Showing 711 results for Type of Study: Research
Vikas Ghute, Onkar Ghadge,
Volume 0, Issue 0 (10-2025)
Abstract
Profile monitoring is generally utilized in manufacturing industries to assess the quality of products and detect deviations from normal conditions. This technique has proven to be effective in improving efficiency, productivity and reducing expenses in manufacturing industries. This paper contains a new Modified Group Runs (MGR) based control chart for the monitoring of simple linear profiles. The suggested control chart relies on employing three distinctive MGR control charts in parallel to monitor the parameters of the simple linear regression profile and is named as the MGR Shewhart-3 control chart. Average run length (ARL) criterion is used to assess the effectiveness of MGR Shewhart-3 control chart under shifts of diverse magnitudes in the parameters of the simple linear regression profile model. The simulation findings demonstrated that the performance of the proposed control chart under error standard deviation shifts is uniformly better than mentioned control charts in detecting small to large shifts. For monitoring shifts in the intercept and slope parameter, the proposed MGR Shewhart-3 control chart performs better than the remaining control charts in detecting medium to large shifts. Finally, the implementation of the proposed control chart is illustrated through an example.
El Ghalya Laaroussi, Badr Dakkak, El Hassan Irhirane, Ahmed Bounit,
Volume 0, Issue 0 (10-2025)
Abstract
This article introduces a novel method for solving problems in Systematic Preventive Maintenance (SPM) by combining the Lean approach with the Algorithm for Inventive Problem Solving (ARIZ). The proposed model first uses Lean to define SPM objectives and constraints, then applies ARIZ to resolve identified contradictions. To ensure an optimal compromise between reliability and availability, a tool based on design of experiment was designed to select the most relevant actors at each stage of the preventive maintenance intervention. The applicability of this method is validated through a case study focused on a crucial piece of mining equipment: the bucket-wheel excavator. This study validates a robust model that explains 86.47% of the variability in reliability and 74.33% of the variability in availability. The analysis revealed that Maintenance Level 1 most significantly affects availability, while Maintenance Level 3 has the greatest impact on reliability. Model optimization predicted maximum values of 5.3125 for reliability and 3.3750 for availability. A sensitivity analysis further confirmed the robustness of this optimal solution, demonstrating that our approach provides concrete solutions beyond the limitations of traditional optimization methods.
Widowati Widowati, Sutrisno Sutrisno, Robertus Heri Soelistyo Utomo,
Volume 0, Issue 0 (10-2025)
Abstract
In the manufacturing and retail sectors, the challenges of supplier selection revolve around determining the most efficient allocation of raw materials to various suppliers to minimize procurement costs. Concurrently, production planning issues focus on optimizing the quantity of products to be manufactured. Simultaneously, warehouses used to store raw materials and products must also be optimally managed to reduce holding costs. To achieve maximum revenue, decision-makers must make optimal arrangements regarding these three problems. This study introduces a novel mathematical model within the realm of dynamic expected-based probabilistic piecewise programming as a decision support tool for those three problems, which are solved in an integrated manner. It aids in identifying optimal solutions for the combined issues of supplier selection, inventory management, and production planning, which encompass discount and uncertainty factors. The primary objective is to enhance supply chain performance, specifically by maximizing profits derived from production activities. The model accommodates supply chain scenarios involving multiple raw materials, suppliers, products, and buyers. Furthermore, the problem is modeled with numerous observation time instants. Numerical experiments were conducted to assess the proposed model and to demonstrate how optimal decisions can be made. Compared to the deterministic model, the proposed model increased the profit by 6%. The results indicate the model's effectiveness in resolving these challenges and providing optimal solutions. As a result, decision-makers and managers in various industries can consider implementing this proposed model.
Tính Nghiêm Văn,
Volume 0, Issue 0 (10-2025)
Abstract
Fuzzy Time Series (FTS), based on fuzzy set theory, models’ data using linguistic labels to handle incomplete data, and has been widely applied in forecasting student enrollment, traffic safety, and energy prices. However, the subjective determination of time intervals and fuzziness parameters reduces prediction accuracy, especially for highly volatile datasets. This study proposes a novel FTS model that employs Particle Swarm Optimization (PSO) to simultaneously optimize the fuzziness parameters of Hedge Algebra (HA) and interval lengths of the universe of discourse, obviating manual tuning. A new defuzzification formula based on fuzzy set indices further enhances forecasting accuracy. Evaluations on University of Alabama enrollments, Belgian traffic accident fatalities, and Vietnamese gasoline prices demonstrate superior performance, with RMSE reductions up to 20-30% over existing methods [e.g., 70.9 for enrollments with 14 intervals], excelling in incomplete data scenarios. This automated and adaptive model improves forecasting performance and supports decision-making not only in education and energy management but also effectively across various domains.
