本文關(guān)鍵詞：考慮碳排放的多車場(chǎng)多車型VRP模型及算法研究　出處：《深圳大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 車輛路徑問(wèn)題 多車場(chǎng) 多車型 碳排放 細(xì)菌覓食優(yōu)化算法

【摘要】：國(guó)際能源機(jī)構(gòu)聲明,交通運(yùn)輸是第二大二氧化碳排放來(lái)源,而交通運(yùn)輸中幾乎3/4的CO2排放來(lái)自于公路運(yùn)輸,因此,降低在車輛運(yùn)輸中所產(chǎn)生的二氧化碳排量是非常必要的。如今物流配送網(wǎng)絡(luò)日益發(fā)達(dá),為擴(kuò)大企業(yè)業(yè)務(wù),更及時(shí)地滿足客戶需求,許多企業(yè)都建立多個(gè)配送中心,并提供多種車型來(lái)適用各種不同類型的貨物。為使研究更趨于現(xiàn)實(shí),具有更大的實(shí)用價(jià)值,論文研究了考慮碳排放的多車場(chǎng)多車型車輛路徑問(wèn)題。本文在國(guó)家自然科學(xué)基金(71571120,71271140,71471158)、廣東省自然科學(xué)基金(2016A030310074)、廣東省促進(jìn)科技服務(wù)業(yè)發(fā)展計(jì)劃項(xiàng)目(2013B040403005)的資助下開(kāi)展了如下研究:首先,本文構(gòu)建了一個(gè)新的數(shù)學(xué)模型——考慮碳排放的多車場(chǎng)多車型車輛路徑問(wèn)題模型。論文對(duì)車輛路徑問(wèn)題的幾種不同模型進(jìn)行了分析研究,并針對(duì)碳排放問(wèn)題,考慮了碳排放量的計(jì)算和碳交易機(jī)制。碳排放量的計(jì)算主要通過(guò)計(jì)算能源消耗量來(lái)獲得,論文對(duì)車輛路徑問(wèn)題中能源消耗的幾種計(jì)算模式進(jìn)行了研究,并選用綜合燃油消耗計(jì)算模式來(lái)計(jì)算配送過(guò)程中車輛的油耗量。對(duì)于不同類型的車輛,固定費(fèi)用也會(huì)有所不同。模型還考慮了時(shí)間窗的約束條件,早到或者晚到都會(huì)產(chǎn)生時(shí)間窗懲罰費(fèi)用。因此,模型主要研究的是在多車場(chǎng)多車型VRP模型考慮了碳排放因素,加入碳交易機(jī)制,使總費(fèi)用達(dá)到最小。其次,論文對(duì)求解模型的算法進(jìn)行了分析和改進(jìn)。細(xì)菌覓食優(yōu)化算法(BFO)是一種較新穎的群體智能優(yōu)化算法,具有并行搜索、善于局部搜索的優(yōu)點(diǎn),而單循環(huán)結(jié)構(gòu)的細(xì)菌覓食優(yōu)化算法(SRBFO)降低了運(yùn)算復(fù)雜度,具有更好的收斂性和優(yōu)化效果。改進(jìn)的綜合學(xué)習(xí)粒子群算法(ECLPSO)采用了綜合學(xué)習(xí)的機(jī)制,能夠很大地提高解決方案的準(zhǔn)確性�；谝延械乃惴ㄑ芯砍晒�,本文結(jié)合了單循環(huán)結(jié)構(gòu)的細(xì)菌覓食優(yōu)化算法(SRBFO)和改進(jìn)的綜合學(xué)習(xí)粒子群算法(ECLPSO)中的綜合學(xué)習(xí)機(jī)制,構(gòu)造了新的算法——單循環(huán)結(jié)構(gòu)綜合學(xué)習(xí)細(xì)菌覓食優(yōu)化算法(SRCLBFO)。針對(duì)SRCLBFO算法,論文還分別選取了三組單峰函數(shù)和三組多峰函數(shù)進(jìn)行有效性驗(yàn)證。最后,論文將算法應(yīng)用到考慮碳排放的多車場(chǎng)多車型車輛路徑問(wèn)題實(shí)例中進(jìn)行優(yōu)化求解。通過(guò)實(shí)驗(yàn)結(jié)果對(duì)比,驗(yàn)證了模型和改進(jìn)算法的有效性。同時(shí),結(jié)合實(shí)例,論文還對(duì)單車場(chǎng)、多車場(chǎng)進(jìn)行對(duì)比實(shí)驗(yàn),證明多車場(chǎng)相對(duì)于單車場(chǎng)的優(yōu)勢(shì);對(duì)單車型、多車型進(jìn)行對(duì)比實(shí)驗(yàn),驗(yàn)證多車型較單車型的有效性。此外,論文還對(duì)碳交易機(jī)制中碳價(jià)格和碳配額上下波動(dòng)對(duì)費(fèi)用、碳排放量和距離等因素的影響進(jìn)行了分析。本文拓展了考慮低碳的車輛路徑問(wèn)題的研究,為企業(yè)實(shí)施低碳運(yùn)輸提供了一定的借鑒意義。
[Abstract]:Transport is the second largest source of carbon dioxide emissions, while almost 3/4 of CO2 emissions in transport come from road transport, the International Energy Agency said. It is necessary to reduce the carbon dioxide emissions generated in the vehicle transportation. Nowadays, the logistics distribution network is increasingly developed, in order to expand the business, more timely to meet customer needs. Many enterprises have set up multiple distribution centers, and provide a variety of models to apply to different types of goods. In order to make the research more realistic, it has more practical value. This paper studies the problem of multi-vehicle vehicle routing considering carbon emissions. This paper is based on the National Natural Science Foundation of China 71571120 71271140 71471158). Natural Science Foundation of Guangdong Province, 2016A030310074. The following research has been carried