A self-adaptive memeplexes robust search scheme for solving stochastic demands vehicle routing problem

作者：Chen Xianshun^*; Feng Liang; Ong Yew Soon

来源：International Journal of Systems Science, 2012, 43(7): 1347-1366.

DOI：10.1080/00207721.2011.618646

• vehicle routing problems with stochastic demands
• self-adaptation
• memeplexes
• robust solution search scheme
• gene-meme co-evolution
• self-adaptive individual learning

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 (RS3) 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.

• 出版日期2012
• 单位南阳理工学院