Univrsity of Tehran PressIndustrial Management Journal3115-738618220260201Integrated Assortment–Shelf Optimization under Substitution and Space Elasticity: A Hybrid Memetic Algorithm13310586510.22059/imj.2026.404854.1008268ENAliEbrahimi KordlerAssociate Prof., Department of Accounting and Finance, College of Management, University of Tehran, Tehran, Iran.SalehMansouriPh.D. Candidate, Department of Industrial Management, School of Management, Kish International Campus, University of Tehran, Kish Island, Iran.FarzadBahramiAssistant Prof., Department of Industrial Management, Faculty of Administrative Sciences and Economics, Arak University, Arak, Iran.Journal Article20251023<strong>Objective</strong>: This study maximizes expected retail profit by jointly optimizing product assortment and shelf-space allocation, considering substitution effects and space-elastic demand. The problem’s NP-hardness, compounded by category-level bounds and store capacity, renders exact methods impractical for large-scale instances. Therefore, we develop a computationally efficient framework to generate near-optimal shelf plans aligned with real-world constraints.
<strong>Methodology:</strong> We propose a hybrid Memetic Algorithm embedded with Iterated Local Search (ILS), combining evolutionary global exploration with local hill-climbing refinement. A two-phase initialization ensures every candidate planogram satisfies capacity constraints. Each chromosome encodes item-to-shelf mappings, with facings emerging endogenously. Mechanisms, including crossover, mutation, and diversity control, preserve solution validity and mitigate premature convergence. The framework was validated using real data from the Iranian retail chain Ofoq Kourosh, encompassing 39 product categories and 21,000 cm of total shelf length.
<strong>Results</strong>: The algorithm consistently converged toward feasible solutions. Under the original category bounds, profit reached 3.04 million; relaxing these bounds improved profit by 6.2% to 3.25 million. Allocation outcomes aligned with demand elasticity: impulse-driven categories reached upper limits, while low-elasticity staples stabilized near minima. Pareto analysis confirmed that roughly 20% of categories generated over 80% of profit. Notably, the optimized solution resulted in a 37% increase in profit compared to the current store configuration.
<strong>Conclusion</strong>: Results confirm the efficacy of hybrid metaheuristics for complex retail optimization. The framework consistently achieved near-optimal solutions under realistic constraints. Managerially, shelf-space allocation should prioritize high-elasticity categories while maintaining a minimal representation of staples. Future research should extend this framework to multi-store and omni-channel contexts with dynamic demand modeling.https://imj.ut.ac.ir/article_105865_c45ddfebeee9ffecfa0288c8d769addd.pdfUnivrsity of Tehran PressIndustrial Management Journal3115-738618220260201ED-ELEC: A Robust Multi-Criteria Decision-Making Framework for Evaluating Sustainable Urban Delivery Systems347310626610.22059/imj.2026.405611.1008271ENMortezaDaripour. PhD Candidate, Faculty of Industrial Management & Technology, College of Management, University of Tehran, Tehran, Iran.EzzatollahAsgharizadehProf., Faculty of Industrial Management & Technology, College of Management, University of Tehran, Tehran, Iran.MohammadrezaTaghizadeh-YazdiProf., Faculty of Industrial Management & Technology, College of Management, University of Tehran, Tehran, Iran.Journal Article20251104<strong>Objective</strong>: The rapid expansion of online food delivery platforms has intensified the need for sustainable and efficient logistics solutions. Evaluating delivery alternatives across economic, environmental, technical, and social dimensions requires robust multi-criteria decision-making (MCDM) tools capable of managing incomplete, uncertain, and conflicting expert judgments. This study develops and validates ED-ELEC, an enhanced MCDM framework that extends D-numbers theory and integrates it with the ELECTRE III outranking method to enable reliable and transparent decision-making under imperfect information.<br /><strong>Methodology:</strong> A new integration function for D-numbers is introduced to convert incomplete expert evaluations into crisp values while retaining the effects of incompleteness and conflict. A two-stage validation was performed: first, an illustrative example compared the proposed integration function with existing ones and benchmarked the complete ED-ELEC framework against another hybrid method; second, the framework was applied to evaluate sustainable delivery systems involving gasoline motorcycles, electric motorcycles, and drones.