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International journal of systems assurance engineering and management
Springer
International journal of systems assurance engineering and management

Springer

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0975-6809

International journal of systems assurance engineering and management/Journal International journal of systems assurance engineering and management
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    Parametric confidence intervals of generalized process capability index for finite mixture distributions

    Mahendra SahaSumit KumarPratibha PareekGaurav Doodwal...
    1679-1688页
    查看更多>>摘要：Abstract Process capability indices (PCIs) are commonly utilized for evaluating a process’s performance in meeting specified criteria. In this study, our first objective is to examine the performance of generalized process capability index (GPCI) Cpy\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathcal {C}_{py}$$\end{document} when the quality characteristic follows some finite mixture distributions, viz., xgamma and Akash distributions. Following this, our objectives are to calculate the GPCI Cpy\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathcal {C}_{py}$$\end{document} for quality attributes that conform to the xgamma and Akash distributions. This will be achieved by employing both the maximum likelihood estimation (MLE) and minimum variance unbiased estimation (MVUE) techniques. Subsequently, we will evaluate and compare the effectiveness of these estimation methods by examining their mean squared errors Monte-Carlo simulation study. Furthermore, we will utilize asymptotic confidence intervals (ACIs) to construct confidence intervals for the Cpy\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathcal {C}_{py}$$\end{document} index within these distributions. To assess the effectiveness of the ACIs, we plan to analyze their average width and coverage probabilities employing Monte Carlo simulation techniques. To showcase the efficacy of the suggested methods of estimation (MLE, MVUE) and ACIs of Cpy\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathcal {C}_{py}$$\end{document}, we have conducted data analysis based on four real data sets related to electronic and food industries.
      原文链接:
    • NETL
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    • Springer Nature

    Optimization of a deteriorated inventory model with bi-level credit periods and variable demand via tournament teaching learning based optimization algorithm

    Goutam MandalAmalesh Kumar MannaSubhash Chandra DasAsoke Kumar Bhunia...
    1689-1710页
    查看更多>>摘要：Abstract Tournamenting approach plays most vital and important role for selecting best individual in knockout tournament system. This idea is used for developing hybridization of algorithm based on teaching learning based optimization technique and named as tournament teaching learning based optimization algorithm (TTLBO). The main goal of this work is to apply hybrid TTLBO for solving maximization problems corresponding to the proposed non-instantaneous inventory model for single deteriorating item with trade-credit financing, partially backlogging and Weibull distributed deterioration. Demand depends on credit period and selling price of item. Now, our aim is to determine optimal order quantity, cycle length, selling price and maximum quantity of shortage by maximizing the retailer’s average profit. The validity of the developed model is tested with the help of an example. Also, the same example is solved by existing nine algorithms, viz. ABC, GQPSO, AQPSO, DE, RAO-1, RAO-2, RAO-3, HBO and TLBO algorithms to compare the performance and efficiency of the proposed TTLBO algorithm. Moreover, the analyses of sensitivity are studied to investigate the impact of different parameters involving in the model on the best found policy. Also, two nonparametric statistical tests, viz. Wilcoxon rank sum test and Friedman test are used to compare the statistical significant of proposed algorithm and existing nine algorithms. Finally, from numerical illustration and sensitivity analysis, a fruitful conclusion of this study is drawn.
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    • Springer Nature

    A novel operational risk assessment model based on evidence reasoning for multi-objective and dynamic operational scenarios

    Guicang PengJon T.ømmerås SelvikEirik Bjorheim AbrahamsenTore Markeset...
    1711-1727页
    查看更多>>摘要：Abstract Operational risk assessment (ORA) practices have traditionally focused on assessing risks within a single operational objective, often overlooking the complexities and interdependencies present in modern operational environments. This study explores the challenges of ORA within a multi-objective and dynamic context, where risks need to be balanced and integrated across varied operational objectives and decision gates. Furthermore, the study addresses the challenges posed by incomplete knowledge and conflicting assessments, which are prevalent in real-world ORA scenarios. Utilizing evidence reasoning (ER) as a multi-criteria decision analysis framework, the paper presents a novel ER-ORA mathematical model to aggregate individual risk beliefs across various operational objectives and decision gates dynamically. The approach resolves conflicts between individual risk assessments and quantifies overall uncertainties and conflicts of the assessment. The ER-ORA model is validated through a dataset reflecting the aforementioned ORA challenges. The paper conclude on the ER-ORA’s functionality and propose future research directions.
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    • Springer Nature

