Journal of Industrial Engineering and Management Studies

Abstract Perishable food supply chains (PFSCs), particularly in the dairy sector, face significant challenges due to product deterioration, quality degradation, and financial risks associated with distribution delays. Despite extensive research on supply chain optimization, a critical gap remains in accurately modeling the dynamic relationship between product shelf-life and selling price—a factor that significantly impacts revenue and risk assessment in real-world operations. This study addresses this gap by developing a multi-objective optimization model for the dairy supply chain in Iran that incorporates: (1) a novel stepwise pricing mechanism based on remaining shelf-life, capturing revenue loss due to spoilage; (2) financial risk assessment of purchase and distribution operations; (3) transportation planning with vehicle routing; and (4) discount sales policies aligned with product freshness. Given the NP-Hard nature of the problem, NSGA-II and MOPSO algorithms with a modified priority-based encoding-decoding method were employed. Algorithm parameters were systematically tuned using the Taguchi method. The model was validated through a numerical example solved via the LP-metric method, followed by 15 larger test problems to evaluate algorithm performance. Comparative analysis using multiple evaluation metrics—including the number of Pareto-efficient solutions (NPF), maximum spread index (MSI), spacing metric (SM), and computational time—was conducted. The TOPSIS technique was applied to rank algorithm performance, revealing that NSGA-II (weight = 0.6945) significantly outperforms MOPSO (weight = 0.3055) across all problem sizes. The key contributions of this research include: (i) introducing a realistic stepwise pricing function linked to perishability, (ii) integrating financial risk into PFSC optimization, and (iii) providing a robust algorithmic framework for large-scale dairy supply chain problems. These findings offer practical guidance for managers seeking cost-effective, risk-aware, and quality-conscious management of perishable food supply chains.

Abstract The rapid digital transformation of the tourism sector has fundamentally altered customer behavior, rendering traditional demographic and transactional segmentation approaches insufficient for modern e-marketing. This study addresses the critical research gap in data-driven customer segmentation by developing a multidimensional clustering framework tailored to the Iranian tourism industry. Utilizing a comprehensive dataset of 6,000 digital tourism consumers, the research employs the K-Means clustering algorithm integrated with advanced validation indices, including the Silhouette coefficient, Within-Cluster Sum of Squares (WCSS), and the Elbow method. The methodology encompasses rigorous data preprocessing, Min-Max normalization, and the derivation of five strategic customer value dimensions: Customer Lifetime Value (CLV), Customer Referral Value (CRV), Customer Influencer Value (CIV), Customer Brand Value (CBV), and Customer Knowledge Value (CKV). The clustering analysis identifies three distinct, statistically valid customer segments, with an optimal Silhouette score of 0.562 and a stabilized inertia decline at K=3. The resulting segments reveal heterogeneous behavioral profiles: a low-value, high-churn-risk group requiring onboarding optimization; a stable, high-retention group demanding loyalty reinforcement; and a high-value, high-influence group necessitating strategic referral and co-creation initiatives. Key numerical findings demonstrate that Cluster 2 contributes disproportionately to total customer value (TCV) while exhibiting superior brand engagement and influencer metrics. The study’s managerial implications emphasize precision resource allocation, hyper-personalized e-marketing campaigns, and dynamic CRM routing. Theoretically, this research extends customer value literature by validating a multidimensional clustering architecture in an emerging market context. By replacing heuristic segmentation with algorithmic, behavior-driven profiling, the framework provides tourism managers with a scalable, actionable tool for enhancing digital marketing efficiency and sustainable competitive advantage.

Abstract The wood and paper industry is considered one of the key and strategic industries in the country's economy. Mazandaran, as one of the important provinces in the production and supply of raw materials for this industry, plays a significant role in meeting domestic and export needs. However, this industry faces numerous challenges, including the sustainable supply of resources, effective cost management, maintaining product quality, and compliance with environmental regulations. In this regard, supply chain optimization is proposed as an effective solution to increase efficiency and reduce costs. This research has examined the optimization of the open-loop supply chain in the Mazandaran wood and paper industry using an integrated multi-objective mathematical model. Given the specific challenges of this industry, including the supply of sustainable raw materials and the need to reduce costs, this model has been able to simultaneously pay attention to various criteria, including price, environmental sustainability, and the importance of suppliers. The results of this research show that by using advanced optimization methods, appropriate choices can be made in terms of vehicles and transport routes, and the volume of products moved between chain components and supply chain performance can be improved. In addition, this model can serve as an effective decision-making tool for managers of the wood and paper industry in Mazandaran and contribute to the sustainable development of this industry.