<br /><strong>Results</strong>: The proposed integration function achieved balanced sensitivity to uncertainty and better interpretability. ED-ELEC produced robust, transparent, and stable rankings, identifying electric motorcycles as the most sustainable option, followed by drones and gasoline motorcycles.<br /><strong>Conclusion</strong>: The ED-ELEC framework effectively combines uncertainty modeling and outranking analysis, providing an adaptive decision-support tool for sustainability evaluation. It enables decision-makers to address incomplete data while improving ranking clarity and robustness in sustainable urban logistics planning.https://imj.ut.ac.ir/article_106266_003f6901d7f109e9c454d19f7211fdb0.pdfUnivrsity of Tehran PressIndustrial Management Journal3115-738618220260201A Model of Export Marketing Performance for SMEs: The Role of Fourth-Generation Technology in the Medical Equipment Industry749510626810.22059/imj.2026.407295.1008274ENZahraMohammadiMSc student, Department of Management, Faculty of Economic and Administrative Sciences, University of Qom, Qom, Iran.Mohammad HasanMalekiProf., Department of Management, Faculty of Economic and Administrative Sciences, University of Qom, Qom, Iran.SeddighehKhorshidAssiatant Prof., Department of Management, Faculty of Economic and Administrative Sciences, University of Qom, Qom, Iran.Journal Article20251205 <br /><strong>Objective</strong>: This study aims to develop a comprehensive model to identify the key Industry 4.0–driven drivers influencing export marketing performance in Iranian medical equipment small and medium-sized enterprises (SMEs).<br /><strong>Methodology:</strong> This research is quantitative and application-oriented, employing a two-phase methodological approach. First, 30 potential drivers were identified through a systematic literature review and expert consultations with ten specialists in export marketing and Industry 4.0 technologies. These drivers were screened using the Fuzzy Delphi method, yielding 11 drivers with defuzzified values above the 0.7 threshold. In the second phase, a cross-impact analysis was conducted using the MicMac software to examine the structural relationships, influences, and dependencies among the selected drivers.<br /><strong>Results</strong>: The results indicate that four drivers—AI-based export data analytics, ML-based optimization of export marketing decisions, AI-enabled customer behavior analysis, and IoT-enabled export quality and standards control—are the most influential determinants of export marketing performance. AI-based analytics and ML-driven decision optimization are bidirectional and risk-prone, forming the strategic nucleus of the system. AI-enabled customer behavior analysis and IoT-based quality control are key influential variables, underscoring the importance of market intelligence, quality assurance, and traceability.<br /><strong>Conclusion</strong>: The study demonstrates that the synergistic adoption of Industry 4.0 technologies can address structural weaknesses in export marketing, including limited market intelligence, weak forecasting, and low supply chain transparency. The proposed model offers a practical framework for policymakers and firms to prioritize digital investments and achieve sustainable export growth in emerging markets.https://imj.ut.ac.ir/article_106268_bc124ae7eefcfdd05f6c9809858c93cd.pdfUnivrsity of Tehran PressIndustrial Management Journal3115-738618220260201Smart Maintenance in the Cement Industry: A Meta-Synthesis of Industry 4.0 Technologies and Applications9612010626910.22059/imj.2026.408275.1008276ENMohadeseBourghani FarahaniM.Sc. Student of Production and Operations Management, Faculty of Industrial Management and Technology, College of Management, University of Tehran, Tehran, Iran.RohollahGhasemiAssistant Prof., Faculty of Industrial Management & Technology, College of Management, University of Tehran, Tehran, Iran.AliMohagharProf., Faculty of Industrial Management & Technology, College of Management, University of Tehran, Tehran, Iran.ArzhangKhoshghalbMSc. of Supply Chain Management , Department of Logistics and Operations, Business School, HEC Montreal University, Quebec, Canada.Journal Article20251216<strong>Objective</strong>: The cement industry, vital to infrastructure and the economy, faces challenges such as high energy use, significant environmental emissions (nearly a ton per