本文關(guān)鍵詞： 三維配載 多車型 車輛路徑 組合優(yōu)化　出處：《華南理工大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：配送活動(dòng)是根據(jù)客戶的要求,對(duì)貨物進(jìn)行揀選、加工、組配等作業(yè),并送達(dá)指定地點(diǎn)的物流活動(dòng),貨物的配載與配送過(guò)程是配送活動(dòng)的主要活動(dòng)之一,其成本也是配送活動(dòng)中的主要成本之一,通過(guò)提高車輛的配載效率、優(yōu)化車輛的配送路徑,可以有效降低運(yùn)輸配送成本,節(jié)省企業(yè)的物流運(yùn)作成本,提高企業(yè)的利潤(rùn)率。目前貨物配載多以考慮體積和重量為主,在實(shí)際的貨物配載中忽略了貨物的不同規(guī)格尺寸對(duì)配載的影響,容易造成在實(shí)際配載時(shí)出現(xiàn)體積和重量都滿足車輛額定容積和載重的約束,但受到貨物的規(guī)格尺寸限制而無(wú)法完全配載的情況;貨物配載與車輛的配送路徑的研究主要以分開(kāi)研究為主,將兩者結(jié)合從整體考慮的研究較少,本文的研究將貨物配載和車輛路徑問(wèn)題結(jié)合起來(lái)研究三維貨物配載與車輛路徑組合的優(yōu)化模型,以車輛容積利用率、載重利用率及配送成本為優(yōu)化目標(biāo),優(yōu)化貨物配載與車輛路徑。本文將三維、多車型的配載與車輛配送路徑相結(jié)合,考慮貨物尺寸、車輛尺寸、多車型、車輛重心等約束問(wèn)題建立以配送成本最低、車輛容積利用率最高、車輛載重利用率最高為優(yōu)化目標(biāo)的組合優(yōu)化模型。在建立模型的基礎(chǔ)上,為解決三維、多車型的貨物配載,設(shè)計(jì)剩余空間合并策略、配載優(yōu)化算法、配載檢驗(yàn)算法用于實(shí)現(xiàn)貨物的配載;選擇遺傳算法作為優(yōu)化算法,設(shè)計(jì)染色體編碼、種群初始化算法,確定選擇、交叉、變異操作規(guī)則,提高算法的適應(yīng)性、降低早熟的可能性同時(shí)加快算法的收斂速度,將配載相關(guān)算法和遺傳算法相結(jié)合,實(shí)現(xiàn)模型的求解。為了驗(yàn)證本文算法的求解效果,利用Gendreau等人提出的標(biāo)桿問(wèn)題討論算法的參數(shù)設(shè)置及算法求解的有效性,并將本文算法求解結(jié)果與Gendreau等人的結(jié)果進(jìn)行對(duì)比分析,驗(yàn)證了本文求解算法的有效性;最后以某知名物流企業(yè)的實(shí)際業(yè)務(wù)數(shù)據(jù)作為案例,采集該企業(yè)的客戶需求信息、位置信息等數(shù)據(jù),利用本文求解算法進(jìn)行求解,并與原方案進(jìn)行對(duì)比分析,驗(yàn)證了本文模型算法在實(shí)際應(yīng)用問(wèn)題上具有較好的求解效果。
[Abstract]:Distribution activity is to select, process, group and deliver goods to designated places according to the requirements of customers. The loading and distribution process of goods is one of the main activities of distribution activities. Its cost is also one of the main costs in distribution activities. By improving the efficiency of vehicle stowage and optimizing the distribution path of vehicles, the cost of transportation and distribution can be effectively reduced, and the logistics operation costs of enterprises can be saved. To improve the profit margin of the enterprise. At present, the bulk and weight of the cargo are mainly considered in the stowage, and the influence of the different size of the goods on the stowage is neglected in the actual loading. It is easy to cause the restriction of the volume and weight of the vehicle to meet the rated volume and load when the actual load is loaded, but it is unable to be completely loaded because of the restriction of the size of the cargo. The research on the distribution route of cargo stowage and vehicle is mainly focused on the separate research, and the research on the combination of the two from the overall consideration is less. The research of this paper combines the problem of cargo stowage and vehicle routing to study the optimization model of the combination of cargo stowage and vehicle routing, with the vehicle volume utilization ratio, load utilization ratio and distribution cost as the optimization goal. This paper combines the three dimensional and multi-model stowage with the vehicle distribution path, considering the size of goods, vehicle size, multi-model, vehicle center of gravity and other constraints to establish the lowest delivery cost. The combined optimization model with the highest utilization ratio of vehicle volume and the highest utilization rate of vehicle load is the optimal model. On the basis of the model, the combined strategy of remaining space is designed to solve the cargo stowage of three-dimensional and multi-vehicle models. The stowage optimization algorithm and the stowage inspection algorithm are used to realize the stowage of the goods. Genetic algorithm is selected as the optimization algorithm, chromosome coding, population initialization algorithm, selection, crossover, mutation operation rules, improve the adaptability of the algorithm. To reduce the possibility of premature convergence and speed up the convergence of the algorithm, the load correlation algorithm and genetic algorithm are combined to achieve the solution of the model, in order to verify the effectiveness of the algorithm in this paper. The parameter setting of the algorithm and the validity of the algorithm are discussed by using the benchmarking problem proposed by Gendreau et al. The results of this paper are compared with those of Gendreau et al. The validity of the algorithm is verified. Finally, take the actual business data of a well-known logistics enterprise as a case, collect the customer demand information, location information and other data of the enterprise, use this algorithm to solve, and compare with the original scheme. It is verified that the model algorithm is effective in practical application.
【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U492

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 姜啟躍;;基于改進(jìn)蟻群算法的考慮車輛行程約束的逆向物流車輛路徑問(wèn)題研究[J];物流技術(shù);2014年19期

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本文編號(hào)：1445452