Wahyu Kurniawan, Achmad Pratama Rifai , Nur Aini Masruroh,
Volume 0, Issue 0 (10-2025)
Abstract
Adaptive Simulated Annealing (ASA) and Adaptive Large Neighborhood Search (ALNS) are two metaheuristic algorithms widely applied to solve discrete optimization problems. This study employs both algorithms to address the Container Loading Problem (CLP), a critical challenge in the consolidation-based freight forwarding industry, where maximizing container utilization directly influences revenue and operational efficiency. The case company, a national freight forwarding enterprise operating consolidation services in Indonesia, currently achieves an average container utilization rate of 56.8%, indicating a substantial opportunity for improvement. By formulating the CLP as a discrete combinatorial optimization model, this research aims to enhance both container load utilization and revenue through algorithmic optimization. The novelty of this work lies in its comparative implementation of ASA and ALNS under adaptive parameter calibration, applied to a real-world freight forwarding context, which remains rarely explored in previous CLP studies. Experimental results show that ALNS consistently outperforms ASA in terms of both objective value and robustness across scenarios. Specifically, the ALNS method achieves 85.4% container utilization and an average revenue increase of 8.6% per container, demonstrating superior efficiency in freight consolidation optimization. Additionally, experiments conducted under equal iteration conditions further support that ALNS maintains higher stability and better solution consistency compared to ASA, particularly in terms of fitness and utilization efficiency across different iteration scenarios. Despite ALNS requiring longer computation time, it remains well within the acceptable time frame for freight forwarding operations, where up to 24 hours is available for shipment planning. These findings provide practical implications for logistics firms seeking to integrate metaheuristic-based decision support systems to improve capacity utilization, responsiveness, and profitability.
Kaviyarasu Velappan, Nagarajan Arumugam,
Volume 0, Issue 0 (10-2025)
Abstract
The quality of pharmaceutical devices determines an entity's effectiveness in fulfilling its intended functions for patients. Statistical quality control entails the utilization of statistical methodologies to oversee and enhance the quality of processes and products. In the realm of the pharmaceutical industry, Reliability Acceptance Sampling Plans (RASP) entail inspecting a sample of items to ascertain whether the entire batch adheres to prescribed quality benchmarks, thereby curbing inspection expenses while upholding quality assurance of medical devices. The special Type of Double Sampling (STDS) plan constitutes a subset of reliability acceptance sampling plans that employ dual sampling stages to ascertain the batch acceptance or rejection of products or materials. This article develops a new methodology by employing the Special Type of Double Sampling plan under Exponential–Poisson (EP) distribution for the mean life when the product life test is truncated at a specified time.t0 ’. The minimum sample size ‘n’ required to guarantee the attainment of the designated mean life ‘β0 ’ within a predetermined consumer's risk. The Operational Characteristic (OC) function values are acquired based on varying tiers of quality and the minimum mean ratio of the real mean lifetime.β ’ to the designated mean lifetime ‘β0 ’ under the designated producer's risk are developed. Moreover, to enhance the comprehension of the proposed methodology, a numerical illustration accompanied by a real-world scenario that is applicable to the pharmaceutical industry is presented.
Aizi Mouna, Bouyaya Linda, Bouguern Siham,
Volume 0, Issue 0 (10-2025)
Abstract
This study applies a Genetic Algorithm (GA) to optimize the Vehicle Routing Problem with Time Windows (VRPTW) for NAFTAL, Algeria’s national fuel distribution company. The model minimizes both fleet size and total travel distance while achieving high compliance (99.3%) with customer time constraints. Using operational data from the Constantine regional network one depot serving 148 service stations (149 nodes in total ), the GA achieved optimal solutions deploying 94 vehicles covering 15,415.63 km. Results demonstrated exceptional convergence stability (σ = 0.00 across 40 runs) and high computational efficiency (under 60 seconds per optimization run). Sensitivity analyses confirmed the robustness of the calibrated configuration, highlighting its reliability and scalability for real-world logistics. The proposed framework provides NAFTAL with a cost-effective, consistent, and practical decision-support tool for optimizing fuel-delivery operations. Future research will focus on integrating machine learning for demand prediction, extending the model to multi-product and heterogeneous-fleet routing, and enabling adaptive real-time optimization to support smart and sustainable logistics.