ton of cement), high costs, and unplanned downtime. Traditional maintenance methods are often inefficient and costly. Transitioning to Smart Maintenance—using Industry 4.0 data-driven, predictive, and prescriptive strategies—is essential for survival. It aims to boost efficiency, reduce costs, improve safety, and meet sustainability goals by optimizing energy and materials, making industries more resilient, agile, and future-ready, and future-proofing industrial operations. This research aims to identify, categorize, and explain key Industry 4.0 technologies and their practical applications in smart maintenance of the cement industry. It addresses a gap by providing a structured framework that links these digital technologies to specific maintenance functions, offering clarity for academia and industry in this slow-to-innovate sector.<br /><strong>Methodology:</strong> This study used a rigorous qualitative meta-synthesis to review and synthesize literature, conducting a protocol-driven search across Scopus and Web of Science for peer-reviewed articles from 2020 to 2025. After a multi-stage screening and quality assessment with CASP, 36 high-quality articles were selected. Through inductive coding and thematic synthesis, core technologies and applications were systematically extracted, analyzed, and categorized.<br /><strong>Results</strong>: The meta-synthesis identified 8 key Industry 4.0 technologies for smart maintenance, with 27 applications. These include IoT with 5 applications; Robotics and Drones with 4; and AI & Machine Learning, Digital Twin, AR/VR, Blockchain, Edge & Cloud Computing, and 3D Printing, each with 3 applications. They cover the entire maintenance lifecycle, from predictive analytics and virtual stress simulation to automated inspections, real-time monitoring, blockchain record-keeping, and 3D printing for spare parts.<br /><strong>Conclusion</strong>: This study provides a validated, structured framework that maps the Industry 4.0 ecosystem to smart maintenance in the cement sector. It offers a clear roadmap for stakeholders from plant managers to executives to shift from reactive to proactive, intelligent asset management. The framework guides technological investments and creates a taxonomy for further research. Future studies can explore implementation, ROI, integration, and sustainability impacts, helping the industry achieve resilience, productivity, and environmental goals.https://imj.ut.ac.ir/article_106269_7a803086f4c5a551f0883f5cdae75c06.pdfUnivrsity of Tehran PressIndustrial Management Journal3115-738618220260201Evaluating Financial Performance of Exchange-Listed Companies with Integrated DEA and Malmquist Index12114910627010.22059/imj.2026.409783.1008283ENAbdol HosseinJafarzadehPh.D. Candidate, Faculty of Industrial Management and Technology, College of Farabi, University of Tehran, Iran.Mohammad RezaFathiAssociate Prof., Faculty of Industrial Management and Technology, College of Farabi, University of Tehran, Iran.Can DenizKÖKSALProf., Akdeniz University, Antalya, Turkey.Journal Article20260117<strong>Objective</strong>: A stock exchange is a formal market where companies' shares are traded. Therefore, examining the efficiency of companies listed on the stock exchange is of significant importance. One of the main shortcomings of existing financial performance evaluation methods is their emphasis on a single key indicator and their reliance on subjective judgments. This study aims to evaluate the financial performance of selected companies listed on the stock exchange using a hybrid approach combining Data Envelopment Analysis (DEA) and the Malmquist Productivity Index.
<strong>Methodology:</strong> In this research, to overcome the limitations of traditional analyses based on financial ratios, such as their one-dimensional nature, potential to be misleading, and difficulty of interpretation, the Data Envelopment Analysis technique is employed to assess corporate performance. This method aggregates multiple financial ratios and assigns each company a single score, called efficiency. Moreover, the Malmquist Productivity Index, an important concept in DEA, is used to evaluate changes in the efficiency of a decision-making unit over two time periods.
<strong>Results</strong>: Based on this study's results, in almost all years, the symbols Sebahan in the mining industry, Khodro in the automotive industry, and Foolad in the basic metals industry ranked first. Therefore, it is recommended that investors considering these industries base their investment decisions on these results.
