University of Tehran PressAdvances in Industrial Engineering2783-174459220251201A Novel Model for the Synchromodal Hub Location Problem21524010175010.22059/aie.2025.391393.1940ENAliGhorbani Farami NezhadPh.D. Candidate, Department of Industrial Engineering, Kish International Campus, University of Tehran, Tehran, IranMohammad MahdiNasiriProfessor, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.FatemehSabouhiAssistant Professor, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.Journal Article20250308With advancements in technology and growing customer demand, the optimal design of hub networks in distribution systems, along with flow synchronization across the entire network, play a critical role in reducing costs and enhancing overall efficiency. These networks significantly contribute to optimizing delivery times and improving responsiveness to customer needs, particularly in the transportation of time-sensitive goods. This study develops a mixed-integer linear programming model for the synchromodal hub location problem. We synchronize the flow throughout the entire network, which consists of origin points, a sender hub, a receiver hub, and demand points. To replicate real-world conditions, we also consider the synchronization of product flow within the distribution hub distribution networks. The proposed model aims to minimize the total cost, which includes transportation costs, operational costs across the distribution network, fixed costs of establishing Hubs and deploying vehicles, and potential penalties incurred as a result of failures to meet customer demand in terms of quantity and timeliness. To aggregate long-, mid-, and short-term decisions, we examine several decisions across different time periods. These decisions include the incomplete hub location problem, the service network design problem involving the scheduling of all network nodes, the synchronization of shipment flows in an intermodal transportation system, as well as integration and sorting operations on all components of the hub distribution network. The model's performance is assessed using data from an actual case study in the Iranian food industry. We conduct various sensitivity analyses on key parameters of the problem and present the numerical findings.https://aie.ut.ac.ir/article_101750_3038b03d48a4f4c37169194d2edc28fe.pdfUniversity of Tehran PressAdvances in Industrial Engineering2783-174459220251201Enhancing Project Schedule Monitoring: Application of CUSUM and EWMA Memory Control Charts in Earned Schedule Method24125510174910.22059/aie.2025.389599.1937ENAmir-MohammadGolmohammadiAssociate Professor, Department of Industrial Engineering, Arak University, Arak, Iran.AliShahabiAssistant Professor, Department of Industrial Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.MohsenShojaeePh.D. Candidate, Department of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran.FazelHajizadeh EbrahimiAssistant Professor, Department of Industrial Engineering, Qom University of Technology, Qom, Iran.HamidrezaAbedsoltanM.Sc., School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.0009-0005-4411-3511Journal Article20250131Earned Value Management ( ) and Earned Schedule ( ) are crucial tools for controlling projects and preventing deviations from schedule and budget objectives. In early-return projects, meeting deadlines is critical; however, Earned Value alone may not provide an appropriate criterion for evaluating and analyzing time-related indicators. This study proposes a method to use statistical control charts that consider indicator deviations from the project's start (memory charts) and are more sensitive to schedule deviations. Specifically, Exponentially Weighted Moving Average (EWMA) and Cumulative Sum (CUSUM) charts are employed to monitor the Schedule Performance Index ( ) based on the system. Instead of Expected Value ( ), is used for monitoring. Results demonstrate that both CUSUM and EWMA charts offer higher accuracy compared to classical Shewhart charts and produce fewer error alarms. The CUSUM chart shows less error (75% reduction in initial error alarms), while EWMA displays higher sensitivity (20% faster deviation detection). This proposed method can assist project managers in identifying schedule deviations more accurately and rapidly. The study utilized data from a 30-month construction project, applying normality tests and data transformation techniques to ensure statistical validity. The