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Mass Evacuation Transportation Model Using Hybrid Genetic Algorithm

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@article{IJASEIT11687,
   author = {Dahlan Abdullah and Herman Fithra},
   title = {Mass Evacuation Transportation Model Using Hybrid Genetic Algorithm},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {11},
   number = {3},
   year = {2021},
   pages = {1157--1161},
   keywords = {Transportation planning; auto-based evacuation model; linear programming; hybrid genetic algorithm.},
   abstract = {The process of evacuating natural disasters requires careful planning. In particular, the evacuation process needs attention in the evacuation process because it involves the safety of many people. Evacuation time itself depends on information about incomplete evacuation routes such as those concerning desired velocity and obstacle parameters. When viewed in terms of transportation planning for evacuation, it is an Auto-Based Evacuation Model problem where the community, in this case, drivers, certainly do not know the evacuation planning or the route they will go through because, in the event of a disaster, it cannot be predicted which areas will be affected. The routing problem can be viewed as a discrete problem where the traffic problem is following a user equilibrium model. It has a bi-level structure. Top-level is used to minimize evacuation time using the contraflow strategy. At the same time, traffic volume and travel time are modeled at a low level. This problem is a linear programming problem whose solution will be optimized using a Hybrid Genetic Algorithm. This model is proposed to carry out mass evacuation processes based on time-window constraints. Finally, computational results are provided to demonstrate the validity and robustness of the proposed model. Based on the test results, it can be seen that the designed model can adjust the path that the vehicle follows with the vehicle station by adjusting the available capacity. The results showed that the intended route provided by the model was the shortest route.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11687},
   doi = {10.18517/ijaseit.11.3.11687}
}

EndNote

%A Abdullah, Dahlan
%A Fithra, Herman
%D 2021
%T Mass Evacuation Transportation Model Using Hybrid Genetic Algorithm
%B 2021
%9 Transportation planning; auto-based evacuation model; linear programming; hybrid genetic algorithm.
%! Mass Evacuation Transportation Model Using Hybrid Genetic Algorithm
%K Transportation planning; auto-based evacuation model; linear programming; hybrid genetic algorithm.
%X The process of evacuating natural disasters requires careful planning. In particular, the evacuation process needs attention in the evacuation process because it involves the safety of many people. Evacuation time itself depends on information about incomplete evacuation routes such as those concerning desired velocity and obstacle parameters. When viewed in terms of transportation planning for evacuation, it is an Auto-Based Evacuation Model problem where the community, in this case, drivers, certainly do not know the evacuation planning or the route they will go through because, in the event of a disaster, it cannot be predicted which areas will be affected. The routing problem can be viewed as a discrete problem where the traffic problem is following a user equilibrium model. It has a bi-level structure. Top-level is used to minimize evacuation time using the contraflow strategy. At the same time, traffic volume and travel time are modeled at a low level. This problem is a linear programming problem whose solution will be optimized using a Hybrid Genetic Algorithm. This model is proposed to carry out mass evacuation processes based on time-window constraints. Finally, computational results are provided to demonstrate the validity and robustness of the proposed model. Based on the test results, it can be seen that the designed model can adjust the path that the vehicle follows with the vehicle station by adjusting the available capacity. The results showed that the intended route provided by the model was the shortest route.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11687
%R doi:10.18517/ijaseit.11.3.11687
%J International Journal on Advanced Science, Engineering and Information Technology
%V 11
%N 3
%@ 2088-5334

IEEE

Dahlan Abdullah and Herman Fithra,"Mass Evacuation Transportation Model Using Hybrid Genetic Algorithm," International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 3, pp. 1157-1161, 2021. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.11.3.11687.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Abdullah, Dahlan
AU  - Fithra, Herman
PY  - 2021
TI  - Mass Evacuation Transportation Model Using Hybrid Genetic Algorithm
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 11 (2021) No. 3
Y2  - 2021
SP  - 1157
EP  - 1161
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Transportation planning; auto-based evacuation model; linear programming; hybrid genetic algorithm.
N2  - The process of evacuating natural disasters requires careful planning. In particular, the evacuation process needs attention in the evacuation process because it involves the safety of many people. Evacuation time itself depends on information about incomplete evacuation routes such as those concerning desired velocity and obstacle parameters. When viewed in terms of transportation planning for evacuation, it is an Auto-Based Evacuation Model problem where the community, in this case, drivers, certainly do not know the evacuation planning or the route they will go through because, in the event of a disaster, it cannot be predicted which areas will be affected. The routing problem can be viewed as a discrete problem where the traffic problem is following a user equilibrium model. It has a bi-level structure. Top-level is used to minimize evacuation time using the contraflow strategy. At the same time, traffic volume and travel time are modeled at a low level. This problem is a linear programming problem whose solution will be optimized using a Hybrid Genetic Algorithm. This model is proposed to carry out mass evacuation processes based on time-window constraints. Finally, computational results are provided to demonstrate the validity and robustness of the proposed model. Based on the test results, it can be seen that the designed model can adjust the path that the vehicle follows with the vehicle station by adjusting the available capacity. The results showed that the intended route provided by the model was the shortest route.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11687
DO  - 10.18517/ijaseit.11.3.11687

RefWorks

RT Journal Article
ID 11687
A1 Abdullah, Dahlan
A1 Fithra, Herman
T1 Mass Evacuation Transportation Model Using Hybrid Genetic Algorithm
JF International Journal on Advanced Science, Engineering and Information Technology
VO 11
IS 3
YR 2021
SP 1157
OP 1161
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Transportation planning; auto-based evacuation model; linear programming; hybrid genetic algorithm.
AB The process of evacuating natural disasters requires careful planning. In particular, the evacuation process needs attention in the evacuation process because it involves the safety of many people. Evacuation time itself depends on information about incomplete evacuation routes such as those concerning desired velocity and obstacle parameters. When viewed in terms of transportation planning for evacuation, it is an Auto-Based Evacuation Model problem where the community, in this case, drivers, certainly do not know the evacuation planning or the route they will go through because, in the event of a disaster, it cannot be predicted which areas will be affected. The routing problem can be viewed as a discrete problem where the traffic problem is following a user equilibrium model. It has a bi-level structure. Top-level is used to minimize evacuation time using the contraflow strategy. At the same time, traffic volume and travel time are modeled at a low level. This problem is a linear programming problem whose solution will be optimized using a Hybrid Genetic Algorithm. This model is proposed to carry out mass evacuation processes based on time-window constraints. Finally, computational results are provided to demonstrate the validity and robustness of the proposed model. Based on the test results, it can be seen that the designed model can adjust the path that the vehicle follows with the vehicle station by adjusting the available capacity. The results showed that the intended route provided by the model was the shortest route.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11687
DO  - 10.18517/ijaseit.11.3.11687