Mohammad Pourali, Abdolsalam Ghaderi,
Volume 0, Issue 0 (10-2025)
Abstract
The rapid growth of waste electrical and electronic equipment (WEEE) presents critical environmental and social challenges, emphasizing the urgent need for effective reuse and recycling solutions. This study develops a comprehensive reverse supply chain network design model for WEEE, with the dual objectives of minimizing total costs and environmental pollution. The proposed multi-echelon network includes retailers, collection centers, recycling facilities, and disposal sites, where e-waste undergoes collection, inspection, sorting, and routing based on quality assessments. To align with legal and regulatory frameworks, the model incorporates government regulations, ensuring compliance with mandated collection targets and supporting Extended Producer Responsibility (EPR) initiatives. An Internet of Things (IoT)-based framework is integrated into the model to enhance operational efficiency through real-time waste tracking, improved transparency, and data-driven decision-making. The study applies the Augmented Epsilon Constraint Method to solve the multi-objective optimization problem, highlighting trade-offs between cost reduction and emission minimization. Results indicate that IoT integration not only enhances environmental performance but also significantly improves cost efficiency, making it a valuable tool for sustainable waste management. The findings contribute to advancing circular economy principles by promoting resource conservation, reducing waste, and fostering a more sustainable and efficient reverse supply chain network for WEEE.
Mohammad Reza Mehregan, Arman Rezasoltani, Amir Mohammad Khani, Ali Hosseinzade Kashan,
Volume 0, Issue 0 (10-2025)
Abstract
In the modern industrial view, it is strategically imperative to predict failure of industrial machinery with a view to reducing the occurrence of unexpected failures and enhancing operational efficiency. This study seeks to introduce a new hybrid machine learning model for predictive maintenance, combining the use of deep learning and advanced ensemble machine learning models. The model presented follows a stacking ensemble structure, where XGBoost, CatBoost, Gradient Boosting, and a deep neural network are base learners. Thereafter, the LightGBM, acting as a meta-model, is used to collect its predictions. Further, in this study, the Optuna hyperparameter optimization framework is employed to optimize the hyperparameters automatically, and the NearMiss algorithm solves the class imbalance problem by enhancing the representation of the minority class and removing the bias in favor of the majority class. As can be seen in the experimental results, the combined model outperforms the single models, achieving an outstanding accuracy of 96.17%. This is followed by a precision of 97.86%, a recall of 94.4%, and an F1 score of 96.1%. It is worth noting that though the XGBoost models' independent results were high (with an F1 score of 89/41) and better than the 16 individual models studied in this paper and regarded as a comparison to the hybrid model, the hybrid model significantly defeated the independent models by nearly 7 percentage points, hence the strong suit of the smart ensemble framework in model combination. The model has been tried using industrial data with 10000 records of a milling machine system, which is representative of most industrial machinery. The model aids in making decisions in preventive maintenance processes in a more informed and timely way by detecting failures accurately before they happen, avoiding unwanted situations of unplanned downtime and operation costs. One can arrive at the conclusion based on these results that the mentioned hybrid model can offer a solid and workable way of predicting failures in the industrial context and can also be integrated into the actual maintenance processes without any issues.