findings suggest that memory control charts based on provide a more reliable and responsive approach to project schedule monitoring, particularly in time-sensitive projects.https://aie.ut.ac.ir/article_101749_8393ad75b20598651d98e1cf33fc9acd.pdfUniversity of Tehran PressAdvances in Industrial Engineering2783-174459220251201Designing a Sustainable Closed-Loop Supply Chain Network for Agricultural Products under Uncertainty with a Focus on Water Consumption Reduction25727810136610.22059/aie.2025.385215.1925ENMohammadMohammadiM.Sc., Department of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran,AbdolsalamGhaderiAssociate Professor, Department of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran.JavidGhahremani-NahrAssistant Professor, Academic Center for Education, Culture and Research (ACECR), Tabriz, Iran.Journal Article20241111As global population growth accelerates, demand for agricultural products has surged, leading to higher production, rising costs, increased water use, and food shortages. This study proposes a sustainable agricultural supply chain network that prioritizes water conservation while meeting customer needs. A mathematical model optimizes a closed-loop supply chain, maximizing demand for agricultural products and compost. The model minimizes costs, maximizes customer satisfaction, and reduces water consumption, ensuring sustainability. A stochastic programming approach manages supply and demand uncertainties through scenarios. Results show that increasing customer satisfaction raises costs and water use. For example, increasing the customer importance factor from 0.2 to 0.8 increases total costs by 4.53% and water use by 43.75%, highlighting the sensitivity of water use to customer satisfaction. Reducing processing center capacity decreases water use but increases costs and reduces customer satisfaction. A 50% reduction in capacity raises costs by 56.41%, decreases customer satisfaction by 4.44%, and reduces water use. Water use reductions vary by stage: a 50% reduction in agricultural production cuts total water use by 32.33%, while similar reductions in processing and composting yield smaller decreases of 17.86% and 28.32%, respectively. This underscores agricultural production as the most water-intensive phase. The model’s effectiveness is demonstrated through numerical examples and sensitivity analyses. Metrics such as the Number of Pareto Fronts (NPF) and Maximum Spread Index (MSI) are used to compare solutions. This study emphasizes aligning sustainable production, resource conservation, and customer needs to create a resilient agricultural supply chain.https://aie.ut.ac.ir/article_101366_f71d374a68763cecff47f9ace3d7a1fd.pdfUniversity of Tehran PressAdvances in Industrial Engineering2783-174459220251201An Interval-Valued Fuzzy Group Decision-Making Model Based on Two New Developed IVF-LBWA and IVF-MAIRCA Methods for Sustainable Project Selection27930510136710.22059/aie.2025.386811.1929ENSajjadKaramiPh.D. Candidate, Department of Industrial Engineering, Shahed University, Tehran, Iran.Seyed MeysamMousaviProfessor, Department of Industrial Engineering, Shahed University, Tehran, Iran.Journal Article20241210In an era of rapid change, complexity, and uncertainty, organizations must rely on sustainable project portfolio management to achieve long-term objectives. In project-oriented environments, selecting the most suitable project portfolio remains a critical challenge. To address this, advanced decision-making approaches, particularly multi-criteria decision-making (MCDM) techniques, have been developed to support well-informed and dependable choices. This study develops a new synergistic integration of Interval-Valued Fuzzy Level-Based Weight Assessment (IVF-LBWA) and Interval-Valued Fuzzy Multi-Attribute Ideal Real Comparative Analysis (IVF-MAIRCA), to improve decision-making in uncertain environments. In contrast to traditional methods, these approaches utilize interval-valued fuzzy numbers, thereby increasing the precision of project ranking and selection. An application example involving five projects and six evaluation criteria is provided to demonstrate the practical application of these methods. The results indicate that IVF-LBWA and IVF-MAIRCA yield stable and consistent project rankings, reinforcing their applicability in real-world scenarios. A sensitivity analysis was performed across 40 different criteria weighting scenarios to evaluate the impact of weight