<strong>Conclusion</strong>: This article presents a combined application of Data Envelopment Analysis and the Malmquist Productivity Index and evaluates the financial performance of selected companies listed on the stock exchange.https://imj.ut.ac.ir/article_106270_60a392cdab7dbbb84e72ba2f1b132608.pdfUnivrsity of Tehran PressIndustrial Management Journal3115-738618220260201A Systematic Review of Project Scheduling Models under Renewable Resource Constraints and Uncertainty15018210627210.22059/imj.2026.404865.1008269ENBabakEjlalyPh.D. Candidate in Industrial engineering Technology Management, Faculty of Industries and Management, Malek Ashtar University of Technology, Tehran, Iran.MortezaAbbasiAssistant Prof., Faculty of Industries and Management, Malek Ashtar University of Technology, Tehran, Iran.MohammadrezaZahediAssociate Prof, Faculty of Industries and Management, Malek Ashtar University of Technology, Tehran, Iran.MahdiYousefi Nejad AttariAssociate Prof., Department of Industrial Engineering, Bon.C., Islamic Azad University, Bonab, Iran.Journal Article20251023<strong>Objective</strong>: This study aims to develop a comprehensive resource-constrained project scheduling model (RCPSP) that accounts for uncertainty in activity durations and resource availability, thereby addressing the limitations of deterministic approaches.
<strong>Methodology:</strong> A mathematical formulation of the RCPSP is extended to an uncertain environment (URCPSP), considering renewable and semi-renewable resources under multiple constraints. The proposed framework integrates deterministic and stochastic components to better handle resource conflicts and project disruptions.
<strong>Results</strong>: Results indicate that classical RCPSP models fail to represent real-world project dynamics. Incorporating uncertainty and mixed resource types enhances scheduling flexibility and solution robustness while minimizing total project duration and resource fluctuation.
<strong>Conclusion</strong>: The proposed model provides a unified framework for project scheduling under uncertainty, supporting decision-making in complex environments. Future work may extend the model to multi-project contexts and advanced metaheuristic optimization techniques.https://imj.ut.ac.ir/article_106272_de350d8122c18a80b5b7fd747a09d3f9.pdfUnivrsity of Tehran PressIndustrial Management Journal3115-738618220260201Integrating Stock Levels into Demand Forecasting and Discount Optimization for Retail18320710655410.22059/imj.2026.408958.1008279ENArezouManosuriPh.D. Candidate, Department of Technology and Innovation Management, Faculty of Industrial Management and Technology, College of Management, University of Tehran, Tehran, Iran.MansourMomeniProf., Department of Technology and Innovation Management, Faculty of Industrial Management and Technology, College of Management, University of Tehran, Tehran, Iran.MinaKazemi MiyangaskaryPostdoctoral Researcher, Department of Management, University of Québec at Trois-Rivières, QC, Canada.Journal Article20251227<strong>Objective</strong>: Accurate sales forecasting is crucial for inventory management and supply chain planning in retail settings with high product variety, short life cycles, and volatile demand. Errors cause excess stock, stock-outs, lost sales, suboptimal promotions, higher costs, reduced customer satisfaction, and brand damage. Reliable forecasts drive replenishment, pricing, promotion design, inventory allocation, and profitability. As retailers embrace data-driven strategies, models must account for interactions among demand, pricing, and inventory. This study introduces a deep neural network framework that jointly tackles demand forecasting and discount optimization.
<strong>Methodology:</strong> Unlike traditional approaches that ignore inventory limits, the model uses store-level stock availability as an input. This captures reality helping separate true demand from inventory-shortage effects. Experiments show this inventory-aware method substantially outperforms baselines without stock data on standard accuracy metrics. The framework also features a heuristic ladder search algorithm for discount optimization. It uses deep learning forecasts to evaluate discrete discount options, balancing demand uplift, remaining inventory, and profit margins. This prevents excessive markdowns that hurt margins or weak discounts that leave excess stock.
<strong>Results</strong>: Tested on real-world data across products and stores, the approach yields better forecasts and notable profit gains by aligning pricing with inventory and demand.