Abstract Abstract
Unplanned pump failures inflict high operational costs, rendering traditional maintenance strategies ‎inefficient. While Machine Learning (ML) has advanced fault diagnosis, a critical gap remains in ‎simultaneously classifying operational states and predicting the "Time Remaining until a State Change" ‎‎(TRSC) using real-world, imbalanced data. This study addresses this necessity by developing an integrated ‎predictive maintenance framework for industrial centrifugal pumps. Leveraging a four-year vibration ‎dataset (2020–2024), we employ Random Forest (RF), XGBoost, and Multi-Layer Perceptron (MLP), ‎utilizing SMOTE and jittering augmentation to mitigate data scarcity and imbalance. The study makes two ‎primary contributions: (1) accurate classification of four operational states (Normal to Failure), and (2) ‎precise regression of TRSC to optimize spare parts logistics. Results indicate that ensemble models ‎‎(RF/XGBoost) achieve classification accuracies exceeding 92% and TRSC prediction with an RMSE ‎below 25 days, significantly outperforming MLP. Furthermore, SHAP analysis reveals that horizontal and ‎axial vibrations are the dominant precursors to failure. These findings offer a robust, interpretable tool for ‎shifting towards condition-based maintenance, ensuring reliability and cost efficiency.‎

Abstract The relentless pursuit of efficiency in open-pit mining, driven by escalating operational costs and stringent environmental mandates, places the optimization of haulage systems at the forefront of mining research. This study addresses the complex dispatching challenge within the shovel-truck system at the Sarcheshmeh open-pit copper mine (Kerman Province, Iran) to simultaneously maximize monthly throughput and minimize transportation costs. We introduce a robust simulation-optimization framework where a Discrete-Event Simulation (DES) model, developed in Arena, is tightly coupled with two leading evolutionary multi-objective optimization algorithms: the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Fast Pareto Genetic Algorithm (FastPGA). This integration facilitates the exploration of the high-dimensional, non-convex Pareto front to identify a set of near-optimal fleet compositions (number of shovels and trucks). Experimental validations demonstrate significant operational improvements: the derived optimal solutions lead to a quantifiable reduction of up to 10% in average monthly transportation costs and an increase of up to 11% in average monthly material throughput. These findings offer critical managerial insights, establishing a data-driven blueprint for mining operators to adopt advanced meta-heuristic optimization techniques to enhance operational sustainability and economic performance in dynamic pit environments.

Abstract Purpose:Many studies, particularly in international literature, have addressed the utilization of this highly beneficial technology in the supply chain. Therefore, the current study is an effort to study blockchain technology to develop the application of the modern supply chain in contrast to the traditional one. This research was carried out with the purpose of identifying the components of key success factors for blockchain, focusing on opportunities and the impacts of these factors alongside modern technology. It seeks to prepare MAPNA Boiler and Equipment Engineering and Manufacturing Company to confront the chaotic trend of challenges during the age of the Fourth Industrial Revolution.
Design/methodology/approach:Regarding its objective, this study is applied in nature, and with respect to the data collection approach, it is descriptive. For data gathering, qualitative methods including expert interviews and content analysis were utilized. In the quantitative section, a questionnaire was employed with a statistical population comprising a number of experts in this field at MAPNA Boiler company. The result was the identification of 41 factors categorized into 7 main groups. Ultimately, using the Pairwise Comparison Matrix (Weighted Sum Vector) and the Step-wise Weight Assessment Ratio Analysis (SWARA) technique.
Findings: According to the results, the most important components such as data quality, speed, process simplification, system guidance and control, system productivity, security, and transparency were ranked.
Originality/value:Blockchain is considered a lean and practical tool with distinctive characteristics such as its decentralized architecture, distributed nodes, storage mechanism, consensus algorithm, smart contracts, security, and transparency.