variations on project rankings. The results demonstrate that the proposed integrated approach preserves ranking stability, reflecting decision-makers’ priorities and the relative importance of each criterion. These findings validate its effectiveness in managing uncertainty and supporting reliable decision-making. The findings confirm that this approach provides a systematic and reliable framework for sustainable project portfolio selection. By enhancing decision accuracy and strengthening resilience to uncertainty, it enables decision-makers to align project selection with long-term sustainability, resource efficiency, and strategic objectives.https://aie.ut.ac.ir/article_101367_cb94e720b9a570a33714ee7eea26c905.pdfUniversity of Tehran PressAdvances in Industrial Engineering2783-174459220251201AI-Powered Selection and Classification of Resilient Suppliers: A Hybrid Approach Using Fuzzy DEA and ML Techniques and Its Application in the Textile Industry30731910174810.22059/aie.2025.388392.1933ENFereshtehKoushkiAssistant Professor, Department of Mathematics, Qa.C., Islamic Azad University, Qazvin, Iran.MonaNaghdehforoushhaAssistant Professor, Department of Computer Engineering, Tak.C., Islamic Azad University, Takestan, Iran.Journal Article20250107A resilient supplier is able to persevere in the face of disruptions and risks. Selecting resilient suppliers is crucial for businesses to receive high-quality services quickly and at a low cost. Clustering resilient suppliers facilitates identifying the most efficient and resilient ones. Mathematical models used to evaluate supplier resilience and cluster suppliers have limitations when addressing large-scale problems and fuzzy data. New techniques, such as Machine Learning (ML) methods, can be used to mitigate these limitations and predict supplier performance accurately. Few studies have used ML methods to cluster suppliers based on resilience criteria in imprecise data environments. To bridge this gap, this study proposes an integrated approach using Fuzzy Data Envelopment Analysis (FDEA) and ML methods to predict efficiency scores and classify suppliers based on resilience criteria. These methods were applied to evaluate a spinning and weaving factory as a real-life case study, based on resilience criteria. The results demonstrated that among five algorithms- Decision Tree (DT), Random Forests (RF), Support Vector Regression (SVR), K-Nearest Neighbors (KNN), and Logistic Regression (LR) - the SVR algorithm had the best performance in predicting the efficiency and resilience of suppliers with the accuracy value of .85. Additionally, the suppliers were classified into weak, medium, and strong classes. Five ML algorithms were used to predict the class of new suppliers. Among the LR, DT, RF, KNN, and SVR algorithms, the DT had the highest accuracy value of 1, while the KNN had the lowest accuracy value of .55.https://aie.ut.ac.ir/article_101748_1a76b81a9795d06a9a3edcab56163920.pdfUniversity of Tehran PressAdvances in Industrial Engineering2783-174459220251201A Comprehensive Framework for Selecting Waste-to-Energy Technologies in Iran: A Multi-Criteria Decision-Making Approach32134210136810.22059/aie.2025.390039.1938ENMaryam SadatHeydariM.Sc., Department of Industrial Engineering, Yazd University, Yazd, Iran,DavoodShisheboriAssociate Professor, Department of Industrial Engineering, Yazd University, Yazd, Iran,0000-0001-7737-6565Mohammad KazemSadeghianPh.D., in Economics, Director General of Mining Industry and Trade of Yazd Province, Yazd, Iran.Journal Article20250206Sustainable energy supply is a critical challenge in developing countries, particularly Iran, where fossil fuels dominate energy production. The environmental consequences of fossil fuel reliance, including greenhouse gas emissions and climate change, underscore the need for alternative energy sources. Municipal solid waste (MSW) represents a significant biomass resource with potential for energy generation, offering a dual solution to waste management and energy needs. This study aims to evaluate six waste-to-energy (WtE) technologies—incineration (INC), gasification (GAS), plasma (PL), landfill gas (LFG), pyrolysis (PYR), and anaerobic digestion (AD)—using a multi-criteria decision-making (MCDM) approach. Four sustainability dimensions—economic, environmental, social, and technical—were assessed through twelve sub-criteria, employing the Best-Worst Method (BWM) for weighting and the Measurement