Shahed UniversityJournal of Quality Engineering and Production Optimization9220241201Simultaneous monitoring of time between events and their multivariate magnitude118478110.22070/jqepo.2025.18419.1271ENMohammadrezaMirzaei NovinDepartment of Industrial Engineering, Faculty of Engineering, Shahed university, Tehran, Iran0000-0001-7372-3101AmirhosseinAmiriDepartment of Industrial Engineering , Faculty of Engineering, Shahed University, Tehran, Iran0000-0002-2385-8910Journal Article20231101Monitoring the time between events without accounting for their magnitudes is an impractical approach. This paper discusses the monitoring of the time between events (TBEs) and their multivariate magnitude (M). The paper explores control charts for the magnitude of events in a multivariate setting, focusing on situations where the magnitude of events has two dimensions. Two statistics are presented for the simultaneous monitoring of the time between events and the multivariate magnitude. The first statistic is a standardized EWMAZ, based on the Shewhart-EWMA approach. The second statistic is a set of three standardized single EWMAs, each used for monitoring TBEs and the two dimensions of magnitude (TSEWMA). The performance of these statistics is evaluated by considering different shifts in the mean of TBE and magnitude under three distinct shift parameter values. The results confirm their effectiveness across various conditions.https://jqepo.shahed.ac.ir/article_4781_bb7acf60be8ad068d2b920e75b8b70c9.pdfShahed UniversityJournal of Quality Engineering and Production Optimization9220241201Flow-shop scheduling problem with tool changes and tool wear to minimize energy consumption1946479110.22070/jqepo.2025.18387.1270ENAtiyehMohammad-RafieiDepartment of Industrial Engineering, Faculty of Engineering, Semnan University, Semnan, IranEhsanMardanDepartment of Industrial Engineering, Faculty of Engineering, Semnan University, Semnan, IranRezaKamran RadDepartment of industrial engineering, Faculty of engineering, Semnan University, Semnan, IranJournal Article20231029– Nowadays, most production units seek to minimize the amount of energy consumption due to the resulting economic pressures and the shortage of energy carriers such as electricity or fossil fuels. On the other hand, minimizing the total time to complete jobs aligns with reducing energy consumption. Therefore, efficient scheduling is one of the important concerns in industrial units. This research aims to minimize joint energy consumption and total job completion time in a flow-shop environment, considering speed level and tool wear level constraints. The increased speed level reduces the time to complete the job, and on the other hand, increases energy consumption. Moreover, the increase in the machine speed causes an increase in the level of wear and eventually a tool change. Since the flow-shop scheduling problem is NP-hard, it cannot be solved in a short time on a large scale. To cope with this issue, we solve this multi-objective optimization problem with two well-known metaheuristic methods, Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO). The experimental results reveal that MOPSO outperforms NSGA-II in terms of MOCV and MID metrics. Conversely, NSGA-II outperforms MOPSO in terms of CPU time.
https://jqepo.shahed.ac.ir/article_4791_9b83747e206ab4236506f56b342639e8.pdfShahed UniversityJournal of Quality Engineering and Production Optimization9220241201A SBM-NDEA Model for Evaluating the Efficiency of Cross-Docking Systems under Uncertainty: A Fuzzy Reasoning-Neutrosophic Programming Approach4784469410.22070/jqepo.2025.19829.1289ENAndisheh SadatAllaeiDepartment 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, IranAlirezaAlinezhadDepartment of Industrial Engineering, QA.C., Islamic Azad University, Qazvin, IranJournal Article20241125– While the evaluation of warehouses' efficiency through various methods has been increasingly prominent in recent times, this study stands out as the pioneering attempt to evaluate the efficiency of cross-docking systems. To this end, this study delves into a detailed analysis of a cross-docking system's structure, taking into account a wide array of factors that impact its operational performance, such as inbound and outbound doors, different modes of transportation, inspection processes, kitting activities, storage procedures, retrieval tasks, and staging operations. A comprehensive range of key performance indicators (KPIs) is recommended for every aspect related to digitization, automation, sustainability, resiliency, and lean principles. A new slack-based measure network data envelopment analysis (SBM-NDEA) model is developed to assess the efficiency of cross-docking systems with regard to undesirable factors. What is more, a novel hybrid uncertainty method is presented, which incorporates fuzzy reasoning techniques and Neutrosophic fuzzy programming to address uncertainties in the recommended KPIs and quantify qualitative assessments. Ultimately, a case study is analyzed to highlight the efficacy and validity of the developed model and uncertainty methodology. The findings reveal that the simultaneous and effective application of pre-distribution and post-distribution policies can boost the efficiency of the analyzed system by 33%. Furthermore, modifying the layout to remove unnecessary transportation activities can result in a 21% improvement in efficiency.https://jqepo.shahed.ac.ir/article_4694_cb386880c23e42eba33c2dd1b8cbffda.pdfShahed UniversityJournal of Quality Engineering and Production Optimization9220241201Sustainable Distribution and Inventory Planning in Supply Chains under VMI Strategy for B2B and B2C Models Using IoT85106478210.22070/jqepo.2025.19895.1291ENNajmehBahrampourDepartment of Industrial Engineering, Faculty of Engineering, Alzahra University, Tehran, IranMehdiSeifbarghyDepartment of Industrial Engineering, Faculty of Engineering, Alzahra University, Tehran, IranSeyed Hamid RezaPasandidehDepartment of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, IranJournal Article20241215–This study explores optimizing sustainable supply chains by integrating vendor-managed inventory (VMI) and internet of things (IoT). The research focuses on business-to-business (B2B) and business-to-customer (B2C) models. While VMI is widely studied in B2B, its B2C application remains limited. This study examines the tire manufacturing sector, addressing significant environmental and safety concerns. A multi-level optimization framework is introduced to minimize costs, reduce carbon emissions and waste, and enhance customer safety. Customer safety is introduced as a novel social factor. The density-based spatial clustering of applications with noise (DBSCAN) algorithm clusters retailers, improving efficiency and reducing computational time. The framework serves very important customers under the VMI strategy, while normal customers are excluded. Empirical data from a tire manufacturer validates the framework using the Gurobi optimization package. The results demonstrate that applying VMI to all customers significantly increases service levels and the objective function value. Conversely, restricting VMI to B2B customers alone leads to a decline in both service levels and the objective function. Results confirm the scalability and efficiency of the model, with sensitivity analysis showing strong performance under varying parameters. This paper explores VMI in B2B and B2C models, offering insights into sustainable supply chain management.
