VGTU Journals (Vilnius Gediminas Technical University - Vilnius Tech)
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Optimizing aircraft maintenance tasks allocation using mixed integer linear programming
No full textThis study addresses the optimization of aircraft maintenance task allocation for small fleets using Mixed-Integer Linear Programming (MILP). The research integrates manpower efficiency, regulatory compliance, and workload balancing to minimize downtime and enhance resource utilization. A mathematical model is formulated to account for task durations, skill levels, and sequential/parallel task constraints, validated via MATLAB implementation. Results from a simulated 50-hour Cessna 172 maintenance check demonstrate a 25% reduction in completion time compared to average manual scheduling. The model balances workloads by assigning tasks based on manpower expertise, highlighting the critical role of human factors in reducing errors and improving efficiency. Practical implications include a cost-effective alternative to commercial software for small operators, enabling optimized planning without high-cost tools. This work bridges a gap in maintenance literature by explicitly incorporating manpower efficiency into MILP frameworks, offering actionable insights for regulators and operators to avoid maintenance congestions and enhance operational resilience
Exploring service improvement through importance-performance analysis considering the reliability of multiple online platforms
No full textService improvement has emerged as a pivotal task for hoteliers to ensure competitive advantage. This study proposes a service improvement method based on online reviews from multiple platforms considering the reliability of online platforms and different evaluation modes, where the reliability of an online platform is defined based on the number of online reviews on that platform and the degree of review helpfulness. In our method, Latent Dirichlet Allocation model is utilized to extract keywords, and lexicon-based sentiment analysis methods are employed to analyze the sentiment of online reviews on each platform considering different evaluation modes. The importance of attributes on each platform is measured by the TextRank method. A multi-platform-oriented importance-performance analysis model is constructed based on the integrated performance and the importance of attributes, so as to classify attributes and formulate service improvement strategies. A case study about hotel service improvement is implemented to illustrate the effectiveness of the method. Results show that the attributes classification results considering the reliability of multiple platforms is more reasonable compared to the results based on a single platform, providing more effective service improvement strategy and clearer view of attribute status on various platforms for hoteliers
Climate change as a socio-economic challenge for the tourism sector: an econometric analysis of countries with the highest economic losses
No full textThe issue of climate change, and in particular the economic losses caused by natural disasters, is a significant problem for the tourism sector and its development. The research conducted aims to address the following research question: Does climate change, quantified by the economic losses attributed to extreme weather events, influence the development of the tourism sector, as measured by tourism’s contribution to GDP? Furthermore, the study explores the role of insurance as a financial instrument for mitigating the effects of climate change on tourism. An important part of the research is an in-depth regional analysis of climate change losses. It was carried out both on a continental level and for the countries studied. Statistical and econometric techniques were employed to investigate the research question. The analysis focused on a selection of countries identified by the Swiss Re Institute as having experienced the highest economic losses due to climate change. Thirteen countries were surveyed, ensuring representation for each continent. The research period spans the years 2014–2023, and the data analysis was conducted using Statistica 13 and Gretl software. The findings indicate that economic losses resulting from natural disasters show an increasing trend, both in absolute terms and as a percentage of national GDP. These losses represent a significant constraint on economic growth, particularly limiting the development of tourism. Furthermore, the survey shows that the negative effects of climate change are more challenging for less economically developed countries. These countries are in addition to being more exposed to climate-related damages because of their natural conditions, but also struggle with underdeveloped insurance sectors. Reduced access to insurance compounds the disruptive impact of climate change on tourism. An analysis of the relationship between the weather damage load on a country’s economy and the insurance gap identifies three countries – the USA and China – as being especially at risk. Investigating the impact of the effects of natural disasters on the development of the tourism sector in countries with the greatest economic losses from climate change fills a research gap in this area and contributes to the development of knowledge on the effects of climate change on the competitiveness and sustainability of tourism. The research should be considered original in its subject coverage. No studies of this scope have been found in the literature. Investigating the impact of insurance on climate change mitigation for the tourism sector should also be considered innovative. The results of the research can be used to shape tourism policy in the countries studied, as well as globally
Evaluating the effectiveness of the Czech national bank’s monetary policy: a longitudinal study since 1997
No full textPurpose – This study examines the effectiveness of the Czech National Bank’s (CNB hereafter) monetary policy with a focus on its adaptive responses to macroeconomic indicators such as inflation, unemployment, economic growth, interbank rate, imports, exports, and exchange rates. The two-week repo rate served as the primary instrument for the analysis period from January 1997 to July 2023.
Research methodology – The research employs a Vector Autoregression (VAR) model, complemented by Impulse Response Functions (IRF), Forecast Error Variance Decomposition (FEVD), Granger Causality and Johansen Cointegration Tests to analyse quarterly data on the dynamic interactions between the CNB’s two-week repo rate and key economic indicators.
