Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/101715
Title: A self-adaptive memeplexes robust search scheme for solving stochastic demands vehicle routing problem
Authors: Chen, Xianshun
Feng, Liang
Ong, Yew Soon
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2012
Source: 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.
Series/Report no.: International journal of systems science
Abstract: 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.
URI: https://hdl.handle.net/10356/101715
http://hdl.handle.net/10220/17038
ISSN: 0020–7721
DOI: 10.1080/00207721.2011.618646
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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