【摘要】：近年來,隨著電子商務(wù)的發(fā)展,并且隨著貨運改革的不斷深入,加快鐵路向現(xiàn)代物流企業(yè)的建設(shè),物流行業(yè)再次迎來了大發(fā)展,物流配送在經(jīng)濟活動中的作用也愈發(fā)突顯。而發(fā)展往往伴隨著問題,因此,作為物流配送中的重要問題——車輛調(diào)度問題重新受到了學者們的關(guān)注。車輛調(diào)度問題已經(jīng)發(fā)展了幾十年,但是隨著社會的不斷發(fā)展,仍然有一些具有鮮明時代特征的新問題不斷涌現(xiàn),尤其是電商物流的大力發(fā)展,客戶滿意度在物流配送中愈發(fā)顯得重要,對企業(yè)的影響越來越大。配送作為一種具有服務(wù)性質(zhì)的行業(yè),對車輛調(diào)度問題進行優(yōu)化,不單是對配送企業(yè)的成本優(yōu)化,更是為了保障服務(wù)的高效性,提升客戶的滿意指數(shù),進而提高配送企業(yè)的競爭力,使其能夠激烈的競爭中獲得長遠的發(fā)展。本文在參閱了已有的相關(guān)文獻及研究成果的基礎(chǔ)上,建立了針對客戶滿意度評價的車輛優(yōu)化調(diào)度問題模型,并運用粒子群算法和改進粒子群算法分別對模型的實例進行了求解,以驗證算法的有效性。具體如下:(1)模型構(gòu)建方面。為了更好的對客戶的滿意度進行評估,使得模型更加貼近現(xiàn)實,本文引入梯形模糊時間函數(shù),利用客戶所期望的服務(wù)時間以及允許的服務(wù)時間這兩個時間段對客戶的滿意度進行評價,并且考慮成本和時間因素,最終建立了在滿足客戶滿意度最大的情況下,使得時間和成本最小的多目標模型。(2)求解算法方面。首先,對求解車輛調(diào)度問題的算法進行了詳細的研究,通過求解算例對算法進行對比分析,說明不同算法的優(yōu)缺點。然后,針對標準粒子群算法的缺陷,引進菌群算法中的復(fù)制和遷移算子,對其進行改進,使用標準測試函數(shù)對算法進行了驗證,通過測試可以得出,改進粒子群算法較標準粒子群算法而言,具有更強的搜索能力,并且有一定的能力跳出局部最優(yōu)。最后,為了更好的使用改進粒子群算法求解模型,對粒子的編碼方式,進化方式進行了改進,對權(quán)重方案的選擇進行了討論,最終通過對實例求解效果的分析對比,證明了改進粒子群算法在求解車輛調(diào)度問題中的有效性。
[Abstract]:In recent years, with the development of electronic commerce and the deepening of freight transport reform, the construction of railway to modern logistics enterprises has been accelerated, the logistics industry has again ushered in a great development, and the role of logistics distribution in economic activities has become increasingly prominent. But the development often accompanies the question, therefore, as the important problem in the logistics distribution, the vehicle scheduling problem has been paid more attention by the scholars. Vehicle scheduling problem has been developed for several decades, but with the continuous development of society, there are still some new problems with distinct characteristics of the times, especially the development of e-commerce logistics. Customer satisfaction is becoming more and more important in logistics distribution and has more and more influence on enterprises. As a kind of service industry, distribution optimizes the vehicle scheduling problem, not only to optimize the cost of distribution enterprises, but also to ensure the efficiency of service and improve the customer satisfaction index. And then improve the competitiveness of distribution enterprises, so that they can get a long-term development in the fierce competition. In this paper, based on the related literature and research results, a vehicle scheduling problem model for customer satisfaction evaluation is established, and particle swarm optimization algorithm and improved particle swarm optimization algorithm are used to solve the model. To verify the effectiveness of the algorithm. The details are as follows: (1) Model building. In order to better evaluate customer satisfaction and make the model closer to reality, this paper introduces trapezoidal fuzzy time function. The customer satisfaction is evaluated by the expected service time and the allowable service time, and the cost and time factors are taken into account. Multi-objective model with minimum time and cost. (2) algorithm. Firstly, the algorithm for vehicle scheduling problem is studied in detail, and the advantages and disadvantages of different algorithms are explained by comparing and analyzing the algorithm by solving an example. Then, aiming at the defects of the standard particle swarm algorithm, the replication and migration operators in the colony algorithm are introduced and improved, and the standard test function is used to verify the algorithm. Compared with the standard PSO, the improved PSO has a stronger searching ability and a certain ability to jump out of the local optimum. Finally, in order to better use the improved particle swarm optimization algorithm to solve the model, the encoding and evolution of particles are improved, and the selection of weight scheme is discussed. It is proved that the improved particle swarm optimization algorithm is effective in solving vehicle scheduling problems.
【學位授予單位】：蘭州交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F252

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