Zahwa Fitria Gumilang, Rakhmat Ceha, Vera Septiawati,
Volume 0, Issue 0 (10-2025)
Abstract
The imperative to align supply chains with sustainability objectives has intensified interest in the technological dimensions of sustainable supply chain management (SSCM). To provide a systematic overview of how this research area has evolved, this study conducts a bibliometric analysis of publications from 2005 to 2025, drawing on data from Scopus and Web of Science (WoS). The results demonstrate that technologies such as blockchain, artificial intelligence, big data analytics, and the Internet of Things have received the most attention, shaping the trajectory of SSCM research. Publication and citation trends show exponential growth after 2016, reflecting both global policy shifts and accelerated digital adoption during the COVID-19 pandemic. The United Kingdom and China lead in research output, while the United States demonstrates high citation impact. Several highly cited publications serve as intellectual anchors, shaping theoretical and methodological development. Keyword analysis and bibliographic coupling further reveal five dominant knowledge clusters: digital transformation for SSCM, life cycle thinking, policy in SSCM, blockchain for food sustainability, and sustainable product design. Overall, the study highlights technological trajectories, underexplored research areas, and regional disparities, providing theoretical and practical guidance for future interdisciplinary research, policy design, and technology adoption aimed at advancing sustainable and resilient supply chains.
Wael Elshanhaby, Sabri Turki, Esraa Abdel Azzem, Bahaa Eldin Saad, Amr Noureldin, Hamdy Nour, Shahenda Habib,
Volume 0, Issue 0 (10-2025)
Abstract
This research examines good work as a strategic asset for reducing employee turnover intent in Saudi Arabia, with work-life balance as a mediator. Using the Conservation of Resources (COR) Theory, the study positions decent work as a multidimensional resource bundle that employees draw upon to preserve psychological and contextual resources. Data were collected from 210 full-time employees across key Saudi sectors (education, healthcare, and industry), reflecting the sectoral dynamics emphasized by Vision 2030. PLS-SEM was employed to test both a first-order and complementary second-order specification of decent work, thereby providing a more rigorous and theoretically coherent examination of its structure than prior studies. The findings demonstrate that decent work significantly reduces turnover intention both directly and indirectly through enhanced work–life balance, confirming the resource gain processes described in COR Theory. The study contributes novel empirical evidence from a transforming Middle Eastern labor market, clarifies the mechanism through which decent work promotes personal resource stability, and offers a sectoral interpretation of employee retention drivers in Saudi Arabia. Practically, the results underscore the importance of sustaining comprehensive decent-work practices, especially fair workloads, safety, and work–life policies, to retain skilled employees and support the human capital objectives of Vision 2030.
F.d. Javanroodi , K. M. Nikbin ,
Volume 17, Issue 3 (9-2006)
Abstract
There is an increasing need to assess the service life of components containing defect which operate at high temperature. This paper describes the current fracture mechanics concepts that are employed to predict cracking of engineering materials at high temperatures under static and cyclic loading. The relationship between these concepts and those of high temperature life assessment methods is also discussed. A model for predicting creep crack growth initiation and growth in terms of C* and the creep uniaxial ductility is presented and it is shown that this model gives good agreement with the experimental results. The effects of cyclic loading on crack growth behaviour are considered and fractography evidence is shown to back a simple cumulative damage concept when dealing with creep/fatigue interaction. Finally a discussion is presented which highlights the important aspect of life assessment methodology for high temperature plant.
H. Yarjiabadi, M. H. Shojaeefard, A.r. Noorpoor, H.yarjiabadi, , M. Habibian , A.r. Noorpoor ,
Volume 17, Issue 3 (9-2006)
Abstract
The hydrocyclone has a very important roll in industrial separation. The consideration of its behavior is very important for design. In this investigation, behavior of water flow and particles trajectory inside a hydrocyclone has been considered by means of numerical and experimental methods, and results have been compared together. To have a numerical simulation, a CFD software was used, and for modeling flow the RNG k – model applied. Finally, the effect of particle size on hydrocyclone performance has been studied. It was found that the grade efficiency and number of particle that exit from underflow of the hydrocyclone is increased when bigger particles is used.
A series of experiments has been carried out in a laboratory with a hydrocyclone. Comparison shows that, there is a good agreement between the CFD models and experimental result.
M. Haghpanahi, H. Pirali ,
Volume 17, Issue 3 (9-2006)
Abstract
Finite element analysis of a tubular T-joint subjected to various loading conditions including pure axial loading, pure in-plane bending (IPB) and different ratios of axial loading to in-plane bending loading has been carried out. This effort has been established to estimate magnitudes of the peak hot spot stresses (HSS) at the brace/chord intersection and to find the corresponding locations as well, since, in reality, offshore tubular structures are subjected to combined loading, and hence fatigue life of these structures is affected by combined loading. Therefore in this paper, at the first step, stress concentration factors (SCFs) for pure axial loading and in-plane bending loading are calculated using different parametric equations and finite element method (FEM). At the next step, the peak HSS distributions around the brace/chord intersection are presented and verified by the results obtained from the API RP2A Code procedure. Also the locations of the peak hot spot stresses which are the critical points in fatigue life assessment have been predicted.
