本文關(guān)鍵詞： 車輛路徑優(yōu)化問題 隨機(jī)配送時間 客戶滿意度 機(jī)會約束 自適應(yīng)遺傳算法　出處：《蘭州交通大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：物流配送中的車輛路徑優(yōu)化問題(Vehicle Routing Problem,VRP)是當(dāng)今物流配送優(yōu)化的關(guān)鍵環(huán)節(jié)，一直是現(xiàn)代物流領(lǐng)域研究的熱點(diǎn)問題。有效地安排車輛行駛路徑，不僅可以加快對客戶需求的響應(yīng)速度，提高服務(wù)質(zhì)量，還可以降低物流服務(wù)商運(yùn)作成本。目前對于VRP的研究多把VRP的約束條件如行駛時間、服務(wù)時間等都看成是固定不變的靜態(tài)VRP，且對模型的目標(biāo)函數(shù)的設(shè)定多從配送企業(yè)出發(fā)，設(shè)定為車輛行駛距離最短、配送成本最低等單目標(biāo)函數(shù)，對于綜合考慮客戶滿意度、配送成本多目標(biāo)的VRP優(yōu)化研究還不多見。事實(shí)上，實(shí)際的物流配送系統(tǒng)中由于交通、車輛和自然條件等因素的影響，使得物流配送系統(tǒng)具有一定的隨機(jī)性和復(fù)雜性，因此對帶有隨機(jī)性VRP的研究更能貼近實(shí)際的配送情況。 本論文研究的重點(diǎn)是圍繞隨機(jī)配送時間車輛路徑問題進(jìn)行的。通過分析了國內(nèi)外VRP研究現(xiàn)狀，指出了國內(nèi)在VRP模型上研究還不夠深入的問題，確定了本文所要解決的問題。設(shè)計了新的適合實(shí)際情況的物流配送路徑優(yōu)化模型，，并進(jìn)行了實(shí)例驗(yàn)證。 首先，在分析了目前VRP模型的基礎(chǔ)上，本文在綜合考慮了企業(yè)運(yùn)輸成本的最小化以及顧客滿意度約束等多方面因素，通過對物流配送時間的隨機(jī)性和顧客的滿意度進(jìn)行相關(guān)的研究；采用隨機(jī)機(jī)會約束規(guī)劃理論構(gòu)建了VRP的隨機(jī)機(jī)會約束規(guī)劃模型，并將顧客滿意度函數(shù)作為首要的約束條件體現(xiàn)在模型當(dāng)中，在模型尋優(yōu)的過程中直接起作用，從而將配送中心以往不能量化的信譽(yù)損失間接的予以量化，這在很大程度上強(qiáng)化了配送中心的長遠(yuǎn)利益，也提高了顧客服務(wù)水平，即準(zhǔn)時化、高效率化等。 其次，在對模型的求解過程中采用了遺傳算法，由于標(biāo)準(zhǔn)遺傳算法在求解車輛路徑問題時易早熟收斂。本文根據(jù)求解VRP模型的特點(diǎn)，對標(biāo)準(zhǔn)的遺傳算法的遺傳操作進(jìn)行了改進(jìn)，設(shè)計了新的自適應(yīng)遺傳算法，算法的運(yùn)行參數(shù)交叉率和變異率不是固定的數(shù)值，而是能夠根據(jù)適應(yīng)度值在進(jìn)化的不同階段進(jìn)行自適應(yīng)調(diào)節(jié)。 最后，通過算例驗(yàn)證了模型和算法的可行性及有效性，對選用的算例建立了隨機(jī)VRP模型，采用改進(jìn)的遺傳算法對建立的模型進(jìn)行了求解，討論了不同的置信度和滿意度取值對于模型解的影響。研究的結(jié)果不僅對于車輛路徑問題的實(shí)際應(yīng)用具有指導(dǎo)意義，而且還能為物流配送調(diào)度系統(tǒng)提供決策支持。
[Abstract]:Vehicle Routing problem (VRP) is the key link of logistics distribution optimization and has been a hot issue in the field of modern logistics. It can not only speed up the response to customer demand, improve the quality of service, but also reduce the operating cost of logistics service providers. At present, the research on VRP mostly focuses on the constraints of VRP, such as travel time. The service time is regarded as the static VRP, and the objective function of the model is set from the distribution enterprise, which is the single objective function, such as the shortest vehicle travel distance, the lowest distribution cost, and the comprehensive consideration of customer satisfaction. Research on multi-objective VRP optimization of distribution cost is rare. In fact, due to the influence of traffic, vehicle and natural conditions, the logistics distribution system has a certain randomness and complexity in the actual logistics distribution system. Therefore, the research with random VRP can be more close to the actual distribution situation. This paper focuses on the problem of random distribution time vehicle routing. By analyzing the current research situation of VRP at home and abroad, this paper points out that the domestic research on VRP model is not enough. The problems to be solved in this paper are determined. A new logistics distribution route optimization model suitable for the actual situation is designed and verified by an example. First of all, based on the analysis of the current VRP model, this paper comprehensively considers the minimum transportation cost and the constraints of customer satisfaction, and so on. Through the research on the randomness of logistics distribution time and customer satisfaction, the stochastic chance constrained programming model of VRP is constructed by using stochastic opportunity constraint programming theory. And the customer satisfaction function is embodied in the model as the first constraint condition, and plays a direct role in the process of model optimization, thus indirectly quantifying the loss of reputation that the distribution center could not quantify in the past. To a great extent, it strengthens the long-term benefit of distribution center and improves the level of customer service, that is, punctuality, high efficiency and so on. Secondly, the genetic algorithm is used in the process of solving the model. Because the standard genetic algorithm is easy to converge in solving the vehicle routing problem, according to the characteristics of solving the VRP model, The genetic operation of the standard genetic algorithm is improved, and a new adaptive genetic algorithm is designed. The crossover rate and mutation rate of the running parameters of the algorithm are not fixed values. It is possible to adjust adaptively at different stages of evolution according to fitness. Finally, the feasibility and validity of the model and algorithm are verified by an example. The random VRP model is established for the selected example, and the improved genetic algorithm is used to solve the established model. The influence of different confidence and satisfaction values on the solution of the model is discussed. The results not only have guiding significance for the practical application of the vehicle routing problem, but also provide decision support for the logistics distribution scheduling system.
【學(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F252;F224

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 葛顯龍;許茂增;王偉鑫;;多車型車輛路徑問題的量子遺傳算法研究[J];中國管理科學(xué);2013年01期

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