Shahed UniversityJournal of Quality Engineering and Production Optimization10120250501Smart Power Generation in Combined Cycle Power Plants: Machine Learning Models for Power Prediction and Neural Network-Driven Input Optimization124489610.22070/jqepo.2025.20698.1303ENHaniGilaniSchool of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran,0000-0002-4849-7920HadiSahebiSchool of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran,Journal Article20250715Fossil fuel-based power generation remains a core component of many energy systems. However, growing electricity demand influenced by government policies, economic development, and societal behavior can challenge the stability of power supply networks. This study focuses on improving the productivity of combined cycle power plants through machine learning techniques to better match electricity generation with consumption while minimizing inefficiencies. Several algorithms, including Bagging decision tree, Random Forest, Gradient Boosting, and XGBoost, are implemented to predict energy output. A parameter tuning process is conducted to enhance model accuracy using negative root mean square error as the evaluation metric. The results indicate that Gradient Boosting achieves the highest accuracy, with a mean absolute error of 2.215449. Additionally, a neural network model is developed to optimize key operational parameters such as temperature, ambient pressure, and humidity. This model integrates a Quasi-Newton-based activation function to improve adaptability and precision. The optimal values identified are a temperature of 19.38°C, exhaust vacuum of 25.43 cm Hg, ambient pressure of 1021.4 millibars, relative humidity of 60.8 percent, and an energy output of 462.09 megawatts. Temperature is recognized as the most significant influencing factor. Using real-world data from the Zavareh Combined Cycle Power Plant, the study offers practical insights for enhancing energy sector performance.https://jqepo.shahed.ac.ir/article_4896_0fc946adfdd8e71b43e29101d9d9467c.pdfShahed UniversityJournal of Quality Engineering and Production Optimization10120250501Enhanced Adaptive Thresholding LASSO Control Chart Using Resubmission Method for Detecting Covariance Matrix Deviations in High-Dimensional Processes2546498010.22070/jqepo.2026.20792.1307ENAnahitaFathianDepartment of Industrial Engineering, University of Eyvanekey, Semnan, Iran.Mohammad RezaMalekiIndustrial Engineering Group, Golpayegan College of Engineering, Isfahan University of Technology0000-0002-6005-3046HosseinEghbaliDepartment of Industrial Engineering, University of Eyvanekey, Semnan, Iran.Journal Article20250808With the increasing complexity of modern manufacturing systems and the growing dimensionality of quality data, the need for effective tools to track covariance matrix shifts in high-dimensional processes has become more urgent. This study proposes an enhanced adaptive thresholding LASSO-based control chart that utilizes a resubmission technique to enable faster detection of covariance matrix shifts in high-dimensional processes. The performance of the proposed chart is compared to the classical adaptive thresholding LASSO control chart through Monte Carlo simulations. To this end, seven out-of-control scenarios are defined for the covariance matrix, including two diagonal cases, two purely off-diagonal cases, and three mixed diagonal/off-diagonal cases. The detection power of both the proposed and existing control charts is evaluated using three key metrics: average run length (ARL), standard deviation of run length (SDRL), and median run length (MRL). Simulation results indicate that the proposed resubmission-based adaptive thresholding LASSO control chart outperforms its classical counterpart in detecting disturbances in the covariance matrix. This improvement is particularly evident when simultaneous changes occur in both diagonal and off-diagonal elements. Finally, the practical application of the proposed control chart is illustrated through a case study from the automotive component industry in Iran.https://jqepo.shahed.ac.ir/article_4980_385aa6bd95dd1552d4a1f284c8831e2e.pdfShahed UniversityJournal of Quality Engineering and Production Optimization10120250501A bi-objective model for civil court scheduling problem with pragmatic procedures and factors under uncertainty4770489510.22070/jqepo.2025.20339.1298ENZeinabLashgariDepartment of Industrial Engineering, QA.C., Islamic Azad University, Qazvin, IranBehnamVahdaniDepartment of Industrial Engineering, QA.C., Islamic Azad University, Qazvin, IranHabib RezaGholamiDepartment of Industrial Engineering, QA.C., Islamic Azad University, Qazvin, IranMaghsoudAmiriDepartment of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba’i University, Tehran, IranJournal Article20250414This study introduces a novel framework for planning and scheduling the handling of litigation cases in a judicial system. For this purpose, an unprecedented, bi-objective optimization model is proposed for scheduling family and civil cases in different courts, including primary, review, and supreme. In this regard, a broad range of pragmatic procedures and factors are considered, including different courts, the structuring of the process of judicial cases, the diversity of lawsuits and their miscellaneous cases, specialized chambers in courts, judges with different