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What is the purpose of the empirical case study on coal manufacturing in the paper?
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To demonstrate the proposed decision support model |
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Which factor does the model NOT consider in route selection for a multimodal transportation network?
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Cultural preferences |
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What is the role of expert judgments in the decision support model?
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They determine the empirical case study |
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What logistics system aspect does the proposed methodology aim to improve?
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Employee satisfaction |
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Essay | Describe the role of Analytic Hierarchy Process (AHP) and Zero-One Goal Programming (ZOGP) in the decision support model for determining an optimal multimodal transportation route. Explain how these methodologies contribute to the model's effectiveness and discuss any potential limitations.
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Freight transportation is an integral supply chain element for providing timely availability and the effective movement of raw materials and finished goods. Due to trade globalization, a traditional truck mode is no longer an all-time feasible solution. Beside the road congestion, traffic congestion and environmental issues are concerned on the agenda. Consequently, the EU transport policy aims to reduce road transport to less polluting and more energy efficient modes of transport. Multimodal transportation is currently a key element of modern transportation systems. Nonetheless, when focusing on the multimodal freight transportation systems, many problems are identified. Since multimodal transportation comprised of many factors and interactions among the different modes can be quite complex. |
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Route selection strategy has become the main aspect in the multimodal transportation network design project. The transport cost and time as well as the inherent risks must be considered when determining a corrective design plan. The selection of a multimodal transportation network route is a complex multi-objective decision problem in terms of multiple conflicting criteria, vague and inaccurate parameters. Furthermore, the uncertainty and imprecision of decision-makers are a significant characteristic of the problem. Considering the impact factors, the transport cost, time and a comprehensive risk assessment model were further created.
This research develops a decision support framework using Multi-Criteria Decision Making (MCDM) tool that includes a five-phase framework. The first phase is to define the scope of study and collect the data of each route. The second phase is to calculate transportation cost and time of each route based on realistic test data. The third phase contributes an integrated quantitative risk analysis, Fuzzy Analytic Hierarchy Process (FAHP) and Data Envelopment Analysis (DEA) methodology to evaluate the multimodal transportation risks. The fourth phase is to determine the weights of each factor which relies on decisions based on expert judgments. The significant weight of criteria obtained from FAHP can be integrated a multi-objective optimization. Finally, Zero-one Goal Programming (ZOGP) is used to generate the optimal multimodal transportation route. The approach is illustrated on actual multimodal coal transportation routes in Thailand. To validate the model and result, sensitivity analysis is carried out on each of the MCDM methods that are studied. It enables to provide a more accurate, practical and systematic decision support tool.
The contribution of this research lies in the development of a valid decision support approach that is flexible and applicable to the users in selecting a multimodal transportation route by minimizing cost, time and risk factors involved with multimodal transportation. The methodology can provide a guidance for effectively determining the multimodal transportation routes to improve performance of logistics systems. The results have shown that the approach can guide for determining the optimal route subject to the aforementioned attributes. |
MULTI OBJECTIVES OPTIMIZATION MODEL FOR MULTIMODAL COAL LOGISTICS AND TRANSPORTATION NETWORK
BY MS. KWANJIRA KAEWFAK |
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What is the role of the FAHP method in the proposed risk analysis model?
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To determine the weights of each risk criterion |
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| 7 |
Which industry is used as a case study in the proposed risk analysis model?
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Coal |
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What does the DEA method do in the proposed FAHP-DEA methodology?
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Determines weights |
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Which method is used to aggregate risk scores into an overall risk score in the proposed model?
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Genetic Algorithm |
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There are two main risk analysis methods. The easier and more convenient method is qualitative risk analysis. Qualitative risk analysis rates or scores risk based on the perception of the severity and likelihood of its consequences. Quantitative risk analysis, on the other hand, calculates risk based on available data. |
https://safetyculture.com/topics/risk-analysis/# |
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Essay | Using the coal industry case study, please explain how the proposed risk analysis model is practical and aids in prioritizing risks. Discuss how this model can be beneficial for industries in optimizing multimodal transportation routes under risk decision criteria.
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How were geotechnical parameters of soils at landslide-prone sites evaluated in the study?
