Industrial Management Journal (مدیریت صنعتی)

Objective : This research aims to identify recurring carbon reduction policies and their key variables, and analyze their relationships within pharmaceutical distribution in Iran. Methods : A mixed-method of qualitative and quantitative approaches was adopted. Firstly, a systematic literature review was employed to identify the policies and variables. Afterward, the Intuitive Fuzzy DEMATEL (Decision Making Trial and Evaluation Laboratory) method was used to analyze the causal relationships among identified variables in uncertain conditions. Following snowball sampling, data were gathered through expert questionnaires from 15 specialists in five groups, selected based on relevant expertise in carbon reduction policies, particularly for pharmaceutical distribution companies in Iran.  Results : The study identified three key carbon reduction policies—cap-and-trade, subsidy allocation, and financial penalties—all shaped by distinct variables. Cap-and-trade includes the emission cap, carbon selling price, and demand for carbon emission permits. Subsidy allocation and financial penalties cover the subsidy amount and the penalty rate, respectively. Notably, carbon emission level emerged as the most influential factor in shaping policy effectiveness, while carbon reduction cost was identified as the most impactful variable. These two variables are integral to all three policies, highlighting their pivotal role in policy formulation. While the demand for carbon emission permits remains neutral regarding influence and susceptibility, other variables demonstrate complex interdependencies, creating a dynamic system where policies interact directly through primary variables or indirectly through shared criteria. Conclusion : This study contributes to environmental policy research by offering an analytical framework that integrates uncertainty logic to assess relationships among key variables. The findings suggest that implementing a single policy may not be sufficient—a combination of strategies is recommended for more effective carbon reduction. Understanding how variables interact can help policymakers and businesses design targeted and efficient pharmaceutical distribution strategies in Iran.

Objective : This paper introduces a groundbreaking optimization-simulation model, a novel approach that promises to revolutionize credit scoring and loan optimization for banks. Methods : The proposed approach follows a three-stage framework: data preparation, credit scoring, and optimization simulation. In the data preparation stage, corporate client data, including bank loan information and financial statements, has been collected and processed to define and calculate relevant features. The credit scoring stage involved meticulous feature selection using the correlation method, followed by the rigorous training and testing of five classification methods: logistic regression (LR), K-nearest neighbors (KNN), artificial neural network (ANN), adaptive boosting (AdaBoost), and random forest (RF). Model performance has been evaluated using accuracy, F1-score, and area under the curve (AUC) to identify the most effective classifier. In the optimization-simulation stage, the Memetic Algorithm (MA) has been utilized to optimize loan characteristics, including loan size, interest rate, and repayment period, while minimizing the rate of loan defaults. Additionally, this stage incorporated the pre-trained credit scoring model to estimate the impact of loan characteristics on default probabilities.  Results : A case study was conducted using data from 1,000 corporate clients of Bank Tejarat. The optimization-simulation approach has successfully reduced the loan default rate from 33% to below 5%, a significant achievement that underscores its potential to mitigate banks' credit risk. This shows the effectiveness of the proposed method in reducing credit risk for banks. Additionally, the AdaBoost technique achieved the best performance among the credit assessment models. Conclusion : The optimization-simulation approach combines determining the optimal loan specifications with the credit assessment process. This approach considers the impact of loan characteristics on the likelihood of customer default and utilizes this information to reduce banks' credit risk

Objective : The construction industry has been increasingly criticized for its poor sustainability performance in recent decades, creating a chance for the sector to play a key role in global sustainability efforts. Rapid technological advancements and increasing construction project complexities have driven the need for flexible, sustainability-focused project management frameworks. This study introduces a fuzzy inference system designed to evaluate construction project sustainability, built on insights from extensive literature and expert input. Methods : To design the proposed model, the system inputs—criteria for evaluating the sustainability level of construction projects at various layers—were first identified. Next, the necessary if-then rules were developed based on expert opinions. The system output was determined in alignment with the research’s final objective. By offering a comprehensive assessment of construction project sustainability, the model enables organizations to identify their strengths and weaknesses, assess their current position, and make informed decisions to enhance their sustainable performance.  Results : The output of the research includes a detailed analysis of the sustainability performance of construction projects. The designed model, along with its measurement tools, provides an opportunity for leaders and managers in the construction industry who are concerned about sustainability to gradually enhance their sustainability status and advance the sustainability level of projects. This model consists of three subsystems named the Direction, Execution, and Results subsystems. The aforementioned subsystems are the result of a literature review and are considered inputs to the final level of the model. Conclusion : The designed model serves as a tool to identify and implement improvement methods and potential areas for project advancement from a sustainability perspective. By utilizing this model, the quality of project execution in line with sustainability indicators, while addressing all three dimensions—economic, social, and environmental—improves continuously and proportionately.

Objective : This research aims to propose a multi-criteria decision-making model for ranking hospital departments. The primary purpose of this model is to assist managers in the optimal allocation of limited resources, thereby reducing costs while increasing patient satisfaction. The ranking results help managers in decision-making processes such as equipment development, staff training, and addressing patient complaints. Methods : This study evaluated the performance of five hospital departments (Emergency, Ophthalmology, Cardiovascular, Infectious, and Neurology) in Shiraz, Iran, using the fuzzy DEMATEL-MARCOS multi-criteria decision-making method. Firstly, criteria were prioritized using the fuzzy DEMATEL method, after which hospital departments were ranked using the fuzzy MARCOS method. A sensitivity analysis was conducted to validate the results. Results : Performance metrics for the hospital departments were identified based on the Lean, Agile, Resilient, Green, and Sustainable (LARGS) paradigm. The results revealed that patient satisfaction and job satisfaction had the most substantial influence on performance, while reducing excess transportation and over-processing had the least impact. Utilizing the fuzzy MARCOS method, the hospital departments were ranked according to their overall desirability. The sensitivity of these rankings was assessed by adjusting the weights of the criteria. A comparative analysis with four other fuzzy methods (ARAS, COCOSO, EDAS, and WASPAS) confirmed that the fuzzy MARCOS method was the most effective tool for prioritizing hospital departments. Conclusion : The fuzzy MARCOS results indicated that the “Infectious Department” performed well, while the “Ophthalmology Department” required improvement. Enhancing the “Infectious Department” hinged on better staff training, cost reduction, and safe waste management. This research introduces a novel approach using the fuzzy DEMATEL-MARCOS model, enabling hospitals to assess performance through modern methodologies, such as Lean, Agile, Resilient, Green, and Sustainable, even in uncertain conditions

