در حضور چند کاربر و شنودگر133115366310.22091/jemsc.2025.11503.1212ENMoslemForouzeshAssistant Professor, Electrical Engineering Department, Faculty of Modern Technologies Engineering,
Amol University of Special Modern Technologies, Amol, Iran0009-0002-7142-5199RezaAghaeiMSc. Student of Telecommunication Network Engineering, Faculty of Modern Technologies Engineering, Amol University
of Special Modern Technologies, Amol, IranArshiaRangamizMSc. Student of Telecommunication Network Engineering, Faculty of Modern Technologies Engineering, Amol University
of Special Modern Technologies, Amol, IranJournal Article20241028In the present era, given the rapid growth of communication technologies and the need for smart networks, novel techniques such as Power Line Communication (PLC) and Orthogonal Frequency Division Multiplexing (OFDM) have been introduced as effective solutions for improving data transmission security and reducing infrastructure costs. This paper examines communication systems based on power lines using Orthogonal Frequency Division Multiple Access (OFDMA) techniques, which play a crucial role in network management, with the aim of enhancing data transmission security and minimizing interference effects. To evaluate the proposed system model, an optimization problem is formulated and analyzed using mathematical optimization models and the NOMAD and CVX tools in MATLAB. The analysis focuses on key performance parameters, including the number of subcarriers and users, distance, data rate constraints, total power, and noise. Therefore, to analyze the proposed system model, comprehensively, we investigate the minimum required secrecy rate for users in the presence of eavesdroppers. The optimization results demonstrate a significant improvement in the performance of PLC-based communication systems and an increase in data transmission rates.https://jemsc.qom.ac.ir/article_3663_84bf98df7f886e99071577b928ace411.pdfUniversity of QomJournal of Engineering Management and Soft Computing3116-015811120250821Localization of Global Forecasting Models with a Clustering Approachمحلیسازی مدلهای پیشبینی سراسری با یک رویکرد خوشهبندی155134365810.22091/jemsc.2025.11595.1218ENHoseinAbbasimehrFaculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, IranMohammadKhodizadeh-NahariFaculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran0009-0007-7416-3100Journal Article20241105With the increasing generation of time series data, forecasting models trained on a set of time series, known as global forecasting models, outperform univariate forecasting models trained on individual series. However, the performance of global models may decrease when faced with heterogeneous data sets of time series with different lengths. In this study, a new method for localization of clustering-based global forecasting models is presented. The main steps of the proposed method include (1) extracting relevant features from each time series (2) clustering time series based on features extracted using K-Medoids and spectral clustering algorithms (3) implementing a global forecasting model using a combination of Temporal Convolution Network and its training for each cluster. To evaluate the prediction accuracy of the proposed approach, experiments were conducted on the M3 dataset that contains 1426 time series with unequal-length. The results of the experiments show the superior performance of the proposed clustering-based models compared to the baseline models and the benchmark models. The proposed model has 0.57 less error in terms of SMAPE metric.https://jemsc.qom.ac.ir/article_3658_ad972c5dfbe45972f328c616163a96b4.pdfUniversity of QomJournal of Engineering Management and Soft Computing3116-015811120250821Increasing the accuracy of predicting project time and cost by applying the effects of project risks and considering the interdependence of risks using the concept of stratificationافزایش دقت پیشبینی زمان و هزینه پروژه با إعمال اثرات مخاطرههای پروژه و درنظر گرفتن وابستگی متقابل مخاطرهها با استفاده از مفهوم طبقهبندی211156366210.22091/jemsc.2025.11594.1219ENFatemehMoazemi GoodarziDepartment of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran0009-0003-0521-3494MaryamAshrafiDepartment of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran0000-0003-0953-6381Seyed HasanGhodsypourDepartment of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran0000-0001-7893-6317Journal Article20241105Uncertainty in projects reduces the accuracy of predicting project time and cost. In this article, a forecasting model is presented that improves this accuracy while considering risk management processes and risk dependency relationships using the concept of stratification. Using the concept of stratification, all the occurrences of risks are evaluated along with their effects on the time and cost of the project. The results of the implementation of the proposed model in a dam construction project showed a 44.73% increase in actual time compared to the predicted time and a 31.13% increase in the actual cost compared to the predicted cost if risks are not considered and a 9.66% decrease in the project time and decrease 15.14% shows the project cost if response strategies are implemented, and highlights the importance of applying risk management processes in predicting project time and cost. Carrying out the sensitivity analysis of the proposed model also confirms the necessity of evaluating all classes and occurrences of project risks using the concept of stratification in the risk assessment and response stages.https://jemsc.qom.ac.ir/article_3662_a9b7e816fe8f33a32c0b9390a1eb7f48.pdfUniversity of QomJournal of Engineering Management and Soft Computing3116-015811120250821The Impact of the Blockchain-Based Approach On Smart Contracts in the Development of E-Commerce Using Data Miningتأثیر رویکرد مبتنی بر بلاکچین بر قراردادهای هوشمند در توسعه تجارت الکترونیک با استفاده از دادهکاوی236212365710.22091/jemsc.2025.11967.1238ENMohammad RezaMaranakiMaster of Science in Information Technology, Faculty of Technology and Engineering, South Tehran Branch, Islamic Azad University