    Predicting software effort using BERT-based word embeddings

    Sanoussi MaigaSaurabh BilgaiyanSantwana Sagnika
    1728-1742页
    查看更多>>摘要：Abstract Accurate software effort estimation is essential for effective project planning and resource allocation, particularly in Agile software development where evolving requirements challenge traditional methods. This study explores the potential of pre-trained BERT (Bidirectional Encoder Representations from Transformers) models, a state-of-the-art NLP technique, to improve estimation accuracy. We compare the performance of the BERT base and BERT large models in diverse project scenarios. The results show that BERT Base consistently outperforms BERT Large in cross-repository and project-based contexts, owing to its computational efficiency and adaptability. A combined CNN and BERT Base model further enhances story point prediction for new projects, achieving superior accuracy and robustness. These findings highlight the practical advantages of leveraging BERT Base in Agile environments, offering valuable insights for researchers, software developers, and project managers. Future work will focus on external validation using commercial datasets, alternative deep learning architectures, and improved fine-tuning strategies to further advance effort estimation practices.
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    • NETL
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    • Springer Nature

    Comparative assessment of soft computing and SVM architectures for multi-class automobile engine fault classification

    Paul A. AdedejiJohnson A. OyewaleTunde I. OgedengbeObafemi O. Olatunji...
    1743-1756页
    查看更多>>摘要：Abstract The advancing complexity of automobile structure and continuous evolvement of automobile functionality has increased the difficulties of automobile fault diagnosis. This study investigates the effectiveness and efficiency of particle swarm optimization-based adaptive neurofuzzy inference system (PSO-ANFIS) models in offline automobile fault diagnosis. The results of this model were compared with error-correcting output code (ECOC) support vector machines with one-vs-one (ECOC-SVM-OVO) and one-vs-all (ECOC-SVM-OVA) based structures, probabilistic neural network (PNN), and standalone adaptive neurofuzzy inference system (ANFIS). Fifty cars were diagnosed, symptoms were classified into ten (10), and coded in binary forms, while likely faults were grouped into 10. The models were evaluated against relevant classification performance metrics and computational time. Similar performance metrics were obtained for the ECOC-SVM-OVO, ECOC-SVM-OVA, PNN and PSO-ANFIS models (accuracy = 1, error = 0, specificity = 1, false positive rate = 0, kappa statistic = 1). The standalone ANFIS model performed the least (accuracy = 0.75, error = 0.25, specificity = 0.97, false positive rate = 0.03, kappa statistic = 0.21), though at the least computational time (2.57 s). Although standalone ANFIS and PSO-ANFIS models could be used as classification models, their efficiencies and effectiveness are lower than those of the PNN and SVM architectures in this study.
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    • NETL
    • NSTL
    • Springer Nature

    Improving software reliability: a hybrid ARIMA-LSTM approach for fault prediction

    Umashankar SamalAjay Kumar
    1757-1769页
    查看更多>>摘要：Abstract Accurate prediction of software faults is essential for effective maintenance and improving overall reliability. This study presents a hybrid model that integrates autoregressive integrated moving average (ARIMA) with long short-term memory (LSTM) networks to enhance fault prediction accuracy. The ARIMA part effectively identifies linear patterns and trends in time series data, while the LSTM component captures complex nonlinear relationships and dependencies. Evaluation on three real-world datasets from open-source software projects shows that the hybrid approach outperforms both standalone ARIMA and LSTM models. The advantages of this model include enhanced decision-making capabilities, minimized downtime, and improved user satisfaction. This research provides a significant contribution to the field of software reliability forecasting, offering practitioners a robust tool for ensuring software dependability and enabling proactive strategies.
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    • NETL
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    • Springer Nature

    MVHFC-Net: AI-driven multi variate hydraulic fault detection and classification network in hydraulic systems

    Rama Bhadri Raju ChekuriTan Kuan TakPravin R. KshirsagarA. K. Sharma...
    1770-1796页
    查看更多>>摘要：Abstract Hydraulic systems find extensive applications in various industrial domains, including aerospace, automotive, and manufacturing. These systems rely on precisely controlling and managing fluid pressure, flow rates, temperatures, and other physical parameters to ensure optimal performance and reliability. However, the complexity and dynamic nature of hydraulic systems poses significant challenges in detecting and diagnosing faults accurately, leading to potential operational inefficiencies and safety concerns. This research addresses the problem of fault detection and classification in hydraulic systems using a novel methodology. This work proposes a Multi-Variate Hydraulic Fault Classification Network (MVHFC-Net) approach comprising a multi-label multi-label dataset. Initially, the dataset contains inputs such as motor power, pressure, temperature, volume flow, vibration, and virtual parameters like cooling efficiency and power, where faults are identified based on these input parameters. Subsequently, data preprocessing ensures data quality and prepares it for further analysis. In addition, an Extra Trees Classifier (ETC) is also employed to capture relevant information from the raw data effectively. Further, the Feature Importance Ranking (FIR) procedure is then utilized for optimal feature selection, enhancing the discriminative power of the classification model. Finally, the Hierarchical Extreme Learning Machine (HELM) classification model is employed to classify outcomes such as the condition of the cooler and valve, severity of internal pump leakage, pressure level of the hydraulic accumulator, and stability indicators. The proposed methodology offers a comprehensive framework for fault detection and classification in hydraulic systems, improving operational efficiency and reducing downtime. The proposed MVHFC-Net achieved an accuracy of 100%, precision of 100%, recall of 100%, and F1-score of 100% on all targets such as cooler condition, value condition, internal pump leakage, hydraulic accumulator, and stable flag. Experimental results demonstrate the approach’s effectiveness in accurately diagnosing system faults and providing actionable insights for maintenance and optimization.
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    P4.0B-FAHP: prioritizing industry 4.0 barriers using fuzzy AHP—case of an Indian construction company

