Comparison of meta-heuristic algorithms for vehicle routing problem with time windows

© Springer International Publishing Switzerland 2016. This paper proposes three meta-heuristic algorithms, namely cuckoo search, central force optimization, and chemical reaction optimization for solving vehicle routing problem with time windows (VRPTW). A comparison study between different meta-heu...

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Main Authors: Teerapun Saeheaw, Nivit Charoenchai
Format: Book Series
Published: 2018
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Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84955516821&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/55771
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-557712018-09-05T03:01:13Z Comparison of meta-heuristic algorithms for vehicle routing problem with time windows Teerapun Saeheaw Nivit Charoenchai Engineering © Springer International Publishing Switzerland 2016. This paper proposes three meta-heuristic algorithms, namely cuckoo search, central force optimization, and chemical reaction optimization for solving vehicle routing problem with time windows (VRPTW). A comparison study between different meta-heuristic algorithms aims to identify their respective strengths and weaknesses . The objective of VRPTW is to serve all customers, at different geographic locations, with varying demands and within specific time windows. The performance evaluation is tested on Solomon’s 56 benchmark instances of 100 customer problems, and yielded 24 solutions better than or equal to the best known solution provided by published papers. This paper is also among the first to document the implementation of all the three meta-heuristic algorithms for VRPTW together with their comprehensive results. 2018-09-05T03:01:13Z 2018-09-05T03:01:13Z 2016-01-01 Book Series 18761119 18761100 2-s2.0-84955516821 10.1007/978-3-319-24584-3_108 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84955516821&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/55771
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Engineering
spellingShingle Engineering
Teerapun Saeheaw
Nivit Charoenchai
Comparison of meta-heuristic algorithms for vehicle routing problem with time windows
description © Springer International Publishing Switzerland 2016. This paper proposes three meta-heuristic algorithms, namely cuckoo search, central force optimization, and chemical reaction optimization for solving vehicle routing problem with time windows (VRPTW). A comparison study between different meta-heuristic algorithms aims to identify their respective strengths and weaknesses . The objective of VRPTW is to serve all customers, at different geographic locations, with varying demands and within specific time windows. The performance evaluation is tested on Solomon’s 56 benchmark instances of 100 customer problems, and yielded 24 solutions better than or equal to the best known solution provided by published papers. This paper is also among the first to document the implementation of all the three meta-heuristic algorithms for VRPTW together with their comprehensive results.
format Book Series
author Teerapun Saeheaw
Nivit Charoenchai
author_facet Teerapun Saeheaw
Nivit Charoenchai
author_sort Teerapun Saeheaw
title Comparison of meta-heuristic algorithms for vehicle routing problem with time windows
title_short Comparison of meta-heuristic algorithms for vehicle routing problem with time windows
title_full Comparison of meta-heuristic algorithms for vehicle routing problem with time windows
title_fullStr Comparison of meta-heuristic algorithms for vehicle routing problem with time windows
title_full_unstemmed Comparison of meta-heuristic algorithms for vehicle routing problem with time windows
title_sort comparison of meta-heuristic algorithms for vehicle routing problem with time windows
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84955516821&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/55771
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