University of Tehran PressAdvances in Industrial Engineering2783-174460120260601Integrated Multi-Agent Problem of Vehicle Routing and Cross-Dock Scheduling Considering Group Purchasing Strategies, Perishability of the Commodities and Requirements of the Customers12910358310.22059/aie.2025.395242.1946ENHadiSarmadiPh.D. Candidate, Department of Industrial Engineering, Kish International Campus, University of Tehran, Tehran, Iran.0009-0000-9754-2559Mohammad MahdiNasiriProfessor, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.0000-0001-9813-1233Journal Article20250513During the recent years, the companies in a wide range of industries have to design their activities in such a way to reduce the costs. A most popular way to reduce the costs in logistics is cross-docking. It is a strategy which is used to serve different purposes including the fast consolidation of received volume of commodities from suppliers, improving the responsiveness by shortening delivery lead time, reducing the inventory holding costs, eliminating spoilage costs of commodities, reducing transportation costs by employing full truck loading policy etc. The objective of this paper is to develop a mixed integer linear programming (MILP) model considering supplier selection and order allocation, perishability of commodities, group purchasing strategy and multi-agent scheduling into the well-known vehicle routing problem with cross-docking. Some small-sized test instances are applied to validate the new proposed model. A weighted-sum method is applied to solve small-sized instances. Then sensitivity analysis of the new proposed model is performed on the key parameters of the objective functions so that the supply decisions are evaluated while the parameters of the distribution costs are changed. Due to NP-hardness of the new proposed problem, two meta-heuristic algorithms including NSGA-II and MOPSO are applied to solve a wide range of instances. The obtained results by applying statistical hypothesis tests are compared through six different criteria. Also, an ordering technique that is called Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is applied to rank the meta-heuristic approaches.https://aie.ut.ac.ir/article_103583_4e9134f3c0bb3bff3ead88d4faa73b83.pdfUniversity of Tehran PressAdvances in Industrial Engineering2783-174460120260601Fuzzy Multi-Criteria Decision-Making Method F-PSWCA: A Case Study on the Selection and Ranking of Criteria and New Technologies in Dialysis315210566710.22059/aie.2026.405928.1959ENArashPazhouhandehPhD candidate, Department of Industrial Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran.0000-0001-5475-6258ParvanehSamoueiAssociate professor, Department of Industrial Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran.0000-0003-0867-7617Journal Article20251109Selecting appropriate technologies in healthcare systems is a complex decision-making problem involving multiple, often conflicting criteria under uncertainty. In this paper, a novel fuzzy multi-criteria decision-making (MCDM) method, referred to as F-PSWCA, is proposed to enhance the criteria weighting process by incorporating historical performance trends within a fuzzy environment. The proposed method integrates fuzzy regression parameters, including slope, intercept, and coefficient of determination (R²), to capture both the magnitude and stability of criteria over time. Unlike conventional fuzzy MCDM approaches that rely solely on static expert judgments, F-PSWCA enables dynamic assessment of criteria importance while preserving uncertainty representation. The applicability of the proposed method is demonstrated through a real-world case study on the selection of dialysis water purification technologies, where multiple technical, economic, and operational criteria are considered. Comparative analysis with Fuzzy SAW, Fuzzy TOPSIS, and Fuzzy SECA is conducted to evaluate the robustness and consistency of the results. The findings indicate that while ranking similarities may occur across methods, F-PSWCA provides additional interpretive insights by distinguishing between temporally stable and unstable criteria. The results confirm the effectiveness of the proposed approach as a decision-support tool for technology selection in complex and evolving healthcare environments.https://aie.ut.ac.ir/article_105667_91cb3d25b281e9962c37ba7a1d142e71.pdfUniversity of Tehran PressAdvances in Industrial Engineering2783-174460120260601A Distribution Network Design Model Using Data Classification and Fleet Optimization537010394210.22059/aie.2025.386390.1927ENMohammadAmirahmadiPh.D. Candidate, Department of Industrial Engineering, Faculty of Engineering, Islamic Azad University, North Tehran Branch, Tehran, Iran.HamidEsmailiAssociate Professor, Department of Industrial Engineering, Faculty of Engineering, Islamic Azad University, North Tehran Branch, Tehran, Iran.0000-0003-0149-8077KiaParsaAssistant Professor, Department of Mathematics, Faculty of Sciences, Islamic Azad University, North Tehran Branch, Tehran, Iran.AminMostafaeeAssociate Professor, Department