M. M. Shokrieh, R. Rafiee ,
Volume 17, Issue 3 (9-2006)
Abstract
The main goal of this research is to extract the full mechanical properties of stitch biax and triax composite materials which are necessary for finite element analysis, based on limited available experimental data and without performing full static characterization tests. Utilized experimental data are limited to elastic modulus of two 0o and 45o directions. Using presented technique and aforementioned data, mechanical properties of unidirectional fabrics of biax and triax are obtained and consequently mechanical properties of biax and triax composites are calculated. Evaluation of the results proved proper performance of the technique in this research.
M.r. Modarres Razavi, S.h. Seyedein, P.b. Shahabi , S.h Seyedein,
Volume 17, Issue 3 (9-2006)
Abstract
In this paper hemodynamic wall parameters which play an important role to diagnose arterial disease were studied and compared for three different rheology models (Newtonian, Power law and Quemada). Also because of the pulsatile behavior of blood flow the results were obtained for three Womersley numbers which represent the frequencies of the applied pulses. Results show that Quemada model always located between Newtonian and Power law models however its behavior is closer to Power law model. Concerning this behavior and better agreement between Quemada and experimental blood viscosity, it can be expected that Quemada results are more realistic and accurate.
M. Nikian, , M. Naghashzadegan, S. K. Arya ,
Volume 17, Issue 3 (9-2006)
Abstract
The cylinder working fluid mean temperature, rate of heat fluxes to combustion chamber and temperature distribution on combustion chamber surface will be calculated in this research. By simulating thermodynamic cycle of engine, temperature distribution of combustion chamber will be calculated by the Crank-Nicolson method. An implicit finite difference method was used in this code. Special treatments for piston movement and a grid transformation for describing the realistic piston bowl shape were designed and utilized. The results were compared with a finite element method and were verified to be accurate for simplified test problems. In addition, the method was applied to realistic problems of heat transfer in an Isuzu Diesel engine, and gave good agreement with available experimental.
M. H. Shojaeefard, F. A. Boyaghchi , M. B. Ehghaghi ,
Volume 17, Issue 4 (11-2006)
Abstract
In this paper the centrifugal pump performances are tested when handling water and viscous oils as Newtonian fluids. Also, this paper shows a numerical simulation of the three-dimensional fluid flow inside a centrifugal pump. For these numerical simulations the SIMPLEC algorithm is used for solving governing equations of incompressible viscous/turbulent flows through the pump. The k-ε turbulence model is adopted to describe the turbulent flow process. These simulations have been made with a steady calculation using the multiple reference frames (MRF) technique to take into account the impeller- volute interaction. Numerical results are compared with the experimental characteristic curve for each viscous fluid. The data obtained allow the analysis of the main phenomena existent in this pump, such as: head, efficiency and power changes for different operating conditions. Also, the correction factors for oils are obtained from the experiment for part loading (PL), best efficiency point (BEP) and over loading (OL). These results are compared with proposed factors by American Hydraulic Institute (HIS) and Soviet :::union::: (USSR). The comparisons between the numerical and experimental results show good agreement.
Gh. Yari , M. D Jafari ,
Volume 17, Issue 4 (11-2006)
Abstract
Main result of this paper is to derive the exact analytical expressions of information and covariance matrix for multivariate Pareto, Burr and related distributions. These distributions arise as tractable parametric models in reliability, actuarial science, economics, finance and telecommunications. We showed that all the calculations can be obtained from one main moment multidimensional integral whose expression is obtained through some particular change of variables. Indeed, we consider that this calculus technique for that improper integral has its own importance.
A. Shidfar, Ali Zakeri,
Volume 17, Issue 4 (11-2006)
Abstract
This paper considers a linear one dimensional inverse heat conduction problem with non constant thermal diffusivity and two unknown terms in a heated bar with unit length. By using the WKB method, the heat flux at the end of boundary and initial temperature will be approximated, numerically. By choosing a suitable parameter in WKB method the ill-posedness of solution will be improved. Finally, a numerical example will be presented.