Findings – The research results show that the CNB monetary policies have mainly been effective in managing inflation targeting and sustaining economic stability. The considerable influence achieved through proactive policy adjustments to unemployment and interbank rates.
Research limitations – External macroeconomic shocks not accounted for in the VAR model might influence the study results. Furthermore, the findings are specific exclusively to the Czech economy and may not be directly applicable to other countries with distinct economic structures or monetary policy frameworks.
Practical implications – The research highlights the importance of predicting repo rate adjustments in response to changes in unemployment and interbank rates. CNB could use these findings to improve the understanding of policy changes and increase their effectiveness.
Originality/Value – The findings offer valuable insights for policymakers, governments, and banking industry participants seeking to enhance monetary policy efficiency
Culinary resilience in a globalized world: how social norms negotiate tradition and adaptation
No full textPurpose – This study investigates how macro-level forces and micro-level cultural constructs shape food culture resilience in different societies. It bridges the knowledge gaps between cultural preservation and adaptation.
Research methodology – PLS-SEM methodology was adopted in the survey data of six countries. Bootstrapping verified path coefficients and model robustness.
Findings – Cultural Openness supports Heritage, preventing globalization from disempowering myths. Social Pressure drives food culture resilience (FCR) in collectivistic cultures yet is diluted in individualistic settings. Taboos subside in secular/pluralistic settings, replicating trends for moral flexibility. Enhanced predictivity for institutionally institutionalized civilizations facilitates easier observation by the models of modest to considerable FCR variation.
Research limitations – Cross-sectional data limit causal inferences. Unmeasured variables could also affect outcomes in transition countries.
Practical applications – Strategies should be implemented with cultural correctness by policymakers and marketers. For instance, leveraging openness to redefine traditions in multicultural settings, framing dietetic interventions as collective efforts in collectivist contexts, and customizing products with flexible ethical schema in secular settings.
Originality/Value – This research fills macro-micro cultural theory gaps, suggesting “adaptive preservation” as a driver of traditions in a globalizing world. This research shakes homogenization discourses, providing a rich model for cultural.
Can self-determination motive predict creative thinking among university students?
No full textCreative thinking is considered as one of the important mental processes in success in life in general and in the university, as it plays an important role in the development and progress of societies in all fields, especially considering the scientific and technological revolution and the great acceleration in all areas of life. The progress and development of societies can be measured by the level of their individuals’ possession of thinking skills in general and creative thinking. There are many variables that can play an important role in creative thinking, one of these factors is the motivation of self-determination. The current study aimed to reveal the predictive ability of self-determination of creative thinking and whether there are statistically significant differences in them due to gender and college. The sample of the study consisted of 367 university students in Jordan who were selected by the cluster random method. To achieve the objectives of the study, two measures of self-determination motivation and creative thinking scale were used. The results showed that the level of creative thinking among the study sample was low in all dimensions of the scale, and there were no statistically significant differences in creative thinking attributed to gender, except for flexibility in which males outperformed females in a statistically significant way, and there were no differences due to the college variable. The results also indicated that self-determination predicts creative thinking among university students
Forecasting pandemic-induced changes in real estate market values through machine learning approaches
No full textIn this study, a new temporal segmentation method is used to forecasting the real estate market based on the structural and spatial attributes of 676 houses in Niğde, Türkiye, from the years 2019 to 2022. Artificial Neural Networks (ANN), Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbours (KNN) were employed for model development and comparative performance analysis. According to the results, the ANN model that used temporal variables showed the most successful performance by achieving the highest R2 for 2019 (1. period: 0.979, 2. period: 0.990, 3. period: 0.914, 4. period: 0.831) and 2022 (1. period: 0.971, 2. period: 0.975, 3. period: 0.586, 4. period: 0.896) scores. Additionally, the COD values (5%–10%) and PRD values (0.98 to 1.03) remained within the acceptable range, further validating the model’s reliability. RF model showed more effective performance than other models by achieving the highest R2: 0.510 for 2019 and R2: 0.509 for 2022 when temporal variables were excluded. These findings highlight the importance of integrating time-sensitive parameters into valuation models to improve forecast accuracy and robustness. The study offers a replicable, flexible methodology for crisis-responsive valuation, providing valuable insights for policymakers, investors, and urban planners aiming to mitigate risks and enhance resilience in real estate market decision-making