University, Tehran, IranMahmoodDeypirAssociate Professor, Faculty of Computer Engineering, Shahid Sattari Aeronautical University of Science and Technology, Tehran, Iran="0000"&"-000"&RANDBETWEEN(1,4)&"-"&RANDBETWEEN(2033,9479)&"-"&RANDBETWEEN(2013,9934)Journal Article20241230A smart contract is a computer protocol for creating or improving a contract. A smart contract allows for the creation of valid transactions without the need for an intermediary. With the advent of blockchain technology, the idea of smart contracts has received more attention and has found a wide range of applications. Privacy, digital assets, and data encryption are three important factors in the benefit of blockchain-based smart contracts. This article examines the impact of a blockchain-based approach on smart contracts in the development of e-commerce using data mining. The research method is descriptive with a data mining approach and regression computation, decision trees, and neural networks. The main objective of this research is to determine the impact of blockchain on smart contracts in the development of e-commerce using data mining. The predictive power of smart contracts based on blockchain is 55%, which shows a level higher than 0.5. Therefore it can be said that the proposed model has appropriate predictive power for examining smart contracts.https://jemsc.qom.ac.ir/article_3657_d3b087033136961cefc3ea4965a518f0.pdfUniversity of QomJournal of Engineering Management and Soft Computing3116-015811120250821Identifying and ranking the critical success factors of business process reengineering in project-oriented organizationsشناسایی و رتبهبندی عوامل کلیدی موفقیت بازمهندسی فرآیندهای کسبوکار در سازمانهای پروژهمحور260237365610.22091/jemsc.2025.12000.1240ENMohammadrezaZahediDepartment of Industrial engineering, Faculty of Management and Industrial Engineering, Malek-Ashtar University of Technology, Tehran, Iran.Seyed MahdiHosseini SarkhoshDepartment of Industrial engineering, Faculty of engineering, University of Garmsar, Garmsar, IranJournal Article20241230Business Process Reengineering (BPR) is a management approach that aims to improve the organization’s performance through fundamental redesign of work processes. However, the implementation of BPR is costly, time-consuming, complex and associated with risks and requires attention to various factors. This becomes more important in project-based companies that work under conditions of uncertainty, limited resources, and high stakeholder expectations. Therefore, this study was conducted with the aim of identifying and ranking the critical success factors (CSFs) for BPR in Iranian project-oriented organizations. In this regard, first a review of the research literature was conducted and then, using interviews with experts, 10 CFSs affecting the success of BPR were identified. In the following, by applying the fuzzy DEMATEL-ANP (DANP) technique, the influence and effectiveness of these factors were analyzed and ranked in terms of importance. The research findings showed that factors such as process integration, organizational culture, change management, information technology infrastructure, leadership commitment, communication and employee participation play an important role in the success of BPR in project-oriented organizations.https://jemsc.qom.ac.ir/article_3656_cc58e646dac3d732e511a5c4d167089d.pdfUniversity of QomJournal of Engineering Management and Soft Computing3116-015811120250821Investigating the impact of our organization's business processes in the direction of agility and leaner processes with emphasis on EFQM 2020 criteriaبررسی تاثیر فرآیندهای کسب و کارسازمان در جهت چابکی و نابتر شدن فرآیندها با تاکید بر معیارهای EFQM 2020285261365510.22091/jemsc.2025.3655.1251ENShahabZiabakhshDepartment of Industrial Engineering, science and research branch, Islamic Azad University, Tehran, IranAmirAziziDepartment of Industrial Engineering, University of science and research, Tehran, Iran0000-0001-7217-9503AliTaghizadeh HeraDepartment of Industrial Engineering, parand branch, Islamic Azad University, Tehran, IranJournal Article20250131The purpose of this research is to investigate the impact of the organization's business processes in the direction of agility and leaner processes with emphasis on EFQM 2020 criteria. This research is descriptive-survey and applied. The statistical population consists of two parts: In the first part, 45 experts including senior managers and process management and organizational excellence specialists were selected. In the second part, 450 managers, experts and employees of Malibal Saipa Company were investigated, and 207 people were selected as a sample using Cochran's formula. This research used structural equation method and included 3 main features and 14 sub-features of EFQM in its model. The results show that some relationships such as organizational culture and leadership with an impact factor of 0.24 and the relationship between implementation and stakeholders with an impact factor of 0.19 have been identified as meaningless.https://jemsc.qom.ac.ir/article_3655_6b63a0fc137b052a934eb008cd79fa12.pdfUniversity of