of Mathematics, Faculty of Sciences, Islamic Azad University, North Tehran Branch, Tehran, Iran.Journal Article20241202This study seeks to address gaps in previous research by introducing a comprehensive data-driven distribution network design model. The process begins with an in-depth analysis of customer demand, utilizing unsupervised learning algorithms to gain valuable insights into consumer behavior. This analysis identifies demand levels across different geographical regions and reveals temporal demand patterns. The resulting insights serve as inputs to the distribution network design model. To facilitate effective data classification and analysis, The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is employed. enabling accurate estimation of customer demand based on innovative parameters. Based on the clustering results, a mixed-integer linear programming model is developed that incorporates facility-location, capacity-planning, product-flow, and fleet-composition decisions. Importantly, during this modeling process, emphasis will be placed not only on optimizing the number, location, and capacity of facilities but also on refining fleet types and their compositions to enhance overall efficiency. The proposed model is solved using CPLEX in GAMS and evaluated through a set of numerical test instances. The results demonstrate that the proposed data-driven model achieves an average profit improvement of 10-15% compared to traditional non-clustered approaches. The model also yields savings in transportation and fleet-related costs. Moreover, its integrated structure enables sensitivity analyses of key parameters and provides useful managerial insights. demonstrating the synergy between data-driven clustering and mathematical optimization for distribution network design.https://aie.ut.ac.ir/article_103942_827401f903a22dee8ae1e1ec99655644.pdfUniversity of Tehran PressAdvances in Industrial Engineering2783-174460120260601A Robust Contract-Based Hybrid Procurement Model with Supplier Incentives for Humanitarian Supply Chains under Uncertainty719210566810.22059/aie.2026.406269.1961ENImanShokrAssistant Professor, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.0000-0003-2794-7264Journal Article20251115Typically, humanitarian agencies (HAs) tend to preposition relief supplies before potential disasters to increase resource availability in the post-disaster phase. However, prepositioning imposes high costs on relief chains. On the other hand, although postponing relief supply procurement to the post-disaster phase may avoid prepositioning costs, it comes with significant supply risks. This study utilizes buyback, option, and quantity discount contracts which enables HAs to adopt a hybrid strategy for providing relief supplies, ensuring maximum post-disaster supply availability. In this paper, a robust bi-objective two-stage stochastic programming model is proposed for supplier selection and the procurement of critical and non-critical relief supplies. The model also emphasizes suppliers' profits to incentivize their participation in the relief chain. In this study, a combination of robust stochastic programming and robust convex programming is employed to manage uncertainty. The model is applied to a case study, and multiple sensitivity analyses are performed. The results demonstrate that, compared with the situation in which hybrid contracts are not utilized, the proposed model reduces the HA’s cost by 38.4% and expected shortages by 22.6%, while increasing suppliers’ profits by 41.8%.https://aie.ut.ac.ir/article_105668_0795c9d4a3316baae116de81d2e5012a.pdfUniversity of Tehran PressAdvances in Industrial Engineering2783-174460120260601Service Price Optimization in Ride-Hailing Company and Its Coordination with Insurance Company by Revenue-Sharing Contract9312310609010.22059/aie.2026.397478.1948ENHamidehBahramiPh.D. Candidate, School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.SaeedYaghoubiProfessor, School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.0000-0003-1218-9050MaryamGhanbarzadeh-RoudbarakiM.Sc., School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.Journal Article20250622This article proposes a mathematical model to examine service pricing in ride-hailing companies and the relationship between ride-hailing and insurance companies, in which insurance is viewed as a competitive advantage. The ride-hailing company insures its drivers, customers, and cars with the insurance company, ensuring that the insurance company will compensate the affected parties in the event of an accident during travel. This study addresses the importance of hygiene in ride-hailing cars, given the impact of epidemics such as COVID-19 on transportation service prices. The interaction between the ride-hailing and insurance companies is modeled using a Stackelberg game in three scenarios: decentralized, centralized, and a coordination game under revenue-sharing contracts. The ride-hailing company decides on insurance and hygiene-level, while the insurance company determines the base