Heuristic approach for solving employee bus routes in a large-scale industrial factory

© 2017 Elsevier Ltd This paper compares different methods for solving a location-routing problem (LRP), using real-world data from the bus transport service for employees of a large-scale industrial factory in Thailand. We tested four AI (artificial intelligence) techniques Maximin, K-means, Fuzzy C...

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Main Authors: Leksakul K., Smutkupt U., Jintawiwat R., Phongmoo S.
Format: Journal
Published: 2017
Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85014517582&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/40586
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-405862017-09-28T04:10:22Z Heuristic approach for solving employee bus routes in a large-scale industrial factory Leksakul K. Smutkupt U. Jintawiwat R. Phongmoo S. © 2017 Elsevier Ltd This paper compares different methods for solving a location-routing problem (LRP), using real-world data from the bus transport service for employees of a large-scale industrial factory in Thailand. We tested four AI (artificial intelligence) techniques Maximin, K-means, Fuzzy C-means, and Competitive Learning and two hybrids of these four K-means with Competitive Learning and K-means with Maximin to allocate the bus stops. The efficiency of the algorithms was compared, in terms of the quality of the solutions. The K-means with Maximin provided the best solution, as it minimized number of bus stop locations and employees’ total traveling distance while satisfied employee at maximum radius 1.73 km, compared to K-means with Competitive Learning, as the same number of bus stop it provided higher total traveling distance and maximum radius. The other non-hybrid techniques provided higher number of bus stop locations. We then used ant colony optimization (ACO) to determine the optimal routing between the 300–700 bus stops as allocated by K-means with Maximin. The optimal bus routing to transport the factory's 5000 plus employees required 134 buses (134 independent routes) covering 500 bus stops and traveling nearly 5000 km. While optimal, this routing was costly and created monitoring difficulties. To address these concerns, we constrained the number of bus routes; while this dramatically increased the total distance, it provided a more practical solution for the factory. 2017-09-28T04:10:22Z 2017-09-28T04:10:22Z Journal 14740346 2-s2.0-85014517582 10.1016/j.aei.2017.02.006 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85014517582&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/40586
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
description © 2017 Elsevier Ltd This paper compares different methods for solving a location-routing problem (LRP), using real-world data from the bus transport service for employees of a large-scale industrial factory in Thailand. We tested four AI (artificial intelligence) techniques Maximin, K-means, Fuzzy C-means, and Competitive Learning and two hybrids of these four K-means with Competitive Learning and K-means with Maximin to allocate the bus stops. The efficiency of the algorithms was compared, in terms of the quality of the solutions. The K-means with Maximin provided the best solution, as it minimized number of bus stop locations and employees’ total traveling distance while satisfied employee at maximum radius 1.73 km, compared to K-means with Competitive Learning, as the same number of bus stop it provided higher total traveling distance and maximum radius. The other non-hybrid techniques provided higher number of bus stop locations. We then used ant colony optimization (ACO) to determine the optimal routing between the 300–700 bus stops as allocated by K-means with Maximin. The optimal bus routing to transport the factory's 5000 plus employees required 134 buses (134 independent routes) covering 500 bus stops and traveling nearly 5000 km. While optimal, this routing was costly and created monitoring difficulties. To address these concerns, we constrained the number of bus routes; while this dramatically increased the total distance, it provided a more practical solution for the factory.
format Journal
author Leksakul K.
Smutkupt U.
Jintawiwat R.
Phongmoo S.
spellingShingle Leksakul K.
Smutkupt U.
Jintawiwat R.
Phongmoo S.
Heuristic approach for solving employee bus routes in a large-scale industrial factory
author_facet Leksakul K.
Smutkupt U.
Jintawiwat R.
Phongmoo S.
author_sort Leksakul K.
title Heuristic approach for solving employee bus routes in a large-scale industrial factory
title_short Heuristic approach for solving employee bus routes in a large-scale industrial factory
title_full Heuristic approach for solving employee bus routes in a large-scale industrial factory
title_fullStr Heuristic approach for solving employee bus routes in a large-scale industrial factory
title_full_unstemmed Heuristic approach for solving employee bus routes in a large-scale industrial factory
title_sort heuristic approach for solving employee bus routes in a large-scale industrial factory
publishDate 2017
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85014517582&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/40586
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