QomJournal of Engineering Management and Soft Computing3116-015811120250821Analyzing customer sentiment with AI to improve the smart supply chainتحلیل احساسات مشتریان با هوش مصنوعی برای بهبود زنجیره تأمین هوشمند306286365410.22091/jemsc.2025.3654.1260ENAsgharHemmatiDepartment of Industrial Engineering, Abhar Branch, Islamic Azad University, Abhar, Iran0000-0001-8074-2834Seyed HesamoddinMotevalliDepartment of Future Studies, Shomal University, Amol, IranAdelPourghader ChobarAssistant Professor, Department of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran0000-0002-8377-5906AliAkhlaghpourMaster of Industrial Engineering, Iran University of Science and Technology, Tehran, IranLeilaNazariDepartment of Industrial Engineering, Abhar branch, Islamic Azad University, Abhar, IranJournal Article20250221Understanding customer sentiment and analyzing it using artificial intelligence plays an important role in improving decision-making in the supply chain. This study aimed to investigate the impact of AI-based customer sentiment analysis on demand forecasting, inventory management, and product design in smart supply chains.Text, audio, and video data from Twitter, Facebook, Amazon, and customer service calls were collected and processed with a pre-trained BERT model for sentiment analysis. Also, Wav2Vec 2.0 and DeepFace models were used to analyze audio and video data. The findings showed that using sentiment analysis increased the accuracy of demand forecasting by 18%, reduced inventory management costs by 20%, and improved customer satisfaction with product design by 25%. The results show that integrating customer sentiment analysis with AI can optimize supply chain processes and increase decision-making accuracy.https://jemsc.qom.ac.ir/article_3654_3ca0c8e91b6892a1d57c116258a7190c.pdfUniversity of QomJournal of Engineering Management and Soft Computing3116-015811120250821Intelligent Model for Predicting Intellectual Capital Maturity in Knowledge-Based Companies Using Machine Learningمدل هوشمند پیشبینی بلوغ سرمایه فکری در شرکتهای دانشبنیان با استفاده از یادگیری ماشین340307366110.22091/jemsc.2025.12548.1266ENHosseinAzizinejadPhD Student, Department of Industrial Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran.0009-0005-6060-8717GholamrezaTavakoliAssociate Professor, Department of Management, Malek Ashtar University of Technology, Tehran, Iran.0000-0003-3182-2114MohammadEhsanifarAssociate Professor, Department of Industrial Engineering, Arak Branch, Islamic Azad University, Arak, Iran.0009-0002-9081-7251AmirNajafiAssociate Professor, Department of Industrial Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran.0000-0003-1949-6846Journal Article20250317The aim of this research is to design an intelligent model for predicting the maturity of intellectual capital in knowledge-based companies located in industrial parks using machine learning algorithms. This study is applied-developmental in purpose and descriptive-modeling in methodology, utilizing a mixed approach for data collection. The data were gathered through a review of the literature, interviews with experts, and two questionnaires. For data analysis, various methods were employed, including the Delphi method, confirmatory factor analysis, and machine learning algorithms such as random forests, K-nearest neighbors, decision trees, naive Bayes, and multi-layer perceptron neural networks, using SPSS, PLS software, and various Python libraries. The results indicated that all models were capable of predicting the level of intellectual capital maturity; however, the multi-layer perceptron (MLP) model outperformed the others based on several criteria, including accuracy, precision, sensitivity, and F1 score, yielding the best results with values of 88.37%, 89.75%, 88.37%, 86.51%, and 0.918 in the area under the ROC curve.https://jemsc.qom.ac.ir/article_3661_10de3a95df516fdeef84b9241ba56627.pdfUniversity of QomJournal of Engineering Management and Soft Computing3116-015811120250821Enhancing Supplier Selection Efficiency through Knowledge Sharing and Blockchain Technology: A Multiple Case Study360341358910.22091/jemsc.2025.13129.1280ENMostafaJafariAssociate Professor, Department of Industrial Engineering, Iran University of Science & Technology, Tehran, IranAmir HosseinAkbariPhD student, Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran="0000"&"-000"&RANDBETWEEN(1,4)&"-"&RANDBETWEEN(2033,9479)&"-"&RANDBETWEEN(2013,9934)Journal Article20250421Knowledge sharing enables organizations to extend beyond their boundaries and maximize its benefits. However, this process faces challenges related to privacy and ownership. This study examines the synergistic role of blockchain technology and knowledge sharing in supplier selection across various industries, including manufacturing, electronics, hardware development, software, and network equipment production. The research employs a structured questionnaire to collect cross-sectional survey data from 336 public procuring entities in factories. The data is analyzed using the Partial Least Squares (PLS) method. The findings indicate that both blockchain technology and knowledge sharing significantly enhance supplier selection efficiency. Specifically, two key features of blockchain technology—decentralization and transparency—play a crucial role in mediating the impact of knowledge sharing on supply chain performance. Moreover, when blockchain technology is integrated into knowledge sharing, supplier selection performance metrics, such as quality and delivery, show notable improvements.https://jemsc.qom.ac.ir/article_3589_597183eeb0fc2285d62ae0c61a4055d3.pdf