發(fā)布時(shí)間：2018-01-03 06:26

  本文關(guān)鍵詞：D公司運(yùn)輸方案優(yōu)化研究　出處：《北京交通大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 運(yùn)輸方案優(yōu)化 運(yùn)輸整合 車輛路徑問題 改進(jìn)的蟻群算法

【摘要】：目前,眾多企業(yè)都致力于在物流環(huán)節(jié)找到新的利潤(rùn)增長(zhǎng)點(diǎn),對(duì)于運(yùn)輸方案的設(shè)計(jì)和優(yōu)化也成為了降低物流成本、實(shí)現(xiàn)利潤(rùn)增長(zhǎng)的重要目標(biāo)。D公司是一家體育用品零售商,同時(shí)有合作的30家原材料廠和74家成品廠,在原材料和成品運(yùn)輸中,D公司目前的運(yùn)輸方案已經(jīng)暴露出空載率高、運(yùn)輸批量和運(yùn)輸頻率不合適等諸多問題,造成了運(yùn)輸成本的居高不下。為解決上述問題,本文提出對(duì)D公司的運(yùn)輸方案進(jìn)行優(yōu)化。首先,本文深入分析了 D公司的運(yùn)輸需求并描述了 D公司的運(yùn)輸方案現(xiàn)狀,從而指出目前運(yùn)輸方案中存在空載率高和運(yùn)輸頻率小、批量大的問題。其次,本文提出了一個(gè)D公司的運(yùn)輸優(yōu)化方案,該方案將D公司目前各自獨(dú)立的原材料和成品運(yùn)輸過程整合為去程運(yùn)輸原材料、回程運(yùn)輸成品的方式并調(diào)整了運(yùn)輸頻率,同時(shí)通過對(duì)實(shí)際情況進(jìn)行分析闡述了運(yùn)輸方案優(yōu)化的原因和方案實(shí)施的可行性。再次,本文指出車輛行駛路線的確定為運(yùn)輸方案優(yōu)化的重點(diǎn),并分別在同時(shí)運(yùn)輸原材料和成品及只運(yùn)輸量差成品兩個(gè)運(yùn)輸過程中建立了車輛路徑模型,同時(shí)運(yùn)輸原材料和成品的車輛路徑模型在傳統(tǒng)的車輛路徑模型的基礎(chǔ)上將車輛行駛費(fèi)用和車輛租金綜合考慮作為目標(biāo),并根據(jù)原材料廠的可使用車輛數(shù)量添加了車輛數(shù)限制作為約束條件。然后,本文用MATLAB軟件將非高峰期和高峰期情況下不同原材料廠和成品廠的數(shù)據(jù)代入模型進(jìn)行求解,求解方法選用了改進(jìn)的蟻群算法,該算法在傳統(tǒng)蟻群算法的基礎(chǔ)上改進(jìn)了全局信息素更新規(guī)則,增加了局部信息素更新規(guī)則,提高了算法的全局搜索能力,使模型的求解結(jié)果更接近最優(yōu)解。最后,本文根據(jù)求解結(jié)果確定了非高峰期和高峰期情況下的車輛行駛路線,根據(jù)成品廠的需求情況確定了車輛的裝載方案,并選取空載率和庫(kù)存水平兩個(gè)指標(biāo)對(duì)優(yōu)化前后運(yùn)輸方案進(jìn)行了對(duì)比,得出優(yōu)化后運(yùn)輸方案的可行性和合理性。從結(jié)果上來看,本文通過對(duì)運(yùn)輸過程進(jìn)行整合并設(shè)計(jì)新的車輛行駛路線極大降低了 D公司的車輛空載率,降低了運(yùn)輸成本。本文的運(yùn)輸優(yōu)化方案優(yōu)化了運(yùn)輸頻率,解決了因原有的運(yùn)輸頻率小、批量大而造成的成品廠庫(kù)存水平高的問題,并且降低了原材料廠和D公司區(qū)域配送中心的庫(kù)存水平,降低了各方的庫(kù)存成本。同時(shí),本文為D公司提出的運(yùn)輸優(yōu)化方案對(duì)其他想進(jìn)行運(yùn)輸方案優(yōu)化的企業(yè)提供了參考和借鑒。
[Abstract]:At present, many enterprises are committed to find new profit growth point in the logistics chain, for the design and optimization of transportation scheme has become an important goal of reducing the logistics cost,.D company profit growth is a sporting goods retailer, and cooperation 30 raw materials factory and 74 products factory, in the raw materials and finished goods in transit, D's current transport scheme has exposed the load rate, transport volume and transport frequency is not suitable so many problems, caused the high transportation cost. To solve the above problems, this transport scheme of D's optimization. Firstly, this paper analyzes the D company's transport demand and describes the present situation of transportation scheme of D company, which pointed out that the existing transport scheme in high load rate and transport in low frequency, large quantities of the problem. Secondly, this paper puts forward the optimization of lost a D of the company The case, the program will D company currently separate raw materials and finished product transportation process integration to transport raw materials, finished products and adjust the way of return transportation transportation frequency, and expounds the feasibility of the implementation of the reasons and solutions of transportation planning optimization based on the actual situation analysis. Thirdly, this paper points out that the vehicle travel route. As a key transport optimization, and transportation in raw materials and finished products and transportation quantity difference finished two in the process of transportation established vehicle routing model, while the vehicle road transport of raw material and finished product size model in the traditional model of vehicle routing based on vehicle and vehicle rental fee considered as a target and, according to the quantity of raw material factory cars available add a limited number of vehicles as a constraint condition. Then, this paper used MATLAB software to non peak and peak period To solve the data into the model of different raw materials and finished products factory factory under the condition that the solution using improved ant colony algorithm, the algorithm based on traditional ant colony algorithm improves the global pheromone update rule, increase the update rules of local information, to improve the global search ability of the algorithm, the model result close to the optimal solution. Finally, according to the results to determine the non peak and peak under the condition of vehicle routes, according to the demand of product factory vehicle loading scheme was determined, compared and selected the load rate and inventory level two indexes before and after the optimization on the transportation plan, the feasibility of optimized transportation scheme and reasonable. As a result, through the integration of the transport process and design of the new vehicle route greatly reduces vehicle load D rate decreased The cost of transportation. The transportation optimized transportation frequency, solves the original frequency due to the transport of small, large quantities of products factory inventory levels caused by the problem of high and low material factory and D regional distribution center inventory levels, reduces the inventory cost. At the same time, provide a reference from the optimization program for the transport of D to optimize transportation program for other enterprises to.

