Optimization for Routing Vehicles of Seafood Product Transportation

Main Authors: Soenandi, IA, Juan, Y, Budi, M
Format: application/pdf
Terbitan: , 2019
Subjects:
Online Access: http://repository.ukrida.ac.id:80/handle/123456789/51
ctrlnum --repository.ukrida.ac.id:80:123456789-51
fullrecord <?xml version="1.0"?> <dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><title>Optimization for Routing Vehicles of Seafood Product Transportation</title><creator>Soenandi, IA</creator><creator>Juan, Y</creator><creator>Budi, M</creator><subject>Optimization</subject><subject>Indonesia Marine product</subject><subject>Stochastic Vehicle Routing problem with Time Windows and Capacity Windows</subject><subject>Ant Colony Optimization</subject><description>Recently, increasing usage of marine products is creating new challenges for businesses of marine products in term of transportations that used to carry the marine products like seafood to the main warehouse. This can be a problem if the carrier fleet is limited, and there are time constraints in terms of the freshness of the marine product. there are many ways to solve this problem, including the optimization of routing vehicle. In this study, this strategy is to implement in the marine product business in Indonesia with such an expected arrangement of the company to optimize routing problem in transportation with time and capacity windows. Until now, the company has not used the scientific method to manage the routing of their vehicle from warehouse to the location of marine products source. This study will solve a stochastic Vehicle Routing Problems (VRP) with time and capacity windows by using the comparison of six methods and looking the best results for the optimization, in this research, we compared the optimization with another method such as branch and bound, dynamic programming and Ant Colony Optimization (ACO). Finally, we get the best result after running ACO algorithm with existing travel time data. With ACO algorithm was able to reduce vehicle travel time by 3189.65 minutes, which is about 23% less than existing and based on consideration of the constraints of time within 2days (including rest time for the driver) using 28 tons capacity of truck and the companies need two units of vehicles for transportation.</description><date>2019-03-18T03:51:09Z</date><date>2019-03-18T03:51:09Z</date><date>2018-11-19</date><identifier>1757-899X</identifier><identifier>http://repository.ukrida.ac.id:80/handle/123456789/51</identifier><language>en_US</language><type>File:application/pdf</type><type>File:application/pdf</type><recordID>--repository.ukrida.ac.id:80:123456789-51</recordID></dc>
format File:application/pdf
File
author Soenandi, IA
Juan, Y
Budi, M
title Optimization for Routing Vehicles of Seafood Product Transportation
publishDate 2019
topic Optimization
Indonesia Marine product
Stochastic Vehicle Routing problem with Time Windows and Capacity Windows
Ant Colony Optimization
url http://repository.ukrida.ac.id:80/handle/123456789/51
contents Recently, increasing usage of marine products is creating new challenges for businesses of marine products in term of transportations that used to carry the marine products like seafood to the main warehouse. This can be a problem if the carrier fleet is limited, and there are time constraints in terms of the freshness of the marine product. there are many ways to solve this problem, including the optimization of routing vehicle. In this study, this strategy is to implement in the marine product business in Indonesia with such an expected arrangement of the company to optimize routing problem in transportation with time and capacity windows. Until now, the company has not used the scientific method to manage the routing of their vehicle from warehouse to the location of marine products source. This study will solve a stochastic Vehicle Routing Problems (VRP) with time and capacity windows by using the comparison of six methods and looking the best results for the optimization, in this research, we compared the optimization with another method such as branch and bound, dynamic programming and Ant Colony Optimization (ACO). Finally, we get the best result after running ACO algorithm with existing travel time data. With ACO algorithm was able to reduce vehicle travel time by 3189.65 minutes, which is about 23% less than existing and based on consideration of the constraints of time within 2days (including rest time for the driver) using 28 tons capacity of truck and the companies need two units of vehicles for transportation.
id IOS6816.--repository.ukrida.ac.id:80:123456789-51
institution Universitas Kristen Krida Wacana
institution_id 165
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library
library Perpustakaan Universitas Kristen Krida Wacana
library_id 529
collection Repository UKRIDA
repository_id 6816
subject_area Medicine and Health/Ilmu Kedokteran, Ilmu Pengobatan dan Ilmu Kesehatan
Computer Science Education/Pendidikan Ilmu Komputer, Pendidikan Teknik Informatika
Econmics/Ilmu Ekonomi
Psychology/Psikologi, Ilmu Jiwa
city JAKARTA UTARA
province DKI JAKARTA
repoId IOS6816
first_indexed 2019-05-09T18:01:09Z
last_indexed 2019-11-06T13:10:25Z
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