Optimization of Vehicle Routing Problem with Time Window (VRPTW) for Food Product Distribution Using Genetics Algorithm
| Main Authors: | Pratama, Rayandra Yala, Mahmudy, Wayan Firdaus |
|---|---|
| Format: | Article info application/pdf Journal |
| Bahasa: | eng |
| Terbitan: |
Faculty of Computer Science (FILKOM) Brawijaya University
, 2017
|
| Online Access: |
http://jitecs.ub.ac.id/index.php/jitecs/article/view/16 http://jitecs.ub.ac.id/index.php/jitecs/article/view/16/19 |
| ctrlnum |
article-16 |
|---|---|
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><title lang="en-US">Optimization of Vehicle Routing Problem with Time Window (VRPTW) for Food Product Distribution Using Genetics Algorithm</title><creator>Pratama, Rayandra Yala</creator><creator>Mahmudy, Wayan Firdaus</creator><description lang="en-US">Food distribution process is very important task because the product can expire during distribution and the further the distance the greater the cost. Determining the route will be more difficult if all customers have their own time to be visited. This problem is known as the Vehicle Routing Problem with Time Windows (VRPTW). VRPTW problems can be solved using genetic algorithms because genetic algorithms generate multiple solutions at once. Genetic algorithms generate chromosomes from serial numbers that represent the customer number to visit. These chromosomes are used in the calculation process together with other genetic operators such as population size, number of generations, crossover and mutation rate. The results show that the best population size is 300, 3,000 generations, the combination of crossover and mutation rate is 0.4:0.6 and the best selection method is elitist selection. Using a data test, the best parameters give a good solution that minimize the distribution route.</description><publisher lang="en-US">Faculty of Computer Science (FILKOM) Brawijaya University</publisher><contributor lang="en-US"/><date>2017-11-05</date><type>Journal:Article</type><type>Other:info:eu-repo/semantics/publishedVersion</type><type>Journal:Article</type><type>File:application/pdf</type><identifier>http://jitecs.ub.ac.id/index.php/jitecs/article/view/16</identifier><identifier>10.25126/jitecs.20172216</identifier><source lang="en-US">Journal of Information Technology and Computer Science; Vol 2, No 2: November 2017</source><source>2540-9824</source><source>2540-9433</source><source>10.25126/jitecs.201722</source><language>eng</language><relation>http://jitecs.ub.ac.id/index.php/jitecs/article/view/16/19</relation><rights lang="en-US">Copyright (c) 2017 Journal of Information Technology and Computer Science</rights><recordID>article-16</recordID></dc>
|
| language |
eng |
| format |
Journal:Article Journal Other:info:eu-repo/semantics/publishedVersion Other File:application/pdf File Journal:Journal |
| author |
Pratama, Rayandra Yala Mahmudy, Wayan Firdaus |
| title |
Optimization of Vehicle Routing Problem with Time Window (VRPTW) for Food Product Distribution Using Genetics Algorithm |
| publisher |
Faculty of Computer Science (FILKOM) Brawijaya University |
| publishDate |
2017 |
| url |
http://jitecs.ub.ac.id/index.php/jitecs/article/view/16 http://jitecs.ub.ac.id/index.php/jitecs/article/view/16/19 |
| contents |
Food distribution process is very important task because the product can expire during distribution and the further the distance the greater the cost. Determining the route will be more difficult if all customers have their own time to be visited. This problem is known as the Vehicle Routing Problem with Time Windows (VRPTW). VRPTW problems can be solved using genetic algorithms because genetic algorithms generate multiple solutions at once. Genetic algorithms generate chromosomes from serial numbers that represent the customer number to visit. These chromosomes are used in the calculation process together with other genetic operators such as population size, number of generations, crossover and mutation rate. The results show that the best population size is 300, 3,000 generations, the combination of crossover and mutation rate is 0.4:0.6 and the best selection method is elitist selection. Using a data test, the best parameters give a good solution that minimize the distribution route. |
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IOS5163.article-16 |
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Universitas Brawijaya |
| affiliation |
mill.onesearch.id |
| institution_id |
30 |
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library:university library |
| library |
Fakultas Ilmu Komputer |
| library_id |
1383 |
| collection |
Journal of Information Technology and Computer Science (JITeCS) |
| repository_id |
5163 |
| subject_area |
Computer Science Information System Computer Engiineering Information Technology |
| city |
KOTA MALANG |
| province |
JAWA TIMUR |
| repoId |
IOS5163 |
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2018-01-25T01:42:21Z |
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2019-05-24T12:09:03Z |
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17.538404 |