A self-adaptive memeplexes robust search scheme for solving stochastic demands vehicle routing problem
In this article, we proposed a self-adaptive memeplex robust search (SAMRS) for finding robust and reliable solutions that are less sensitive to stochastic behaviours of customer demands and have low probability of route failures, respectively, in vehicle routing problem with stochastic demands (VRP...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/101715 http://hdl.handle.net/10220/17038 |
| _version_ | 1811690062511865856 |
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| author | Chen, Xianshun Feng, Liang Ong, Yew Soon |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Chen, Xianshun Feng, Liang Ong, Yew Soon |
| author_sort | Chen, Xianshun |
| collection | NTU |
| description | In this article, we proposed a self-adaptive memeplex robust search (SAMRS) for finding robust and reliable solutions that are less sensitive to stochastic behaviours of customer demands and have low probability of route failures, respectively, in vehicle routing problem with stochastic demands (VRPSD). In particular, the contribution of this article is three-fold. First, the proposed SAMRS employs the robust solution search scheme (RS 3) as an approximation of the computationally intensive Monte Carlo simulation, thus reducing the computation cost of fitness evaluation in VRPSD, while directing the search towards robust and reliable solutions. Furthermore, a self-adaptive individual learning based on the conceptual modelling of memeplex is introduced in the SAMRS. Finally, SAMRS incorporates a gene-meme co-evolution model with genetic and memetic representation to effectively manage the search for solutions in VRPSD. Extensive experimental results are then presented for benchmark problems to demonstrate that the proposed SAMRS serves as an efficable means of generating high-quality robust and reliable solutions in VRPSD. |
| first_indexed | 2024-10-01T05:58:01Z |
| format | Journal Article |
| id | ntu-10356/101715 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:58:01Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/1017152020-05-28T07:18:58Z A self-adaptive memeplexes robust search scheme for solving stochastic demands vehicle routing problem Chen, Xianshun Feng, Liang Ong, Yew Soon School of Computer Engineering DRNTU::Engineering::Computer science and engineering In this article, we proposed a self-adaptive memeplex robust search (SAMRS) for finding robust and reliable solutions that are less sensitive to stochastic behaviours of customer demands and have low probability of route failures, respectively, in vehicle routing problem with stochastic demands (VRPSD). In particular, the contribution of this article is three-fold. First, the proposed SAMRS employs the robust solution search scheme (RS 3) as an approximation of the computationally intensive Monte Carlo simulation, thus reducing the computation cost of fitness evaluation in VRPSD, while directing the search towards robust and reliable solutions. Furthermore, a self-adaptive individual learning based on the conceptual modelling of memeplex is introduced in the SAMRS. Finally, SAMRS incorporates a gene-meme co-evolution model with genetic and memetic representation to effectively manage the search for solutions in VRPSD. Extensive experimental results are then presented for benchmark problems to demonstrate that the proposed SAMRS serves as an efficable means of generating high-quality robust and reliable solutions in VRPSD. 2013-10-30T04:08:57Z 2019-12-06T20:43:11Z 2013-10-30T04:08:57Z 2019-12-06T20:43:11Z 2012 2012 Journal Article Chen, X., Feng, L., & Ong, Y. S. (2012). A self-adaptive memeplexes robust search scheme for solving stochastic demands vehicle routing problem. International Journal of Systems Science, 43(7), 1347-1366. 0020–7721 https://hdl.handle.net/10356/101715 http://hdl.handle.net/10220/17038 10.1080/00207721.2011.618646 en International journal of systems science |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Chen, Xianshun Feng, Liang Ong, Yew Soon A self-adaptive memeplexes robust search scheme for solving stochastic demands vehicle routing problem |
| title | A self-adaptive memeplexes robust search scheme for solving stochastic demands vehicle routing problem |
| title_full | A self-adaptive memeplexes robust search scheme for solving stochastic demands vehicle routing problem |
| title_fullStr | A self-adaptive memeplexes robust search scheme for solving stochastic demands vehicle routing problem |
| title_full_unstemmed | A self-adaptive memeplexes robust search scheme for solving stochastic demands vehicle routing problem |
| title_short | A self-adaptive memeplexes robust search scheme for solving stochastic demands vehicle routing problem |
| title_sort | self adaptive memeplexes robust search scheme for solving stochastic demands vehicle routing problem |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/101715 http://hdl.handle.net/10220/17038 |
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