PENENTUAN RUTE TERBAIK PENDISTRIBUSIAN DENGAN METODE ANT COLONY OPTIMIZATION (STUDI KASUS PERUSAHAAN JASA PERGUDANGAN SPAREPART JAWA BARAT)

Annisa Indah Pratiwi, Sri Sustariyah, Akda Zahrotul Wathoni, Sobar Pazri, Dimas Cheli Ahsanunadia
{"title":"PENENTUAN RUTE TERBAIK PENDISTRIBUSIAN DENGAN METODE ANT COLONY OPTIMIZATION (STUDI KASUS PERUSAHAAN JASA PERGUDANGAN SPAREPART JAWA BARAT)","authors":"Annisa Indah Pratiwi, Sri Sustariyah, Akda Zahrotul Wathoni, Sobar Pazri, Dimas Cheli Ahsanunadia","doi":"10.36805/teknikindustri.v8i2.5644","DOIUrl":null,"url":null,"abstract":"This study aims to determine the best route for spare part product distribution. This research was conducted in one of the companies engaged in the production of spare parts, where in the company there is a Logistics department that functions to handle the product distribution process. In the process of distributing goods, costs are required. In addition, the trucks used in the process of distributing goods to each distributor go through different routes, so this research also aims to determine the best route based on the distance between locations. In order to obtain the best route, this research offers a solution using the Ant Colony Optimization (ACO) method by determining the route to be taken by the truck. The best route selection using the ACO algorithm uses the Traveling Salesman Problem (TSP) where one location point can only be made one visit. the best route results while the first cycle results are depots located in the area in the area of the best route V0-V4-V6-V2-V5-V7-V1-V8-V3-V0. That is the depot located in the Karawang area (V0) to MPP (V4), then to DAS (V6), then to KKI (V2), then to TBB (V5), then to MTN (V7), then to DYH (V1), then to MMH (V8), then to MSS (V3), and back to the depot (V0). The route covers a distance of 83 km. The route covers a distance of 255.5 km.Keywords: Ant Colony Optimization (ACO); Goods Distribution; Best Route","PeriodicalId":318629,"journal":{"name":"Industry Xplore","volume":"337 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Industry Xplore","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.36805/teknikindustri.v8i2.5644","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

This study aims to determine the best route for spare part product distribution. This research was conducted in one of the companies engaged in the production of spare parts, where in the company there is a Logistics department that functions to handle the product distribution process. In the process of distributing goods, costs are required. In addition, the trucks used in the process of distributing goods to each distributor go through different routes, so this research also aims to determine the best route based on the distance between locations. In order to obtain the best route, this research offers a solution using the Ant Colony Optimization (ACO) method by determining the route to be taken by the truck. The best route selection using the ACO algorithm uses the Traveling Salesman Problem (TSP) where one location point can only be made one visit. the best route results while the first cycle results are depots located in the area in the area of the best route V0-V4-V6-V2-V5-V7-V1-V8-V3-V0. That is the depot located in the Karawang area (V0) to MPP (V4), then to DAS (V6), then to KKI (V2), then to TBB (V5), then to MTN (V7), then to DYH (V1), then to MMH (V8), then to MSS (V3), and back to the depot (V0). The route covers a distance of 83 km. The route covers a distance of 255.5 km.Keywords: Ant Colony Optimization (ACO); Goods Distribution; Best Route
与蚂蚁殖民地优化方法进行最好的分配路线(西爪哇省维修服务案例研究)
本研究旨在确定备件产品配送的最佳路线。本研究是在一家从事备件生产的公司中进行的,该公司有一个物流部门,负责处理产品的分销过程。在分配货物的过程中,成本是需要的。此外,在向每个经销商配送货物的过程中,所使用的卡车会经过不同的路线,因此本研究的目的也是基于地点之间的距离来确定最佳路线。为了获得最佳路线,本研究采用蚁群优化(Ant Colony Optimization, ACO)方法,确定卡车的路线。使用蚁群算法的最佳路线选择使用旅行推销员问题(TSP),其中一个位置点只能访问一次。最佳路线结果而第一周期结果为车厂所在区域内最佳路线V0-V4-V6-V2-V5-V7-V1-V8-V3-V0。这是位于卡拉旺地区的仓库(V0)到MPP (V4),然后到DAS (V6),然后到KKI (V2),然后到TBB (V5),然后到MTN (V7),然后到DYH (V1),然后到MMH (V8),然后到MSS (V3),再回到仓库(V0)。这条路线全长83公里。这条路线全长255.5公里。关键词:蚁群算法;商品分布;最好的路线
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信