Evaluation model of construction quality data uncertainty: a study based on industry park project
No full textThis study proposes an evaluation and adjustment model for construction quality data uncertainty based on the research project of the Sichuan Provincial Building Industry Park. The core objective is to address the variability in concrete strength data caused by multiple factors during the construction process through a data-driven approach, which enables the accurate prediction of concrete strength. By analyzing construction quality data across different strength grades and curing ages, this study establishes a dynamic model centered on “data fitting” and “data prediction”. The model defines a reliability range for construction quality specific to the project, which facilitates the cleansing of outlier data and the adjustment of biased data. The results demonstrate that the model significantly enhances the reliability of the construction quality data. Further analysis of data from the same construction site revealed that it is highly applicable, and the date adjustment become more concentrated and stable. The fitting coefficient of this model approaches 1, which significantly improving the representativeness and reliability of the strength data. The findings provide a scientific basis for the dynamic assessment and optimization of construction quality, and robust support for quality control and prediction during the construction process
Performance comparison of various time-series forecasting models for bridge sufficiency rating prediction
No full textThe rapid increase in the number of bridges worldwide has intensified the need for effective maintenance strategies to ensure structural safety and economic efficiency. Accurate predictions of future bridge performance are essential for preventing unexpected failures and optimizing road network maintenance planning. However, existing prediction models frequently overlook the time-series characteristics inherent in bridge inspection data, thereby limiting their accuracy. This study aims to develop improved prediction models by integrating sequential data patterns using advanced deep-learning techniques. Data from the National Bridge Inventory were utilized. As most NBI data lacked explicit sequential structures, preprocessing techniques were applied to generate meaningful time-series patterns. Deep-learning models, including deep neural networks (DNNs), convolutional neural networks, long short-term memory (LSTM), and Transformers, were developed and evaluated using cross-validation to optimize their performance. Results showed that the LSTM model improved prediction accuracy by approximately 46% compared to the baseline DNN model. The Transformer model further improved accuracy by approximately 7% over the LSTM, highlighting its superior ability to capture long-term dependencies. These findings highlight the potential of the Transformer model as a powerful tool for predicting bridge performance, thereby supporting effective maintenance planning and reducing the risk of structural failures
Quality of designed landscapes and the possibilities for their assessment
No full textThis paper explores the concept of quality and the possibilities of assessing the quality of designed landscapes in an urbanised environment. Quality is examined in the context of urban green space issues. The quality of urban green spaces is an important factor for human health, well-being and the effectiveness of urban green infrastructure. However, its assessment remains a challenge due to different terms, quality data and assessment methods. Drawing on international and national sources, the tangible and intangible dimensions of quality, objective and subjective methods of assessment, and key quality criteria are discussed. Examples from Lithuanian cities (Vilnius, Lazdijai, Naujoji Akmenė) are given, revealing discrepancies between planning decisions and user experience. The paper proposes the integration of evaluation models based on both quantitative and qualitative data, including innovative methods such as the analysis of geographic information provided by social media or smartphone apps.
Article in English.
Kraštovaizdžio architektūros objektų kokybė ir jos vertinimo galimybės
Santrauka
Šiame straipsnyje nagrinėjama kraštovaizdžio architektūros objektų kokybės samprata ir vertinimo galimybės urbanizuotoje aplinkoje. Kokybė nagrinėjama analizuojant miesto žaliosios erdvės problematiką. Miesto žaliųjų erdvių kokybė yra svarbus veiksnys žmonių sveikatai, gerovei bei žaliosios miesto infrastruktūros efektyvumui. Tačiau jos vertinimas išlieka iššūkiu dėl skirtingų terminų, kokybės sričių ir vertinimo metodų. Remiantis tarptautiniais ir nacionaliniais šaltiniais, aptariamos pamatuojamos ir nepamatuojamos kokybės dimensijos, objektyvūs ir subjektyvūs vertinimo būdai bei pagrindiniai kokybės kriterijai. Pateikiami pavyzdžiai iš Lietuvos miestų (Vilniaus, Lazdijų, Naujosios Akmenės), atskleidžiantys planavimo sprendimų ir naudotojų patirties neatitikimus. Straipsnyje siūloma integruoti kompleksinius vertinimo modelius, pagrįstus tiek kiekybiniais, tiek kokybiniais duomenimis, įtraukiant ir inovatyvius metodus, tokius kaip socialinių medijų ar išmaniųjų telefonų programėlėmis teikiamos geografinės informacijos analizė. Šis tyrimas rodo, kad kraštovaizdžio objektų kokybė yra daugialypė ir reikalauja aiškiai įsivardintų vertinimo priemonių, siekiant užtikrinti ekosistemos paslaugas teikiančią, socialiai teisingą ir naudotojų poreikius atitinkančią miestų aplinką.
Reikšminiai žodžiai: kraštovaizdžio architektūra, miesto žalioji erdvė, kokybė, vertinimo metodai, lankytojų patirtis