本文選題：車輛路徑問(wèn)題　切入點(diǎn)：時(shí)間窗　出處：《計(jì)算機(jī)科學(xué)》2014年03期

【摘要】：針對(duì)帶時(shí)間窗車輛路徑問(wèn)題,設(shè)計(jì)了一種同時(shí)考慮顧客的時(shí)間和空間鄰近性的路徑改進(jìn)方法。首先設(shè)計(jì)了一種顧客間時(shí)空距離的表達(dá)方式,然后利用遺傳算法對(duì)顧客點(diǎn)進(jìn)行時(shí)空聚類,并將聚類結(jié)果應(yīng)用于路徑調(diào)整中,使得顧客盡可能被加入到時(shí)空距離近的顧客所在路徑中,這樣既能有效減小搜索范圍,又能更快到達(dá)更好的解。以含1000個(gè)點(diǎn)的標(biāo)準(zhǔn)問(wèn)題集作為算例,計(jì)算結(jié)果表明,與不采用時(shí)空聚類的方法相比,該算法能在更短的時(shí)間內(nèi)取得更好的解,顯示了在解決大規(guī)模車輛路徑問(wèn)題時(shí)具有很好的潛力。
[Abstract]:To solve the vehicle routing problem with time windows, a path improvement method considering both customer time and spatial proximity is proposed.In this paper, we first design a representation of the spatio-temporal distance between customers, and then use genetic algorithm to cluster the customer points in time and space, and apply the clustering results to path adjustment.The customer can be added to the path of the customer who is close to time and space as far as possible, which can not only reduce the search range effectively, but also reach a better solution faster.Taking the standard problem set with 1000 points as an example, the calculation results show that the algorithm can obtain better solution in a shorter time than the method without spatio-temporal clustering.It shows that there is a good potential in solving the large-scale vehicle routing problem.
【作者單位】： 清華大學(xué)深圳研究生院物流與交通學(xué)部;深圳市物流工程與仿真重點(diǎn)實(shí)驗(yàn)室;
【基金】：國(guó)家自然科學(xué)基金面上項(xiàng)目(71272030) 深圳科技計(jì)劃項(xiàng)目(CXZZ2013032114 5336439) 東莞科技計(jì)劃項(xiàng)目(201010810107)資助
【分類號(hào)】：U492.22;TP18

【共引文獻(xiàn)】

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本文編號(hào)：1701637