【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F416.8;F252

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 周曉玲;王震;肖文濤;;原油遠(yuǎn)洋拼船運(yùn)輸方案優(yōu)化研究[J];中國(guó)石油大學(xué)學(xué)報(bào)(自然科學(xué)版);2016年02期

2 翁克瑞;諸克軍;劉耕;;協(xié)同運(yùn)輸?shù)穆肪�整合問題研究[J];中國(guó)管理科學(xué);2015年01期

3 葛顯龍;辜羽潔;王偉鑫;;供應(yīng)鏈環(huán)境下的庫(kù)存與運(yùn)輸整合優(yōu)化模型及算法[J];系統(tǒng)工程;2014年01期

4 陳迎欣;;基于改進(jìn)蟻群算法的車輛路徑優(yōu)化問題研究[J];計(jì)算機(jī)應(yīng)用研究;2012年06期

5 張建民;恰汗·合孜爾;高大利;;基于改進(jìn)蟻群算法的物流配送路徑問題研究[J];計(jì)算機(jī)工程與科學(xué);2010年07期

6 劉冉;江志斌;陳峰;劉黎明;劉樹軍;劉天堂;;多車場(chǎng)滿載協(xié)同運(yùn)輸問題模型與算法[J];上海交通大學(xué)學(xué)報(bào);2009年03期

7 陳相東;;多種運(yùn)輸方式的組合優(yōu)化模型及其求解[J];天津理工大學(xué)學(xué)報(bào);2008年04期

8 梁雪玲;靳文舟;;運(yùn)輸方式選擇的模型及算法研究[J];交通與計(jì)算機(jī);2008年03期

9 郭倩倩;黃天民;施繼忠;胡明俊;;一種改進(jìn)的蟻群算法及其在旅行商問題中的應(yīng)用[J];西南民族大學(xué)學(xué)報(bào)(自然科學(xué)版);2006年06期

10 樂逸祥;周磊山;樂群星;孫琦;;求解物流配送路徑優(yōu)化問題的一種改進(jìn)蟻群算法[J];計(jì)算機(jī)集成制造系統(tǒng);2006年06期

相關(guān)碩士學(xué)位論文 前7條

1 陳靜;以運(yùn)輸成本最低為目標(biāo)的同時(shí)取送貨車輛路徑優(yōu)化研究[D];吉林大學(xué);2016年

2 彭曦;回收需求隨機(jī)的帶時(shí)間窗逆向物流車輛路徑問題研究[D];武漢理工大學(xué);2013年

3 石華t@;改進(jìn)的蟻群算法在實(shí)際VRP中的應(yīng)用研究[D];山東大學(xué);2012年

4 王靚靚;國(guó)際工程物流項(xiàng)目中運(yùn)輸方案優(yōu)化研究[D];大連海事大學(xué);2010年

5 侯棟梁;大件貨物運(yùn)輸方案制定研究[D];西南交通大學(xué);2009年

6 吳強(qiáng);大件貨物加氫反應(yīng)器運(yùn)輸方案設(shè)計(jì)與實(shí)施[D];大連海事大學(xué);2006年

7 孟馨;水利水電工程物流分析與控制研究[D];天津大學(xué);2003年

，

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