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Satellite remote sensing datasets |
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The parameters that can be statistically analyzed are Angle of internal friction, Drained cohesion, Undrained cohesion, Relative density, Consistency index, Oedometric modulus of compressibility, Shear modulus, Young's modulus, Nspt, Natural unit weight, Saturated unit weight, Pocket penetrometer, Poisson's ratio |
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What modeling techniques were used to assess the probability of landslide occurrence in the future?
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Autoregressive Moving Average (ARIMA) model |
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What is the potential application of the study's findings in hazard management?
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Devising countermeasures for managing landslides |
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How does the study aim to contribute to hazard management in the Himalayas?
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By serving as a guiding framework for using artificial intelligence and machine learning |
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Essay | Explain the methodology employed in the study to evaluate geotechnical parameters and assess the probability of future landslide events. Discuss the potential implications of using artificial intelligence and machine learning in hazard management in the Himalayas, with reference to the study's guiding framework.
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How was the landslide inventory database divided for training and testing in the research?
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80% training, 20% testing |
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Which machine learning model showed the highest prediction rate among LR, RF, and DRT?
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Logistic Regression (LR) |
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What do the ROC values for training and testing data signify in the context of landslide susceptibility mapping?
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The accuracy of the machine learning models |
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Which model is considered more realistic according to susceptibility zones in the research?
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Decision and Regression Tree (DRT) |
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Essay | Compare and contrast the performance of Logistic Regression (LR), Random Forest (RF), and Decision and Regression Tree (DRT) models in landslide susceptibility mapping. Discuss the strengths and limitations of each model based on the research findings.
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The main purpose of the present study is to use three state-of-the-art data mining techniques, namely, logistic model tree (LMT), random forest (RF), and classification and regression tree (CART) models, to map landslide susceptibility. Long County was selected as the study area. First, a landslide inventory map was constructed using history reports, interpretation of aerial photographs, and extensive field surveys. A total of 171 landslide locations were identified in the study area. Twelve landslide-related parameters were considered for landslide susceptibility mapping, including slope angle, slope aspect, plan curvature, profile curvature, altitude, NDVI, land use, distance to faults, distance to roads, distance to rivers, lithology, and rainfall. The 171 landslides were randomly separated into two groups with a 70/30 ratio for training and validation purposes, and different ratios of non-landslides to landslides grid cells were used to obtain the highest classification accuracy. The linear support vector machine algorithm (LSVM) was used to evaluate the predictive capability of the 12 landslide conditioning factors. Second, LMT, RF, and CART models were constructed using training data. Finally, the applied models were validated and compared using receiver operating characteristics (ROC), and predictive accuracy (ACC) methods. Overall, all three models exhibit reasonably good performances; the RF model exhibits the highest predictive capability compared with the LMT and CART models. The RF model, with a success rate of 0.837 and a prediction rate of 0.781, is a promising technique for landslide susceptibility mapping. Therefore, these three models are useful tools for spatial prediction of landslide susceptibility. |
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The frequency of landslides and related economic and environmental damage has increased in recent decades across the hilly areas of the world, no exception is Bangladesh. Considering the first step in landslide disaster management, different methods have been applied but no methods found as best one. As a result, landslide assessment using different methods in different geographical regions has significant importance. The research aims to prepare and evaluate landslide susceptibility maps (LSMs) of the Chattogram district using three machine learning algorithms of Logistic Regression (LR), Random forest (RF) and Decision and Regression Tree (DRT). Sixteen landslide conditioning factors were determined considering topographic, hydro-climatic, geologic and anthropogenic influence. The landslide inventory database (255 locations) was randomly divided into training (80 %) and testing (20 %) sets. The LSMs showed that almost 9–12 % of areas of the Chattogram district are highly susceptible to landslides. The highly susceptible zones cover the Chattogram district's hill ranges where active morphological processes (erosion and denudation) are dominant. The ROC values for training data were 0.943, 0.917 and 0.947 and testing data were 0.963, 0.934 and 0.905 for LR, RF and DRT models, respectively. The accuracy is higher than the previous research in comparison to the extent of the study area and the size of the inventory. Among the models, LR showed the highest prediction rate and DRT showed the highest success rate. According to susceptibility zones, DRT is the more realistic model followed by LR. The maps can be applied at the local scale for landslide hazard management. |
A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility written by
Wei Chan , Xiaoshen Xie , Jiale Wang and Biswajeet Pradhan |
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