entrance premium to interact with the ride-hailing company. This model allows ride-hailing companies to optimize their profitability and decision-making, and provide safe and reliable services to their customers. The study is validated using information from Lyft. The result shows that the revenue-sharing contract between the ride-hailing company and the insurance company increases the profits of both companies. This article provides a framework for ride-hailing companies to gain a competitive advantage by coordinating with insurance companies and ensuring the safety and hygiene of services.https://aie.ut.ac.ir/article_106090_4193b6a29e1d8268860cff6fac06579a.pdfUniversity of Tehran PressAdvances in Industrial Engineering2783-174460120260601A Data-Driven Industrial Engineering Framework for Hospital Performance Evaluation Using the Balanced Scorecard12514110633510.22059/aie.2026.409978.1967ENZahraMojaradiPh.D. Candidate, Department of Industrial Engineering, Alborz Campus, University of Tehran, Tehran, Iran.0000-0002-1180-9572AliBozorgi-AmiriAssociate Professor, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.0000-0002-1180-9572ZeinabSazvarAssociate Professor, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.0000-0002-0059-8781Journal Article20260127Performance evaluation in complex service systems is a central concern in industrial engineering, particularly in regulated environments such as healthcare. This study develops a data-driven industrial engineering framework for hospital performance evaluation structured through the Balanced Scorecard (BSC) and supported by exploratory predictive modelling. A structured evidence-based screening process was conducted to identify operationally measurable performance indicators, which were subsequently organised within the four BSC perspectives and aggregated into composite performance dimensions using standardised equal-weight scoring. Quarterly organisational data (2018–2024) from a tertiary hospital were used to examine structural relationships among indicators through a multilayer perceptron neural network. The predictive component is intended as exploratory validation rather than universal forecasting. Results indicate strong alignment between predicted and observed composite performance scores (R = 0.84), suggesting that the selected indicators collectively explain substantial variation within the studied organisational context. Importance and sensitivity analyses further identify cost-efficiency and operational-process variables as influential drivers, while safety, workforce, and patient-related indicators demonstrate meaningful associations. The proposed framework enhances transparency in indicator selection, clarifies multidimensional performance structuring, and provides analytically informed decision support for complex service systems.https://aie.ut.ac.ir/article_106335_fb9d64b4666a7ae6f663481331860895.pdfUniversity of Tehran PressAdvances in Industrial Engineering2783-174460120260601A Non-Radial SBM DEA Framework for Assessing the Performance of EU Countries14316410633610.22059/aie.2026.410874.1968ENHodaMoradiPostdoctoral Researcher, Department of Management, Meybod University, Meybod, Iran.0000-0002-2505-051XHamidBabaei MeybodiAssociate Professor, Department of Management, Meybod University, Meybod, Iran.0000-0002-3063-4501Journal Article20260209This study presents a comprehensive and robust framework for evaluating and ranking the energy and environmental efficiency of European Union countries under conditions of data uncertainty. A non-radial Slack-Based Measure model is proposed, incorporating undesirable outputs and utilizing a unified efficiency frontier. To rank countries based on interval efficiency results, a preference-based algorithm is employed that utilizes pairwise comparisons and incorporates the decision-maker's behavioral preferences through an optimism parameter. This approach enables flexible, behavior-sensitive rankings. Furthermore, the study demonstrates that several commonly used possibility-degree-based ranking formulas yield equivalent results within the conventional interval comparison framework, regardless of whether intervals overlap or are completely disjoint. Moreover, the study illustrates that the proposed model offers significant advantages over these formulas in terms of discriminatory power, consistency, and robustness. To validate the proposed model, interval data covering the energy and environmental efficiency of 27 EU countries during 2021–2022 are analyzed. The results indicate that the model achieves high discriminatory power, consistent rankings, and robustness against data fluctuations. Empirical findings reveal that countries such as Luxembourg, the Netherlands, and Ireland exhibit superior energy and environmental performance, whereas countries like Romania and Poland rank significantly lower. Overall, the proposed framework serves as an effective tool for decision analysis and policymaking in the field of energy and environmental management. https://aie.ut.ac.ir/article_106336_cffb199a3fa0ee49ec1496f12f7b8bd2.pdfUniversity