Alternatives and Ranking according to Compromise Solution (MARCOS) method for ranking the technologies. The results suggest that landfill gas is the most suitable WtE technology for Iran, providing optimal waste volume reduction and significant potential for renewable energy generation This study provides a strategic framework aimed at improving waste management and fostering sustainable energy production in Iran, thereby facilitating the shift from a linear economy to a circular one.https://aie.ut.ac.ir/article_101368_e388001ecf25c1ecf3f942f425b61d2c.pdfUniversity of Tehran PressAdvances in Industrial Engineering2783-174459220251201Identifying Customer Segments in E-Commerce: A Data-Driven Framework Using Transactional Patterns34335810308410.22059/aie.2025.394483.1943ENMehranAkabrpourM.Sc. Student, Department of Industrial Engineering, K. N. Toosi University of Technology (KNTU), Tehran, Iran.AliBozorgi-AmiriAssociate Professor, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.MostafaSetakAssociate Professor, Department of Industrial Engineering, K. N. Toosi University of Technology (KNTU), Tehran, Iran.Journal Article20250501This study proposes a structured and interpretable approach to customer segmentation by enhancing the classical RFM (Recency, Frequency, Monetary) model. The research addresses the common limitation of traditional RFM-based methods, which often overlook behavioral diversity and lack flexibility for practical, data-driven decision-making. The main objective is to develop a segmentation framework that provides actionable insights based on transparent, rule-based logic rather than opaque clustering algorithms. Using transactional data from a leading e-commerce platform in the Netherlands, the methodology applies quartile-based scoring to RFM indicators and maps customers into six distinct behavioral segments. Visual analytics are employed to support interpretation, enabling businesses to tailor engagement strategies for each group. The results demonstrate improved segment differentiation, managerial interpretability, and relevance to real-world applications. The study also highlights the model’s adaptability to other customer-centric industries, with future research directions focused on incorporating machine learning and behavioral enrichment for Customer Lifetime Value (CLV) prediction.https://aie.ut.ac.ir/article_103084_1920091a9963c13cf690a3eac7fb4093.pdfUniversity of Tehran PressAdvances in Industrial Engineering2783-174459220251201Unpacking the Key Influencers for Customer Satisfaction in Tourism Mobile Applications Using Fuzzy Cognitive Mapping35937510338610.22059/aie.2025.390571.1939ENMortezaHemmati-AsiabarakiPh.D. Candidate, Department of Industrial and Systems Engineering, Fouman Faculty of Engineering, College of Engineering, University of Tehran, Iran.SalmanNazari-ShirkouhiAssociate Professor, Department of Industrial and Systems Engineering, Fouman Faculty of Engineering, College of Engineering, University of Tehran, Iran.Mohamad SadeghSangariResearch Associate, Ted Rogers School of Management, Toronto Metropolitan University (formerly Ryerson University), Canada.AliBozorgi-AmiriAssociate Professor, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.Journal Article20250215This research tries to identify the critical success factors (CSFs) for Tourism Mobile Applications (TMAs) and to evaluate their influence on customer satisfaction. Our process was twofold. Initially, a framework of potential CSFs was constructed through a comprehensive literature review and expert opinions in the field. Consequently, the interrelationships and relative influence of these factors were analyzed using Fuzzy Cognitive Mapping (FCM). The data feeding the FCM model was derived from two distinct expert-administered questionnaires: one designed for the Analytic Hierarchy Process (AHP) to establish initial weights, and another to define the causal relationships within the FCM. The results reveal that "perceived playfulness" is the most influential critical success factors. By providing strategies for service providers to attract more customers and users, the proposed framework aims to ensure satisfactory service delivery. This research contributes to the operational strategies that can enhance the competitiveness capability of TMAs and highlights the essential considerations for TMA developers.https://aie.ut.ac.ir/article_103386_67a72307e0a083e4dea058b40518930b.pdfUniversity of Tehran