specializations, the frequency of reviewing cases in courts, filing objections, priority of cases, and permissible waiting time for handling cases and issuing their verdicts. More importantly, a sharing mechanism is embedded within the scheduling procedure to provide a balanced workload among the chambers of the primary court due to the high volume and variety of cases. Furthermore, since the uncertainty of the handling time of cases is an undeniable concern in planning such a problem, the set-induced robust optimization approach is applied to cope with this concern. Finally, a real case study of Iran's judicial system is examined to illustrate the applicability and validation of the proposed model. The obtained results revealed that the considered sharing mechanism can reduce 28% excess service capacity.https://jqepo.shahed.ac.ir/article_4895_792d513c59abe95389473002c9fb71f1.pdfShahed UniversityJournal of Quality Engineering and Production Optimization10120250501A Hybrid Model for Customer Churn Prediction: An Optimized Combination of Multilayer Perceptron and Atomic Orbital Search7190489810.22070/jqepo.2025.20710.1304ENLeilaZolqadrDepartment of Industrial Engineering, Yazd University, Yazd, IranMajidShakhsi-NiaeiDepartment of Industrial Engineering, Yazd University, Yazd, Iran.0000-0001-8034-9955AzamSafariHoly Shrine Fatemeh Masoumeh (SA), Qom, IranJournal Article20250718Customer churn is a formidable and persistent challenge for the telecommunications sector, as it significantly erodes profitability and impedes sustainable competitive advantage. Conventional machine learning methods often exhibit limitations in capturing non-linear intricacies and effectively managing voluminous, high-dimensional datasets. To address these issues, this investigation presents a novel hybrid predictive framework for enhanced customer churn prediction. Our proposed methodology integrates an optimized Multilayer Perceptron (MLP) with the Atomic Orbital Search (AOS) metaheuristic algorithm. Crucially, AOS is systematically deployed to fine-tune the critical connection weights and biases of the MLP architecture, mitigating prevalent issues like premature convergence and suboptimal parameter selection that are common in standalone neural networks. Empirical validation, conducted using the comprehensive Teldata dataset (7043 instances, 20 features) in MATLAB, unequivocally demonstrates the hybrid approach's superior efficacy over established conventional methodologies. Specifically, the hybrid MLP-AOS model achieved an impressive predictive accuracy of 97.8% on the test subset, a notable advancement compared to the 79% attained by Support Vector Machines (SVMs) and 75% by a conventional Multilayer Perceptron. These compelling findings underscore the proposed approach's capability in predicting customer churn with heightened precision, providing telecommunication management with an analytical instrument for identifying pivotal influencing factors and formulating effective retention strategies.https://jqepo.shahed.ac.ir/article_4898_397a05a13e04eb9fb74a2b7548e978f1.pdfShahed UniversityJournal of Quality Engineering and Production Optimization10120250501A hybrid decision analysis-based soft computing approach to solve ppp project selection problem with incomplete information91106501310.22070/jqepo.2026.21256.1316ENSeyed MasoudMortazaviDepartment of Civil Engineering, Qeshm Branch, Islamic Azad University, Qeshm, IranMohammad RezaAdlparvarAssociate professor, Technical and Engineering Faculty, University of Qom, Qom, IranMahtiamShahbaziAssistant professor, Department of Architecture, Science and research Branch, Islamic Azad University, Tehran, IranJournal Article20251117Nowadays, governments are cooperating with the private sector under the name of public-private partnership (3P) projects in order to reduce the risks of projects and to achieve the ultimate goal, which is the construction and operation of the project. In order to help decision makers and managers to make decisions in 3P projects, this article examines the financial challenges of the project in uncertain conditions with an intuitive fuzzy approach. The intuitionistic fuzzy multi-criteria weighting and ranking model (IF-MCWR) proposed method based on the intuitionistic fuzzy values to compute the weighting and ranking of the criteria. In process of the proposed IF-MCWR model, criteria's weights are generated to reduce the errors. Also, criteria with optimal weights are obtained based on IF Hamming distance measure by utilizing an extended maximizing deviation approach. Furthermore, judgments of decision makers (DMs) and experts are taken into account for calculating the criteria’ weights. Nevertheless, weights of experts are shown based on proposed new IF technique for order performance by similarity to ideal solution method. Then, based on hamming distance, a new IF index measure is proposed to obtain the relative closeness coefficient for ranking the candidates or alternatives. By doing this, project managers can make decisions with a more confident view. Finally, a case study is presented that examines the issue of urban development and presents the option of constructing a highway project as the most important and highest ranking among the projects that brings the most financial challenge.https://jqepo.shahed.ac.ir/article_5013_55c44a034998207ace9e9d95f0318755.pdf