Heuristic approach for solving employee bus routes in a large-scale industrial factory. Issue 2 (April 2017)
- Record Type:
- Journal Article
- Title:
- Heuristic approach for solving employee bus routes in a large-scale industrial factory. Issue 2 (April 2017)
- Main Title:
- Heuristic approach for solving employee bus routes in a large-scale industrial factory
- Authors:
- Leksakul, Komgrit
Smutkupt, Uttapol
Jintawiwat, Raweeroj
Phongmoo, Suriya - Abstract:
- Abstract: 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 theAbstract: 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. … (more)
- Is Part Of:
- Advanced engineering informatics. Volume 32:Issue 2(2017:Apr.)
- Journal:
- Advanced engineering informatics
- Issue:
- Volume 32:Issue 2(2017:Apr.)
- Issue Display:
- Volume 32, Issue 2 (2017)
- Year:
- 2017
- Volume:
- 32
- Issue:
- 2
- Issue Sort Value:
- 2017-0032-0002-0000
- Page Start:
- 176
- Page End:
- 187
- Publication Date:
- 2017-04
- Subjects:
- Clustering -- Routing -- Ant colony optimization -- Bus stop allocation -- SBRP
Computer-aided engineering -- Periodicals
Engineering -- Data processing -- Periodicals
620.00285 - Journal URLs:
- http://www.sciencedirect.com/science/journal/14740346 ↗
http://books.google.com/books?id=KhFVAAAAMAAJ ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.aei.2017.02.006 ↗
- Languages:
- English
- ISSNs:
- 1474-0346
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 0696.851100
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 2132.xml