https://imj.ut.ac.ir/ Industrial Management Journal en daily 1 Sun, 01 Feb 2026 00:00:00 +0330 Sun, 01 Feb 2026 00:00:00 +0330 https://imj.ut.ac.ir/article_105865.html Objective: 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. Methodology: 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. Results: 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. Conclusion: 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_106266.html Objective: 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.Methodology: 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.Results: 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.Conclusion: 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_106268.html  Objective: 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).Methodology: 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.Results: 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.Conclusion: 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_106269.html Objective: 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.Methodology: 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.Results: 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.Conclusion: 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_106270.html Objective: 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. Methodology: 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. Results: 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. Conclusion: 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_106272.html Objective: 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. Methodology: 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. Results: 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. Conclusion: 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.