【摘要】：近年來(lái),隨著電子商務(wù)的發(fā)展,并且隨著貨運(yùn)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(lái)了大發(fā)展,物流配送在經(jīng)濟(jì)活動(dòng)中的作用也愈發(fā)突顯。而發(fā)展往往伴隨著問(wèn)題,因此,作為物流配送中的重要問(wèn)題——車(chē)輛調(diào)度問(wèn)題重新受到了學(xué)者們的關(guān)注。車(chē)輛調(diào)度問(wèn)題已經(jīng)發(fā)展了幾十年,但是隨著社會(huì)的不斷發(fā)展,仍然有一些具有鮮明時(shí)代特征的新問(wèn)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(duì)企業(yè)的影響越來(lái)越大。配送作為一種具有服務(wù)性質(zhì)的行業(yè),對(duì)車(chē)輛調(diào)度問(wèn)題進(jìn)行優(yōu)化,不單是對(duì)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(jìn)而提高配送企業(yè)的競(jìng)爭(zhēng)力,使其能夠激烈的競(jìng)爭(zhēng)中獲得長(zhǎng)遠(yuǎn)的發(fā)展。本文在參閱了已有的相關(guān)文獻(xiàn)及研究成果的基礎(chǔ)上,建立了針對(duì)客戶滿意度評(píng)價(jià)的車(chē)輛優(yōu)化調(diào)度問(wèn)題模型,并運(yùn)用粒子群算法和改進(jìn)粒子群算法分別對(duì)模型的實(shí)例進(jìn)行了求解,以驗(yàn)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(duì)客戶的滿意度進(jìn)行評(píng)估,使得模型更加貼近現(xiàn)實(shí),本文引入梯形模糊時(shí)間函數(shù),利用客戶所期望的服務(wù)時(shí)間以及允許的服務(wù)時(shí)間這兩個(gè)時(shí)間段對(duì)客戶的滿意度進(jìn)行評(píng)價(jià),并且考慮成本和時(shí)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(shí)間和成本最小的多目標(biāo)模型。(2)求解算法方面。首先,對(duì)求解車(chē)輛調(diào)度問(wèn)題的算法進(jìn)行了詳細(xì)的研究,通過(guò)求解算例對(duì)算法進(jìn)行對(duì)比分析,說(shuō)明不同算法的優(yōu)缺點(diǎn)。然后,針對(duì)標(biāo)準(zhǔn)粒子群算法的缺陷,引進(jìn)菌群算法中的復(fù)制和遷移算子,對(duì)其進(jìn)行改進(jìn),使用標(biāo)準(zhǔn)測(cè)試函數(shù)對(duì)算法進(jìn)行了驗(yàn)證,通過(guò)測(cè)試可以得出,改進(jìn)粒子群算法較標(biāo)準(zhǔn)粒子群算法而言,具有更強(qiáng)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(jìn)粒子群算法求解模型,對(duì)粒子的編碼方式,進(jìn)化方式進(jìn)行了改進(jìn),對(duì)權(quán)重方案的選擇進(jìn)行了討論,最終通過(guò)對(duì)實(shí)例求解效果的分析對(duì)比,證明了改進(jìn)粒子群算法在求解車(chē)輛調(diào)度問(wèn)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(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：F252

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