| 1 |
What is the purpose of the empirical case study on coal manufacturing in the paper?
|
To demonstrate the proposed decision support model |
|
The empirical case study of coal manufacturing is conducted to demonstrate the proposed model. This methodology can provide a guidance for effectively determining the multimodal transportation routes to improve performance of logistics systems. |
Z.D.U. Durmusoglu, Assessment of techno-entrepreneurship projects by using Analytical Hierarchy Process (AHP), Technol. Soc., Vol. 54, 2018, pp. 41-46. |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 2 |
Which factor does the model NOT consider in route selection for a multimodal transportation network?
|
Cultural preferences |
|
Solving the freight route choice problem by simultaneously considering the wide range of constraints, including transport logistics cost, transport time, and inherent risk is considered as the most effective approach for generating an optimized freight route choice. |
1. H. Min, International intermodal choices via chance-constrained goal programming, Transp. Res. Part A Gen., Vol. 25, 1991, pp. 351-362.
2. F. Southworth and B.E. Peterson, Intermodal and international freight network modeling, Transp. Res. Part C Emerg. Technol., Vol. 8, 2000, pp. 147-166.
3. H. Ham, T.J. Kim, and D. Boyce, Implementation and estimation of a combined model of interregional, multimodal commodity shipments and transportation network flows, Transp. Res. Part B Methodol., Vol. 39, 2005, pp. 65-79.
4. X. Zhang, Z. He, and Y. Pan, Study on multimodal transport network model base on genetic algorithm method, in International Conference of Logistics Engineering and Management (ICLEM) (Chengdu, China), pp. 3514-3520.
5. H. Ayed, C. Galvez-Fernandez, Z. Habbas, and D. Khadraoui, Solving time-dependent multimodal transport problems using a transfer graph model, Comput. Ind. Eng., Vol. 61, 2011, pp. 391-401. |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 3 |
What is the role of expert judgments in the decision support model?
|
They define the ZOGP objective function |
|
This paper develops a decision support model using an analytic hierarchy process (AHP) and zero-one goal programing (ZOGP) to determine an optimal multimodal transportation route. |
1. A. Kengpol and S. Tuammee, The development of a decision support framework for a quantitative risk assessment in multimodal green logistics: an empirical study, Int. J. Prod. Res., Vol. 54, 2016, pp. 1020-1038.
2. W. Meethom and N. Koohathongsum-rit, Design of decision support system for road freight transportation routing using multilayer zero one goal programming, Eng. J., Vol. 22, 2018, pp. 185-205.
3. N. Huynh and F. Fotuhi, A new planning model to support logistics service providers in selecting mode, route, and terminal location, Polish Maritime Res., Vol. 20, 2013, pp. 67-73. |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 4 |
What logistics system aspect does the proposed methodology aim to improve?
|
Cost management |
|
Nowadays, several manufacturers are striving to reduce logistics cost, deliver products on time, minimize freight transportation damage or risks to remain competitive. |
R. Rostamzadeh and S. Sofian, Prioritizing effective 7Ms to improve production systems performance using fuzzy AHP and fuzzy TOPSIS, Expert Syst. Appl., Vol. 38, 2011, pp. 5166-5177. |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 5 |
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.
|
Solving the freight route choice problem by simultaneously considering the wide range of constraints, including transport logistics cost, transport time, and inherent risk is considered as the most effective approach for generating an optimized freight route choice. |
|
Solving the freight route choice problem by simultaneously considering the wide range of constraints, including transport logistics cost, transport time, and inherent risk is considered as the most effective approach for generating an optimized freight route choice. |
H. Ayed, C. Galvez-Fernandez, Z. Habbas, and D. Khadraoui, Solving time-dependent multimodal transport problems using a transfer graph model, Comput. Ind. Eng., Vol. 61, 2011, pp. 391-401. |
10 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 6 |
What is the role of the FAHP method in the proposed risk analysis model?
|
To determine the weights of each risk criterion |
|
The proposed FAHP-DEA methodology uses the FAHP method to determine the weights of each risk criterion. |
H. Ghunaim and J. Dichter, "Applying the FAHP to improve the performance evaluation reliability of software defect classifiers", IEEE Access, vol. 7, pp. 62794-62804, 2019. |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 7 |
Which industry is used as a case study in the proposed risk analysis model?
|
Coal |
|
A case study of the coal industry demonstrates that the proposed risk analysis model is practical and allows users to more accurately prioritize risks while selecting an optimal multimodal transportation route. |
R. Rostamzadeh and S. Sofian, "Prioritizing effective 7Ms to improve production systems performance using fuzzy AHP and fuzzy TOPSIS (case study)", Expert Syst. Appl., vol. 38, no. 5, pp. 5166-5177, May 2011. |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 8 |
What does the DEA method do in the proposed FAHP-DEA methodology?
|
Evaluates linguistic variables and generates risk scores |
|
The proposed method can group risk alternatives into different risk categories for each criterion by characterizing the linguistic assessment grades. When faced with a large number of alternatives, this approach is much more practical for rank-ordering decision alternatives. |
1. Y.-M. Wang, J. Liu and T. M. Elhag, "An integrated AHP—DEA methodology for bridge risk assessment", Comput. Ind. Eng., vol. 54, no. 3, pp. 513-525, 2008.
2. A. Hadi-Vencheh and A. Mohamadghasemi, "A fuzzy AHP-DEA approach for multiple criteria ABC inventory classification", Expert Syst. Appl., vol. 38, no. 4, pp. 3346-3352, Apr. 2011.