PressAdvances in Industrial Engineering2783-174459220251201A Combined Two-Stage Clustering and Sequential Protective Submatrix Algorithms in Emergency Facility Coverage Sets37739910277210.22059/aie.2025.380198.1944ENShimaBagherimonfaredM.Sc., Department of Industrial Engineering, University of Science and Culture, Tehran, Iran.RezaKamranradAssistant Professor, Department of Industrial Engineering, Faculty of Mechanical Engineering, Semnan University, Semnan, Iran.0000-0002-5535-491XMostafaZareeAssistant Professor, Department of Logistics and Supply Chain Management, Faculty of Humanities, Imam Hossein Guard Training and Officer University, Tehran, Iran.Journal Article20250504A major part of crisis management is logistics. Setting up an effective logistics system during emergencies and reducing damage is essential. This study first introduces a mathematical model for emergency logistics. Then, a hybrid metaheuristic algorithm is proposed to optimize how demand in affected areas is met based on this model. The focus is on emergency logistics with the goal of reducing costs and improving coverage of people in need. It also presents a model for locating distribution and relief centers using a two-stage clustering approach to form binary clusters from a distance matrix, where each cluster pair represents which distribution centers serve which demand areas. In contrast, our approach consistently matches the optimal solutions faster than GAMS, as detailed in Table 11. Notably, for larger instances (Ins9–10), OPSM reduces runtime by 30–50% while still achieving optimal solutions. This efficiency is particularly evident when GAMS fails to reach optimality, as our method outperforms its best-found solutions. Findings shows that, proposed algorithm is efficient and suitable for optimizing and solving coverage set problems.https://aie.ut.ac.ir/article_102772_34e30c66cce3b24e6b4fceea806e1b84.pdfUniversity of Tehran PressAdvances in Industrial Engineering2783-174459220251201Crowd-Based Multi-Echelon Routing Problem with Roaming Delivery and Mobile Intermediate Transfer Locations40142410174710.22059/aie.2025.388216.1932ENSadjadKhalesiPh.D. Candidate, Industrial Engineering Department, Sharif University of Technology, Tehran, Iran.Mohammad RezaAkbari JokarProfessor, Industrial Engineering Department, Sharif University of Technology, Tehran, Iran.Journal Article20250105The Vehicle Routing Problem with Roaming Delivery Locations (VRPRDL) represents a recent innovation in last-mile delivery, wherein a customer's order is delivered to the trunk of their vehicle, which may be parked at various locations across different time windows. In this Paper, we introduce a novel crowd-based multi-echelon variant of the vehicle routing problem with roaming delivery locations (crowd-based ME-VRPRDL). This model integrates a flexible multi-echelon logistics structure with hybrid intermediate transfer locations (satellites), which can be either mobile or stationary. The flexibility in our approach allows the optimal solution to dynamically adapt between single-echelon and multi-echelon configurations, depending on the specific problem parameters and constraints. In the proposed model, crowd shippers—individuals who assist with deliveries—are assigned to intermediate satellites based on their availability and time windows, enabling more efficient and dynamic resource allocation. To address the complexity of this problem, we develop an innovative heuristic algorithm that combines node classification with a greedy optimization approach. This algorithm is particularly tailored to handle the unique challenges posed by occasional crowd shippers and hybrid satellite configurations. Our findings demonstrate that the integration of multi-echelon logistics systems with crowd shipping and strategically placed satellites offers significant potential to optimize last-mile delivery operations. Specifically, it reduces delivery costs and travel times while leveraging underutilized resources in the logistics network. The study underscores the value of combining traditional and crowd-based delivery mechanisms in achieving more sustainable and cost-effective solutions for modern logistics challenges.https://aie.ut.ac.ir/article_101747_9b3a2505cf492b7c5f1a8f4440eae997.pdfUniversity of Tehran PressAdvances in Industrial Engineering2783-174459220251201A Simulation Model of Credit Risk in Supply Chain Finance Using a Dynamic Systems Analysis