https://jqepo.shahed.ac.ir/article_4782_f052305fbdea5f889b1896c29c31a765.pdfShahed UniversityJournal of Quality Engineering and Production Optimization9220241201Optimizing Pricing and Inventory for Perishable Goods in Closed-Loop Supply Chains with SSMD Strategy107132477810.22070/jqepo.2025.19860.1290ENHeibatolahSadeghiDepartment of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, IranSaharMansuriDepartment of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, IranMehdiGolbaghiDepartment of Technology and Engineering, Payame Noor University, Tehran, IranSaminArbabiDepartment of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, IranJournal Article20241203This paper proposes a comprehensive bi-level pricing-inventory model for closed-loop supply chains managing perishable goods with price-sensitive demand. The study addresses a critical gap in the existing literature by integrating perishability constraints and pricing dynamics into a unified optimization framework. The proposed framework incorporates the Single Setup Multiple Delivery (SSMD) strategy, which enables manufacturers to optimize shipment frequencies and quantities, thereby minimizing inventory holding costs and mitigating perishability losses. Utilizing a hybrid mixed-integer optimization method that combines derivative-based techniques with integer search, the model identifies optimal pricing and inventory strategies to maximize supply chain profitability. A comprehensive sensitivity analysis is conducted to examine the interaction between manufacturer-retailer pricing strategies and evaluate the impact of key parameters on system performance. The results demonstrate the model’s effectiveness in addressing critical challenges, including the trade-off between shipment costs and perishability, while underscoring its potential to enhance profitability in perishable goods supply chains. Finally, future research directions are outlined, focusing on the integration of real-time data and machine learning for dynamic decision-making.https://jqepo.shahed.ac.ir/article_4778_7b357e1b1d31c7541e67af0d612bab3f.pdfShahed UniversityJournal of Quality Engineering and Production Optimization9220241201A Hybrid Group-MCDM Framework for Supplier Selection Problem in Organ Transplantation Networks under an Interval-Valued Fuzzy Environment133156487410.22070/jqepo.2025.20674.1301ENR.AskarianzadehDepartment of Industrial Engineering, Shahed University, Tehran, IranS. M.MousaviDepartment of Industrial Engineering, Shahed University, Tehran, IranM. J.BarzegariDepartment of Industrial Engineering, Shahed University, Tehran, IranJournal Article20250711Selecting appropriate suppliers in organ transplant affiliation networks is of critical importance due to its direct impact on service quality and increased life expectancy. This process is recognized as a complex group-multiple criteria decision-making (G-MCDM) problem, involving the evaluation of multiple supplier alternatives based on key criteria for organ transplantation. In this study, a new integrated model is proposed by combining the Borda and CoCoSo methods using Interval-Valued Fuzzy Sets (IVFSs) within a group decision-making environment. Leveraging the enhanced capabilities of fuzzy theory, the proposed method effectively addresses the inherent uncertainties present in real-world applications. The weights of the criteria are determined using an interval-valued fuzzy Shannon entropy (IVF-Shannon entropy) method, incorporating expert judgments. Subsequently, the hybrid Borda-CoCoSo approach is employed to rank supplier alternatives for organ transplant equipment within affiliation networks. An application example is presented to assess the performance of the proposed model, and both comparative and sensitivity analyses are conducted to investigate the influence of key parameters on the results. In addition, a comparative evaluation is performed with three existing methods from the literature. The results highlight the accuracy and efficiency of the proposed model in supplier selection and in improving decision-making within the organ transplant supply chain.https://jqepo.shahed.ac.ir/article_4874_fd521da9611ae0a9532bfe4a2fc57b95.pdf