<strong>Conclusion</strong>: Overall, integrating demand forecasting and discount optimization outperforms separate handling, delivering retailers a practical tool to enhance inventory efficiency, promotion effectiveness, and profitability.https://imj.ut.ac.ir/article_106554_8fe05f1d0d257b358dbdbe482176033e.pdfUnivrsity of Tehran PressIndustrial Management Journal3115-738618220260201AI-Powered Supply Chains: Mapping the Future of Resilience and Sustainability20823610655510.22059/imj.2026.410606.1008286ENArashGhadamiPh.D. Candidate, Department of Management, Urmia Branch, Islamic Azad University, Urmia, Iran.MahdiAbdsharafatPh.D. Candidate, Department of Management, Urmia Branch, Islamic Azad University, Urmia, Iran.RezaRostamzadehAssociate Prof. , Department of Management, Artificial Intelligence, Automation, Big Data Research Center, Urmia Branch, Islamic Azad University, Urmia, Iran.HeroIsaviAssistant Prof., Department of Management, Urmia Branch, Islamic Azad University, Urmia, Iran.Journal Article20260206<strong>Objective</strong>: This study aims to present a bibliometric mapping of artificial intelligence applications in supply chain management.
<strong>Methodology:</strong> Performance analysis and scientific mapping are used based on 805 Scopus-indexed journal articles published between 1996 and 15 March 2025.Three research questions are formulated to guide the analysis concerning disciplinary priorities, thematic structure, and international collaboration.
<strong>Results</strong>: we document a marked post-2020 acceleration in publications and citations, identify leading sources and authors, and reveal four interconnected thematic clusters: methodological foundations, digital infrastructures, application and resilience foci, and emerging topics. While resilience and sustainability emerge as salient application lenses within the corpus, our contribution is descriptive and explanatory rather than predictive.
<strong>Conclusion</strong>: The study clarifies conceptual contours, highlights collaboration hubs led by the United States, India, and China, and delineates gaps that motivate future work on data governance, technical integration, and evaluation across sectors.https://imj.ut.ac.ir/article_106555_2728b694e6e6b00fa233ee4c74018c38.pdfUnivrsity of Tehran PressIndustrial Management Journal3115-738618220260201Evaluating Blockchain Technology Implementation Risks in the Automotive Industry: A Fuzzy Decision-Making Approach23726410655610.22059/imj.2026.410718.1008287ENSeyyed JalaladdinHosseini DehshiriAssistant Prof., Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba’i University, Tehran, Iran.MaghsoudAmiriProf., Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba’i University, Tehran, Iran.Journal Article20260207<strong>Objective</strong>: This study aims to identify and prioritize the potential risks associated with adopting blockchain technology in the supply chain of Small and Medium-sized Enterprises (SMEs) operating in the automotive industry.
<strong>Methodology:</strong> A fuzzy Multi-Criteria Decision-Making (MCDM) approach is employed. The Stepwise Weight Assessment Ratio Analysis (SWARA) method is used to determine the relative importance of identified blockchain implementation risks while accounting for ambiguity and subjectivity in expert judgments. The proposed framework is validated through a case study involving SMEs in the automotive sector, where expert evaluations are collected and modeled using fuzzy numbers to improve the robustness and accuracy of the results.
<strong>Results</strong>: The findings indicated that security, technical, and organizational risks were the most important ones. Additionally, the sub-risks of privacy/confidentiality of information, lack of necessary policies and regulations, and cyber-attacks with values of 0.1224, 0.0933, and 0.0927 were recognized as the most critical, respectively. These results highlight the areas that require immediate managerial and regulatory attention to ensure successful blockchain implementation.
<strong>Conclusion</strong>: This study demonstrates that integrating fuzzy sets with SWARA method provides a systematic and reliable approach for assessing blockchain adoption risks under uncertainty. The proposed framework offers practical guidance for supply chain managers and policymakers seeking to mitigate implementation risks and achieve sustainable competitive advantages through blockchain technology.https://imj.ut.ac.ir/article_106556_94aa408ea6db88b3cea4fa57afe51af1.pdf