Approach42544910105110.22059/aie.2025.388627.1935ENReyhanehAbedPh.D. Candidate, Department of Finance and Banking, Faculty of Management and Accounting, Allameh Tabataba’i University, Tehran, Iran.MostafaSargolzaeiAssociate Professor, Department of Finance and Banking, Faculty of Management and Accounting, Allameh Tabataba’i University, Tehran, Iran.MaghsoudAmiriProfessor, Department of Management of Operations and Information Technology, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran.Mohammad AliDehghan DehnaviAssistant Professor, Department of Finance and Banking, Faculty of Management and Accounting, Allameh Tabataba’i University, Tehran, Iran.Journal Article20250111Objectives: This study aims to identify the key factors influencing credit risk in supply chain finance (SCF), determine the main subsystems, and present a simulation model for credit risk in SCF using a dynamic systems analysis approach.<br />Method: The research employs a two-tier supply chain financial model, consisting of a buyer company (core company) and a seller company (supplier) in the pharmaceutical industry. The factoring method is used as one of the SCF techniques to construct the supply chain financial model using a dynamic systems analysis approach. Vensim software is utilized for the simulation.<br />Results: The study identifies the subsystems, key factors influencing credit risk in SCF, causal relationships, delays between these factors, and a systemic view of credit risk in SCF. The findings show that the credit risk of small and medium-sized enterprises (SMEs), decreases when they participate in SCF. A sensitivity analysis of three key variables was conducted by simulating changes in critical parameters over a five-year period. The results highlight that the financial conditions of both the supplier and the main company, macroeconomic and industry risk, supply chain position, the quality of credit risk management by the lending bank, and the effectiveness of risk intermediaries significantly impact credit risk in SCF.<br />Innovation: This study is the first in the country to develope a simulation model of credit risk in SCF using a dynamic systems analysis approach. It specifically analyzes and evaluates the credit risk of SMEs (supplier companies) within the context of SCF participation.https://aie.ut.ac.ir/article_101051_0e22be2961da90b8a93ab3ab773c3b3c.pdfUniversity of Tehran PressAdvances in Industrial Engineering2783-174459220251201Dual-Phase Local Search Embedded Multi-Verse Optimizer for Optimal Feature Subset Selection45146910338510.22059/aie.2025.388689.1936ENMaryamAskariPh.D. Candidate, Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran.FaridKhoshalhanAssociate Professor, Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran.HodjatHamidiAssociate Professor, Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran.Journal Article20250116Feature selection plays a pivotal role in enhancing the performance of machine learning models by reducing dimensionality, improving interpretability, and minimizing computational overhead. This study presents the Improved Multi-Verse Optimizer (IMVO), a new feature selection algorithm that merges the global exploration ability of the standard Multi-Verse Optimizer (MVO) with a dual-phase mutation-based local search mechanism. Unlike previous MVO-based or hybrid metaheuristic approaches, IMVO simultaneously strengthens exploitation through targeted refinement of the best solutions and preserves population diversity via periodic random-solution mutations. This strategic combination mitigates premature convergence, accelerates convergence speed, and improves robustness across diverse high-dimensional datasets. Comprehensive experiments on 14 widely used datasets obtained from the UCI repository show that IMVO consistently achieves higher classification accuracy, fewer selected features, and lower fitness values than five state-of-the-art algorithms (MVO, GA, PSO, SSA, HHO). Quantitative analysis using the Wilcoxon signed-rank test certifies the significance of these enhancements, underscoring the algorithm’s reliability. While the inclusion of local search increases computational cost, the demonstrated gains in accuracy, stability, and feature reduction affirm this cost-benefit relationship, positioning IMVO as a competitive and versatile tool for feature selection and related optimization problems.https://aie.ut.ac.ir/article_103385_d26127ea22201e31158f9fa992399b47.pdf