Objective : In the Fourth Industrial Revolution, advanced technologies are revolutionizing supply chains by enhancing data collection, processing, and analysis across material, financial, and information flows. This shift enables businesses to adopt intelligent supply chain processes with unprecedented efficiency. The integration of intelligent strategies and process-oriented approaches, supported by tools like Intelligent Business Process Management Systems (iBPMS), holds transformative potential for supply chain management, paving the way for Intelligent Supply Chain Management (iSCM) models. This study aims to identify the key dimensions and sub-dimensions of intelligent supply chain processes within the context of Industry 4.0 technologies. Methods : The research employs a meta-synthesis methodology, systematically reviewing peer-reviewed literature and international publications from 2016 to 2025. Following strict meta-synthesis protocols, the study involved keyword screening, thematic evaluation, and iterative refinement, resulting in a curated selection of 62 high-impact journal articles and 4 seminal books. These sources underwent rigorous validation to ensure scholarly relevance before analysis.  Results : The findings identified 117 open codes related to intelligent supply chain processes, which were consolidated into 18 core codes and further classified into five key dimensions: (1) Intelligent Supply Chain Management (covering SCM and intelligent procurement); (2) Process Intelligent Automation (including automation approaches, intelligent processes, and equipment); (3) Process Management (focused on process-oriented approaches, systems, and modeling); (4) Technological Infrastructure (encompassing emerging technologies, ICT infrastructure, software maturity, and robotics); and (5) Macro & Structural Dimensions (addressing managerial, industrial, e-business, market, and organizational factors). Conclusion : The study concludes that Industry 4.0 technologies—such as IoT, AI, blockchain, robotics, and big data analytics—facilitate advanced data-driven supply chain management. When integrated with iBPMS, these innovations enhance efficiency, agility, and end-to-end visibility, establishing a foundation for next-generation intelligent supply chains

Objective : This study tackles the reviewer assignment problem by proposing a model that optimizes reviewer-proposal matching based on thematic coverage, fairness, and expertise, while considering workload balance and team size constraints. The model incorporates practical constraints such as limits on the number of proposals each reviewer can handle and team composition requirements. This approach is especially relevant to institutions like academic conferences, journals, and funding organizations, aiming to enhance the integrity and efficiency of the review process. Methods : This study is classified as descriptive research with a practical orientation and relies on data collection through applied methods. The approach is grounded in mathematical modeling. Initially, the selected articles are grouped into clusters. Reviewers are then assigned to these clusters using a multi-objective binary integer programming model that incorporates all relevant criteria and constraints. To implement this model, 150 articles were selected through purposive sampling. The model was optimized using Python, employing both the branch-and-bound algorithm and a genetic metaheuristic algorithm to maximize the degree of reviewer-proposal matching within the proposed framework.  Results : The proposed model demonstrates strong practical relevance by closely reflecting real-world reviewer assignment challenges. By simultaneously optimizing thematic coverage, evaluation fairness, and reviewer expertise, the model captures the complexity of actual allocation scenarios. To validate its effectiveness, the model was solved using both the branch-and-bound algorithm and a genetic algorithm. The branch-and-bound method yielded an objective value of 177.349 in approximately one hour, while the genetic algorithm reached 120.35 in just seven minutes. Although branch-and-bound guarantees optimality, its longer runtime makes it less practical for larger datasets. Given the similarity of results, the genetic approach is a reliable and scalable alternative. Conclusion : This study introduces a new allocation strategy and mathematical model for reviewer assignment, addressing often-overlooked factors such as reviewer expertise, grouping, and conflicts of interest. By integrating these elements, the proposed model better reflects real-world conditions. Future work is encouraged to expand on these findings with new frameworks and methods.

Objective : This research aims to propose a large-scale vehicle routing model for the distribution network of a food industry product and apply the model in a real-world case study. Methods : A mathematical model is formulated to minimize the total variable transportation costs.  Considering the complexity of the model, a constrained clustering algorithm is used to decompose the problem. Then, vehicles are assigned to demand clusters according to their capacity. Finally, each cluster's symmetric traveling salesman problem (TSP) is solved using a genetic algorithm. The parameters of the proposed genetic algorithm were calibrated based on its widespread application in solving symmetric TSPs. A conservative approach was adopted to ensure the solution's validity by evaluating a worst-case scenario considering the highest node demands.  Results : By applying the proposed algorithm to the case study, over 2,000 demand nodes across Tehran were grouped into 91 clusters. Then, based on the demand level of each cluster, the vehicles are assigned, consisting of 26 small and 65 large cars. Within each cluster, the assigned vehicle followed an optimized route among the nodes, designed based on the optimal tour generated by solving the cluster-specific TSP using the genetic algorithm, and then returned to the central warehouse. Conclusion : Comparing the results with the current situation, the size of the proposed transportation fleet showed a 40% reduction. Additionally, reducing fleet size and optimizing the routes improved the total distribution network costs by 25%. Given the model's computational efficiency, this improvement is considered satisfactory.