發(fā)布時(shí)間：2018-11-09 12:44

【摘要】：隨著經(jīng)濟(jì)蓬勃發(fā)展,物流在各行各業(yè)的地位愈發(fā)重要,可謂是國民經(jīng)濟(jì)的命脈。然而,國內(nèi)物流水平與經(jīng)濟(jì)發(fā)展速度并不匹配,長此以往勢必拖累經(jīng)濟(jì)發(fā)展。時(shí)下,國家各項(xiàng)政策都提到需要提高國內(nèi)物流水平。提高物流水平需要多方面努力,但根本目的是要降低物流費(fèi)用。而物流費(fèi)用中,運(yùn)輸費(fèi)用占比達(dá)到一半,科學(xué)地進(jìn)行車輛調(diào)度,可以降低運(yùn)輸費(fèi)用、提高車輛使用率、減少空氣污染等。本文在研究配送中心車輛調(diào)度優(yōu)化問題時(shí),增加了客戶滿意度這一因素。增加的原因在于如今的市場是買方市場,客戶有眾多選擇,提高客戶滿意度可以留住現(xiàn)有客戶并且吸引潛在客戶。但是客戶對企業(yè)的貢獻(xiàn)程度或者說企業(yè)對客戶的依賴程度不同,企業(yè)在如今的激烈市場環(huán)境中想要立足必須準(zhǔn)確識別客戶,重點(diǎn)服務(wù)那些重要客戶,這里引入“客戶重要程度”這一概念表示客戶對企業(yè)的重要程度,而這一重要程度也將作為客戶滿意度的權(quán)重系數(shù),如此更為合理。至于在實(shí)際中如何計(jì)算“客戶重要程度”,將根據(jù)專家打分并運(yùn)用模糊綜合評價(jià)法求得各客戶的重要程度。如何將滿意度與調(diào)度優(yōu)化問題融合在一起,文章利用滿意度模糊隸屬函數(shù)表示滿意度,二者之間依靠車輛抵達(dá)客戶處的時(shí)間聯(lián)系起來。之后根據(jù)相應(yīng)條件,以總配送距離最短、滿意度最高為目標(biāo)建立數(shù)學(xué)模型。因?yàn)檫z傳算法具有很強(qiáng)的魯棒性和快速尋優(yōu)能力,已被前人證實(shí)了其有效性,所以本文選用遺傳算法求解此類問題。最后,在求解具體案例時(shí),引入企業(yè)A旗下配送中心的案例。考慮到案例實(shí)際的客戶點(diǎn)數(shù)量不多,如果按照多目標(biāo)優(yōu)化尋優(yōu),可能會(huì)導(dǎo)致解空間縮小,算法可能得到局部尋優(yōu)結(jié)果。所以文章將分別以運(yùn)輸距離最短、滿意度最高為單目標(biāo)進(jìn)行優(yōu)化。首先以運(yùn)輸距離最短為單目標(biāo)進(jìn)行優(yōu)化后,得到幾個(gè)優(yōu)良染色體,利用滿意度隸屬函數(shù)與滿意度權(quán)重系數(shù)計(jì)算染色體的加權(quán)客戶滿意度,再利用“單位滿意度行駛距離”指標(biāo)選取最優(yōu);之后以客戶滿意度最高為單目標(biāo)進(jìn)行優(yōu)化,得到幾條優(yōu)良染色體,再結(jié)合“單位滿意度行駛距離”指標(biāo)選取最優(yōu)。最后再次運(yùn)用“單位滿意度行駛距離”指標(biāo)選取前后二者間的最優(yōu)結(jié)果作為最終方案。
[Abstract]:With the booming development of economy, logistics is becoming more and more important in various industries, which is the lifeblood of national economy. However, the domestic logistics level and the economic development speed does not match, in the long run is bound to drag down the economic development. Nowadays, national policies all mention the need to improve the level of domestic logistics. It takes many efforts to improve the level of logistics, but the fundamental purpose is to reduce the cost of logistics. However, transportation costs account for half of the logistics costs. Scientific vehicle scheduling can reduce transportation costs, improve vehicle utilization rate, and reduce air pollution, and so on. In this paper, customer satisfaction is increased when studying vehicle scheduling optimization problem in distribution center. The increase is due to the fact that today's market is a buyer's market, with a wide range of choices, and increased customer satisfaction can retain existing customers and attract potential customers. However, the degree of customer's contribution to the enterprise or the degree of dependence of the enterprise on the customer is different. In order to establish a foothold in today's fierce market environment, enterprises must accurately identify customers and focus on serving those important customers. The concept of "customer importance" is introduced here to indicate the importance of the customer to the enterprise, and this important degree will be the weight coefficient of customer satisfaction, so it is more reasonable. As to how to calculate "customer importance" in practice, the importance degree of each customer will be obtained by using fuzzy comprehensive evaluation method and scoring by experts. In this paper, the fuzzy membership function of satisfaction degree is used to express the satisfaction degree, which depends on the time when the vehicle arrives at the customer. Then, according to the corresponding conditions, the mathematical model is established with the shortest distance and the highest satisfaction. Because genetic algorithm has strong robustness and fast optimization ability, it has been proved to be effective by predecessors, so this paper chooses genetic algorithm to solve this kind of problem. Finally, in solving specific cases, the introduction of enterprise A distribution center case. Considering that the actual number of customer points in the case is not many, if the optimization is based on multi-objective optimization, the solution space may be reduced, and the algorithm may get local optimization results. Therefore, the article will be the shortest transportation distance, the highest degree of satisfaction for single-objective optimization. First of all, after optimizing with the shortest transportation distance as a single objective, several excellent chromosomes are obtained, and the weighted customer satisfaction degree of the chromosome is calculated by using the degree of satisfaction membership function and the satisfaction degree weight coefficient. Secondly, the optimum is chosen by using the index of "driving distance per unit satisfaction degree". After that, the highest customer satisfaction is used as a single objective to optimize, and several excellent chromosomes are obtained, and then the optimum is selected by combining with the index of "driving distance per unit satisfaction". Finally, the final scheme is to select the optimal result between the two factors.
【學(xué)位授予單位】：成都理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F274;F426.4

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本文編號：2320449