The synthesis model as a planning tool for effective supply chains resistant to adverse events

Semenov, Iouri N.; Jacyna, Marianna

doi:10.17531/ein.2022.1.16

Cited by 15 publications

(4 citation statements)

References 64 publications

(62 reference statements)

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“…Machine learning is also used to detect railway defects such as corrugation [ 18 ] which the performance was satisfying. Besides the mentioned literature, machine learning and mathematical models were used in different areas such as supply chain [ 19 ], railway design [ 20 ], inspection scheduling [ 21 ], and cost management [ 22 ]. The summary of related studies is shown in Table 3 .…”

Section: Exploration Of Track Geometry Measurement and Predictionmentioning

confidence: 99%

Track Geometry Prediction Using Three-Dimensional Recurrent Neural Network-Based Models Cross-Functionally Co-Simulated with BIM

Sresakoolchai

Kaewunruen

2022

Sensors

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Railway track maintenance plays an important role in enabling safe, reliable, and seamless train operations and passenger comfort. Due to the increasing rail transportation, rolling stocks tend to run faster and the load tends to increase continuously. As a result, the track deteriorates quicker, and maintenance needs to be performed more frequently. However, more frequent maintenance activities do not guarantee a better overall performance of the railway system. It is crucial for rail infrastructure managers to optimize predictive and preventative maintenance. This study is the world’s first to develop deep machine learning models using three-dimensional recurrent neural network-based co-simulation models to predict track geometry parameters in the next year. Different recurrent neural network-based techniques are used to develop predictive models. In addition, a building information modeling (BIM) model is developed to integrate and cross-functionally co-simulate the track geometry measurement with the prediction for predictive and preventative maintenance purposes. From the study, the developed BIM models can be used to exchange information for predictive maintenance. Machine learning models provide the average R2 of 0.95 and the average mean absolute error of 0.56 mm. The insightful breakthrough demonstrates the potential of machine learning and BIM for predictive maintenance, which can promote the safety and cost effectiveness of railway maintenance.

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Section: Exploration Of Track Geometry Measurement and Predictionmentioning

confidence: 99%

Track Geometry Prediction Using Three-Dimensional Recurrent Neural Network-Based Models Cross-Functionally Co-Simulated with BIM

Sresakoolchai

Kaewunruen

2022

Sensors

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“…The proper functioning of any construction company is often disturbed by the appearance of many decision-making problems, which certainly include difficulties related to the purchase and supply of materials and resources. The question of satisfying as much as possible the expectations of construction contractors with respect to suppliers of building materials has been addressed many times by researchers [1][2][3][4][5][6][7][8][9][10][11]. In literature, there are attempts to evaluate logistic systems by means of, among others, cost analyses and simulations, as well as multicriteria analysis of the impact of logistic system structure on inventory management.…”

Section: Review Of the Literaturementioning

confidence: 99%

“…Semenov and Jacyna [2] considered the problem of planning effective modular supply chains resistant to adverse events. Their analysis confirms the validity of forming efficient and reliable supply chains ready to match supplies to specific orders, adapt to flexible and innovative transformations, and minimize time losses and costs of restoring supply capacity in case of an emergency.…”

Section: Review Of the Literaturementioning

confidence: 99%

Evaluating logistic systems of building materials supply

Rzepecki

2022

tren

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There are many logistical tasks in the field of supplying construction materials, as well as financial and information flows in construction projects. All logistical processes related to the flow of resources, physical, informational, and financial, occurring between the different elements of the company's structure make up its logistical system. One of the many decisions made during the planning of a construction project is the choice of how to control the logistics system of supply. It is impossible to indicate a universal system that will optimally meet the needs of many construction projects due to the individual nature of each project. Properly selected type of logistic service and structure of logistic system allow one to reduce risk in supply chain management. This paper proposes a multi-criteria analysis to evaluate the models of the logistic system of supply in the execution of a construction project. The Analytic Hierarchy Process AHP was used for the comparative evaluation, allowing multi-criteria pairwise comparisons of the various systems. The proposed approach allows to indicate the structure of the logistic system, ensuring continuity of construction output and also reduction of logistic costs.

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“…Finally, in Section 6, we present our concluding remarks. It is worth noting that in the more general setting of information modelling, being able to detect epidemic waves may help supply chains in planning increased resistance to such adverse events [28]. We note that all computations were carried out using the Matlab software package.…”

Section: Introductionmentioning

confidence: 99%

A Skew Logistic Distribution for Modelling COVID-19 Waves and Its Evaluation Using the Empirical Survival Jensen–Shannon Divergence

Levene

2022

Entropy

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A novel yet simple extension of the symmetric logistic distribution is proposed by introducing a skewness parameter. It is shown how the three parameters of the ensuing skew logistic distribution may be estimated using maximum likelihood. The skew logistic distribution is then extended to the skew bi-logistic distribution to allow the modelling of multiple waves in epidemic time series data. The proposed skew-logistic model is validated on COVID-19 data from the UK, and is evaluated for goodness-of-fit against the logistic and normal distributions using the recently formulated empirical survival Jensen–Shannon divergence (ESJS) and the Kolmogorov–Smirnov two-sample test statistic (KS2). We employ 95% bootstrap confidence intervals to assess the improvement in goodness-of-fit of the skew logistic distribution over the other distributions. The obtained confidence intervals for the ESJS are narrower than those for the KS2 on using this dataset, implying that the ESJS is more powerful than the KS2.

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The synthesis model as a planning tool for effective supply chains resistant to adverse events

Cited by 15 publications

References 64 publications

Track Geometry Prediction Using Three-Dimensional Recurrent Neural Network-Based Models Cross-Functionally Co-Simulated with BIM

Track Geometry Prediction Using Three-Dimensional Recurrent Neural Network-Based Models Cross-Functionally Co-Simulated with BIM

Evaluating logistic systems of building materials supply

A Skew Logistic Distribution for Modelling COVID-19 Waves and Its Evaluation Using the Empirical Survival Jensen–Shannon Divergence

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