    Ankur TayalSaurabh AgrawalRajan Yadav
    1797-1812页
    查看更多>>摘要：Abstract Industry 4.0 is revolutionizing the construction industry by integrating advanced technologies to enhance efficiency and productivity. This article offers insights into implementing Industry 4.0 in the construction industry of developing nations like India. However, its implementation faces significant challenges due to diverse barriers. Hence, this research explores and prioritizes these barriers for the effective implementation of Industry 4.0 within the construction industry. This study delved into 20 barriers across five primary criteria, utilizing an extensive literature review and expert input. A Fuzzy Analytical Hierarchy Process is applied to prioritize the barriers. The utilized Fuzzy framework significantly deals with unreliability and ambiguity. A case study of the Indian Construction Company illustrates the suggested model. A sensitivity study was conducted to verify the model’s resilience. The findings reveal that high initial investment, lack of funding, data management, and security are notable barriers to the implementation of Industry 4.0 within the construction industry of developing nations. The insights of this study serve as valuable resources for managers and policymakers in strategizing effective implementation of Industry 4.0.
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    • Springer Nature

    Impact of carbon emission and preservation investment on a manufacturing system for deteriorating item with price and greenness dependent demand via equilibrium optimizer algorithm

    Hachen AliFleming AkhtarSudipta GuinPritam Kumar Pakhira...
    1813-1829页
    查看更多>>摘要：Abstract The environmental sustainability and eco-friendliness of a product, sometimes known as its “greenness,” can have a wide range of effects on various levels, including the human, social, economic, and environmental. In terms of a company’s income, profitability, market positioning, and client relationships, the price of a product is crucial. Choosing the ideal price/value ratio is a crucial choice that necessitates careful evaluation of numerous internal and external considerations. Combining this impact, in this study, a production model is developed for deteriorating goods, considering preservation technology investment and product’s green level and selling price dependent demand. The manufacturing firm's rate of carbon emissions increases corresponding to production’s rate and time. Also, the cost per unit of produced items increases as the green level rises. The primary goal of this study is to find the optimal policy of the manufacturer to maximize the average profit. Due to the highly nonlinearity of the objective function, the associated optimization problem of our suggested model cannot be solved analytically. Five well-known metaheuristics [Equilibrium optimizer algorithm (EOA), Artificial Electric Field Algorithm (AEFA), Grey Wolf Optimizer Algorithm (GWOA), Whale Optimizer Algorithm (WOA) and Sparrow Search Algorithm (SSA)] are used to find the best-found solutions. From the results of different statistical test (ANOVA, Wilcoxon rank sum and Friedman tests) and convergence rate of different algorithms, it is evident that EOA perform better than other metaheuristics. A numerical example is taken into consideration in order to assess the validity of the suggested model. Finally, the effects of changes in different inventory parameters are studied on the best-found solutions by performing sensitivity analyses and draw a conclusion.
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    • Springer Nature

    A multi-criteria approach to blockchain in supply chain management assessment: entropy-CRITIC weight method and MCDM for enhanced decision support

    SalajMina Kumari
    1830-1843页
    查看更多>>摘要：Abstract Blockchain technology has gained significant responsiveness in the field of supply chain management owing to its potential to enhance limpidity, security, and efficiency in global network. Global Blockchain in Supply chain Management networks are rapidly adopted by app builders and technology integrators, enabling clients to perform a wide range of tasks in any sector because they offer flexible capabilities like intelligent contracts and decentralized software. But given the number of varied elements, it is challenging to choose a suitable and practical open source Blockchain in Supply chain Management technology due to the immaturity of the technology's environments. This essay outlines and ranks the best publicly accessible Blockchain in Supply chain Management systems using a multi-criteria decision-making (MCDM) approach. To do this, the researcher introduces ECWM, a novel load distribution method that combines volatility with CRITIC of sustainable supply chain. On a variety of datasets containing sixteen attributes (i.e., markers indicating what to look for), the researcher used ECWM. Several prominent digital currencies have adopted the Blockchain in Supply chain Management protocol. The researcher utilizes the WSM, TOPSIS, and a method known as VIKOR, to produce rankings of sustainable supply chain. Since these methods result in different ranks, Spearman’s correlation method has been employed between ranks. The rankings are carefully assessed in each sector.
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    • Springer Nature