of Tehran PressAdvances in Industrial Engineering2783-174460120260601Development of an Inventory Management Strategy Model (Two-Products) Based on Demand Predicting in Digital Supply Chain Networks by Combining Data Analysis Methods16518410609110.22059/aie.2026.406483.1962ENEhsanMardanAssistant Professor, Department of Industrial Engineering, Semnan University, Semnan, Iran.0000-0002-3611-2683AliQorbaniM.Sc., Department of Industrial Engineering, Semnan University, Semnan, Iran.0000-0003-0850-8661Journal Article20251117Recording and classifying commerce data electronically within the new age of information and development has many benefits for sellers and consumers in the online supply chain. We can predict customer purchase behavior patterns by analyzing this classified data. In recent years, digital stores have received additional attention due to their advantages. On the other hand, the performance of these stores is directly associated with the performance of their suppliers. Hence, supply chain management is essential during this sales system. In this investigation, the classification methods (WFRM), demand prediction analysis (Binary Logistic Regression), data classification (Discriminant Analysis), time series analysis (Trend Analysis), and mathematical modeling have been used to select suppliers to order led to the development of a management strategy to prevent shortages and reduce the average inventory and costs of sending and producing for suppliers. It ultimately covers a 29% error in sales data for assigning suppliers to orders.https://aie.ut.ac.ir/article_106091_7f827b8e863d484b8b48234c2491fd8a.pdfUniversity of Tehran PressAdvances in Industrial Engineering2783-174460120260601Sustainable Urban Development through Optimizing Urban Agriculture: A Comprehensive Study on Location, Technology, and Gender Equality in Kermanshah, Iran18521310436910.22059/aie.2025.402183.1952ENEhsanHeydari SeChakaniPh.D. Candidate, Department of Industrial Engineering, School of Engineering, University of Kurdistan, Kurdistan, Iran.0009-0005-1637-4116AbdolsalamGhaderiAssociate Professor, Department of Industrial Engineering, School of Engineering, University of Kurdistan, Kurdistan, Iran.0000-0001-9678-023XJamal SArkatProfessor, Department of Industrial Engineering, School of Engineering, University of Kurdistan, Kurdistan, Iran.0000-0002-9973-7269Journal Article20250911Rapid urban growth, driven by urbanization and agglomeration of goods and services in metropolitan cities, jeopardizes land conversion for agriculture and challenges traditional models of urban development. The traditional urban growth models emit carbon, destroy the environment, and create food deserts, thereby compromising food security and health for urban residents. Sustainable urban agriculture can be an alternative solution since it provides food security and access to fresh and affordable food. The present research employs a two-stage model: regional ranking with an integrated ANP-TOPSIS method, and best allocation of resources, such as location selection, cultivation technology, and gender-balanced human resource deployment, with the mixed integer programming methodology. Implemented in Iran, Kermanshah, the proposed model recommends vertical hydroponic production of cauliflower and tomatoes to reduce water and land use. It also promotes gender balance in working opportunities, thus lessening the disparity among men and women in job activities. Economic evaluation using the net present value method reaffirms the economic viability of urban farming, with a mention of the effect of land price and return on investment. Analysis of the ANP-TOPSIS model establishes that increasing the level of sustainability raises the farm's sustainability, albeit non-linearly in all the dimensions and sub-criteria. This approach supports observations regarding effective urban agriculture practices towards sustainable urban development.https://aie.ut.ac.ir/article_104369_1d4f61e50e0fab5b9f19fdf75c395b91.pdfUniversity of Tehran PressAdvances in Industrial Engineering2783-174460120260601Developing a Conceptual Framework for the Design of a Modular Service Platform: The Case of the Logistics Industry21523810456910.22059/aie.2025.401007.1950ENHosseinFarrokhiPh.D, Department of Science, Technology and Innovation Policy, College of Management, University of Tehran, Tehran, Iran.0009-0006-0739-1472FatemehSaghafiAssociate Professor, Department of System Management and Decision Science, Faculty of Technology and Industrial Management, College of Management, University of Tehran, Tehran, Iran.0000-0003-4843-6885SoroushGhazinooriProfessor, Department of Technology and Entrepreneurship Management, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran.0000-0001-6356-0257Journal Article20250827Despite growing interest in digital transformation and modular services, many logistics firms—especially in Iran—lack a cohesive framework that integrates modular architecture with operational and technological needs amid rising complexity and customer