3. Q. H. Do and J.-F. Chen, "A hybrid fuzzy AHP-DEA approach for assessing University performance", WSEAS Trans. Bus. Econ., vol. 11, no. 1, pp. 386-397, 2014. |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 9 |
Which method is used to aggregate risk scores into an overall risk score in the proposed model?
|
Simple Additive Weighting (SAW) |
|
The simple additive weighting (SAW) method is used to aggregate risk scores under different risk criteria into an overall risk score. |
1. A. Kengpol and S. Tuammee, "The development of a decision support framework for a quantitative risk assessment in multimodal green logistics: An empirical study", Int. J. Prod. Res., vol. 54, no. 4, pp. 1020-1038, Feb. 2016.
2. Y.-M. Wang, J. Liu and T. M. Elhag, "An integrated AHP—DEA methodology for bridge risk assessment", Comput. Ind. Eng., vol. 54, no. 3, pp. 513-525, 2008. |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 10 |
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.
|
The process raises user's attention to the high-priority risks and is useful for industries in optimizing a multimodal transportation route under risk decision criteria. |
|
The process raises user's attention to the high-priority risks and is useful for industries in optimizing a multimodal transportation route under risk decision criteria. |
Y. Zhao, P. A. Ioannou and M. M. Dessouky, "Dynamic multimodal freight routing using a co-simulation optimization approach", IEEE Trans. Intell. Transp. Syst., vol. 20, no. 7, pp. 2657-2667, Jul. 2019. |
10 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 11 |
How were geotechnical parameters of soils at landslide-prone sites evaluated in the study?
|
Satellite remote sensing datasets |
|
Through extensive field surveys, we evaluated different geotechnical parameters of soils at the most landslide-prone site along the Highway and augmented it with the satellite remote sensing datasets to determine the threshold values that trigger a landslide |
1. Assessing the Yield of Wheat Using Satellite Remote Sensing-Based Machine Learning Algorithms and Simulation Modeling
2. Calibration and validation of a distributed energy–water balance model using satellite data of land surface temperature and ground discharge measurements |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 12 |
What modeling techniques were used to assess the probability of landslide occurrence in the future?
|
Autoregressive Moving Average (ARIMA) model |
|
And assess the probability of occurrence of landslide events in the future using Autoregressive Moving Average (ARIMA) model and IBM SPSS Forecasting Model. |
A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 13 |
What is the potential application of the study's findings in hazard management?
|
Devising countermeasures for managing landslides |
|
This work shall help devise countermeasures for managing the landslides in the study area locally and shall serve as the guiding framework for using artificial intelligence and machine learning techniques for hazard management in the Himalayas. |
1. A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data
2. Disaster prediction system using IBM SPSS data mining tool |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 14 |
How does the study aim to contribute to hazard management in the Himalayas?
|
By serving as a guiding framework for using artificial intelligence and machine learning |
|
Analyzed to determine the months with the most and least landslide events to set the danger levels and priorities for the months for the Early Warning System. It will help to perceive whether the months with heavy rainfall have more landslides or have no effect on triggering landslides. |
A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 15 |
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.
|
Statistical analysis such as time series forecasting using IBM SPSS and ARIMA are used to forecast the important and highly responsible variables to find the probability of occurrence of landslide events in the study area. Geological and field investigation is carried out to find the cause and threshold values of various factors responsible for landslides. |
|
Statistical analysis such as time series forecasting using IBM SPSS and ARIMA are used to forecast the important and highly responsible variables to find the probability of occurrence of landslide events in the study area. Geological and field investigation is carried out to find the cause and threshold values of various factors responsible for landslides. |
1. Management of landslides in a rural–urban transition zone using machine learning algorithms—a case study of a national highway (NH-44), India, in the rugged Himalayan terrains
2. Modeling the sediment retention and ecosystem provisioning services in the Kashmir valley, India, Western Himalayas
3. Rainfall thresholds for prediction of shallow landslides around Chamoli-Joshimath region, Garhwal Himalayas, India |
10 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 16 |
How was the landslide inventory database divided for training and testing in the research?
|
80% training, 20% testing |
|
The landslide inventory database (255 locations) was randomly divided into training (80 %) and testing (20 %) sets. |
1. Implications of landslide inventory in susceptibility modeling along a Himalayan highway corridor, India
2. Landslide susceptibility assessment based on an incomplete landslide inventory in the Jilong Valley, Tibet, Chinese Himalayas |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 17 |
Which machine learning model showed the highest prediction rate among LR, RF, and DRT?
|
Logistic Regression (LR) |
|
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. |
Application of logistic regression (LR) and frequency ratio (FR) models for landslide susceptibility mapping in Relli Khola river basin of Darjeeling Himalaya, India |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 18 |
What do the ROC values for training and testing data signify in the context of landslide susceptibility mapping?
|
The accuracy of the machine learning models |
|
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. |
1. An introduction to ROC analysis
2. Is the ROC curve a reliable tool to compare the validity of landslide susceptibility maps? |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 19 |
Which model is considered more realistic according to susceptibility zones in the research?
|
Decision and Regression Tree (DRT) |
|
According to susceptibility zones, DRT is the more realistic model followed by LR. |
1. Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia
2. Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia
3. Landslide susceptibility mapping using information value and logistic regression models in GonchaSisoEneses area, northwestern Ethiopia |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 20 |
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.
|
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. |
|
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. |
An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting and randomization |
10 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|