【摘要】：物流配送是現(xiàn)代化物流系統(tǒng)的一個重要環(huán)節(jié),它是指根據(jù)客戶的訂單需求,在配送中心進(jìn)行分貨、配貨,并將配好的貨物及時運(yùn)送到客戶。在配送業(yè)務(wù)中,存在許多決策優(yōu)化的問題,其中車輛路徑的優(yōu)化對物流企業(yè)加快配送速度、提高服務(wù)質(zhì)量、降低配送成本以及增加經(jīng)濟(jì)效益有較大的影響。根據(jù)配送中心數(shù)目的多少,車輛路徑問題有單配送中心車輛路徑問題和多配送中心車輛路徑問題之分；根據(jù)客戶服務(wù)需求,車輛路徑問題有純送貨車輛路徑問題、純?nèi)∝涇囕v路徑問題和取送一體化車輛路徑問題之分。但在實(shí)際中,往往存在的是取送一體化多配送中心車輛路徑問題(Multiple Depot Vehicle Routing Problem with Simultaneous Pick-up and Deliveries, MDVRPSPD)。因此對取送一體化多配送中心車輛路徑問題進(jìn)行研究具有重要的現(xiàn)實(shí)意義。本文針對MDVRPSPD主要進(jìn)行了以下幾個方面的研究：首先,本文在對傳統(tǒng)車輛路徑問題進(jìn)行理論研究的基礎(chǔ)上,根據(jù)現(xiàn)實(shí)的需求,提出了更貼近現(xiàn)實(shí)的取送一體化的多配送中心車輛路徑問題,并且建立了相應(yīng)的數(shù)學(xué)模型；其次,本文對車輛路徑的求解方法進(jìn)行了分析和比較,指出了傳統(tǒng)方法求解多配送中心車輛路徑問題存在的不足,提出了增加虛擬配送中心的方法把多配送中心車輛路徑問題轉(zhuǎn)化成單配送中心車輛路徑問題,然后再對單配送中心車輛路徑問題進(jìn)行求解；最后,針對遺傳算法在求解單配送中心車輛路徑問題時存在早熟收斂,易陷入局部最優(yōu)解,本文提出利用云模型中云滴所具有的隨機(jī)性與穩(wěn)定傾向性,由正態(tài)云模型的X條件云發(fā)生器實(shí)現(xiàn)對交叉概率和變異概率的調(diào)整,從而提高了遺傳算法的性能。最后將改進(jìn)后的遺傳算法應(yīng)用于車輛路徑問題的求解中,并將其求解的結(jié)果與傳統(tǒng)方法求解結(jié)果進(jìn)行了比較,體現(xiàn)了改進(jìn)算其可行性和有效性。
[Abstract]:Logistics distribution is an important link in modern logistics system. It refers to the distribution and distribution of goods in the distribution center according to the customer's order demand and the timely delivery of the matched goods to the customer. In the distribution business, there are many problems of decision optimization, among which the optimization of vehicle routing has a great impact on logistics enterprises to speed up distribution, improve the quality of service, reduce the cost of distribution and increase economic benefits. According to the number of distribution centers, the vehicle routing problem can be divided into single distribution center vehicle routing problem and multi-distribution center vehicle routing problem; according to customer service demand, the vehicle routing problem has a pure delivery vehicle routing problem. The path problem of pure cargo vehicle and the vehicle routing problem of integration of pick-up and delivery. However, in practice, the vehicle routing problem of multi-distribution center is often (Multiple Depot Vehicle Routing Problem with Simultaneous Pick-up and Deliveries, MDVRPSPD). Therefore, it is of great practical significance to study the vehicle routing problem of multi-distribution center. This paper mainly studies the following aspects of MDVRPSPD: firstly, based on the theoretical research of the traditional vehicle routing problem, according to the actual needs, This paper puts forward the vehicle routing problem of multi-distribution center, which is closer to the reality, and establishes the corresponding mathematical model. Secondly, this paper analyzes and compares the methods of solving the vehicle path. This paper points out the shortcomings of the traditional method to solve the vehicle routing problem of multi-distribution center, and puts forward the method of adding virtual distribution center to transform the vehicle routing problem of multi-distribution center into single distribution center vehicle routing problem. Then the vehicle routing problem of single distribution center is solved. Finally, the genetic algorithm has premature convergence in solving the vehicle routing problem of single distribution center, which is easy to fall into the local optimal solution. In this paper, the random and stable tendency of cloud droplets in cloud model is used to adjust the crossover probability and mutation probability by the X condition cloud generator of normal cloud model, so as to improve the performance of genetic algorithm. Finally, the improved genetic algorithm is applied to the vehicle routing problem, and the results of the improved genetic algorithm are compared with those of the traditional method, which shows the feasibility and effectiveness of the improved algorithm.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：F259.1

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本文編號：2174202