demands. This study aims to develop a conceptual framework for a modular logistics service platform that enhances flexibility, innovation, and collaboration across supply chain actors. The research identifies core service modules and examines how modularity can support the design of efficient, adaptive service offerings. Using a qualitative case study approach, the study investigates one of Iran’s leading courier companies. Data were collected through semi-structured interviews with ten senior managers, direct observations, and analysis of internal documents. Thematic content analysis revealed key service modules, processes, and a three-layer modular architecture consisting of service, process, and activity layers. These are structured around the First Mile, Mid Mile, and Last Mile segments, incorporating nodes, links, and carriers as core elements. The platform supports modular processes such as routing and packaging, enables outsourcing at multiple levels, and integrates Artificial Intelligence (AI), and Internet of Things (IoT) technologies to optimize performance. The framework addresses major gaps in existing literature, including role definition, modular governance, smart technology integration, and service scalability. This research offers a novel, multi-level modular logistics framework validated in a real-world context, providing a practical blueprint for logistics firms seeking to transition to flexible modular platforms that enhance efficiency and collaboration.https://aie.ut.ac.ir/article_104569_d3deed73b39de3801561618f52c53c90.pdfUniversity of Tehran PressAdvances in Industrial Engineering2783-174460120260601Exchange-Based Industry Selection for Eco-Industrial Parks: A Mixed-Integer Programming Mathematical Model23925410491810.22059/aie.2025.398445.1949ENYasamanFallahpourPh.D. Candidate, Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran.0000-0002-9164-8528EmadRoghanianProfessor, Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran.0000-0001-7969-3783DonyaRahmaniAssociate Professor, Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran.0000-0002-0040-5206Journal Article20250712The optimal use of natural resources and energy has become an important issue and has been given attention worldwide due to the increase in population and environmental pollution. In recent years, Eco-industrial parks (EIPs) have gained popularity as a way to make better use of natural resources. In these parks, companies try to cooperate with each other by exchanging materials and energy and pay more attention to environmental issues. Unlike previous models proposed in this field, which were based on existing EIPs and were presented for improvement, we present a new model for creating these parks. In this study, we propose a mixed integer programming (MIP) model, considering real and feasible exchanges, which, taking into account sustainability conditions (economic, social, and environmental), selects industries to establish the park so that the value of exchanges is maximized and the costs of infrastructure construction are minimized. The results show that the selected set of industries leads to economic benefits, where the total value of exchanges exceeds the costs of infrastructure, thus supporting profitability and sustainability.https://aie.ut.ac.ir/article_104918_e00e24cb2fd8d6015514155cf9d9c932.pdfUniversity of Tehran PressAdvances in Industrial Engineering2783-174460120260601A Novel Multi-Sector, Multi-Period DEA Framework for Evaluating Bank Branch Efficiency25527410561710.22059/aie.2026.405941.1960ENMoslemNilchiAssistant Professor, Department of Management, Faculty of Social Sciences and Economics, Alzahra University, Tehran, Iran.0000-0001-8946-4693Seyed HosseinRazavi HajiaghaAssociate Professor, Department of Management, Khatam University, Tehran, Iran.0000-0003-2084-7244Journal Article20251109From an economic perspective, efficiency refers to the optimal use of resources to produce the maximum possible output, highlighting its critical role in management systems. Managers generally strive to utilize resources effectively to maximize outputs and satisfy all stakeholders. This is particularly important in the Iranian banking industry, where high costs of fund maintenance make efficiency a central concern. In this study, considering the operational structure of Iranian banks, a five-sector model is proposed to illustrate the flow of activities within banks. Based on this structure, a mathematical model using Data Envelopment Analysis (DEA) is developed to evaluate the efficiency of the five sectors. A fuzzy-based approach is then introduced to solve the model. Application of the proposed model to 210 branches of a major Iranian bank indicates that, while the Services and Mobilization of Resources sectors demonstrate relatively high efficiency, the Management sector suffers from significant inefficiency. These findings highlight the need for focused managerial interventions to enhance operational performance in bank branches.https://aie.ut.ac.ir/article_105617_0592cd603d4801529227f55223d0d612.pdf