out with the support of the Guangdong Provincial Project for the Development of Science and Technology Services (2013B040403005): first of all. In this paper, a new mathematical model, multi-vehicle vehicle routing problem with carbon emissions, is constructed. Several different models of vehicle routing problem are analyzed and studied in this paper, and the carbon emission problem is analyzed. The calculation of carbon emissions and carbon trading mechanism are considered. The calculation of carbon emissions is mainly through the calculation of energy consumption. In this paper, several calculation models of energy consumption in vehicle routing problem are studied. A comprehensive fuel consumption calculation model is used to calculate the fuel consumption of vehicles in the distribution process. For different types of vehicles, the fixed costs will be different. The model also takes into account the constraints of the time window. Therefore, the main research of the model is to consider the carbon emission factors in multi-vehicle VRP model, add carbon trading mechanism to make the total cost to the minimum. This paper analyzes and improves the algorithm for solving the model. The bacterial foraging optimization algorithm (BFOO) is a novel swarm intelligence optimization algorithm with the advantages of parallel search and local search. The single cycle structure of bacteria foraging optimization algorithm (SRBFOO) reduces the computational complexity. The improved synthetic learning particle swarm optimization algorithm (ECLPSO) adopts the mechanism of integrated learning. Can greatly improve the accuracy of the solution. Based on the existing algorithm research results. This paper combines the integrated learning mechanism of single cycle structure bacterial foraging optimization algorithm (SRBFOO) and improved integrated learning particle swarm optimization (ECLPSO). A new algorithm, single cycle structure integrated learning bacteria foraging optimization algorithm, is constructed. The SRCLBFO algorithm is aimed at it. Three groups of single-peak functions and three groups of multi-peak functions are selected to verify the validity. Finally. In this paper, the algorithm is applied to solve the multi-vehicle vehicle routing problem with carbon emissions. The experimental results show that the model and the improved algorithm are effective. At the same time, an example is given. The paper also carries on the contrast experiment to the bicycle yard and the multi-car yard, proves the advantage of the multi-car yard compared with the single car yard; To verify the effectiveness of multi-vehicle model compared with single-vehicle model, the paper also makes a comparative experiment on the carbon price and carbon quota in the carbon trading mechanism. The effects of carbon emissions and distance are analyzed. This paper extends the research on the vehicle routing problem considering low carbon and provides a reference for enterprises to implement low-carbon transportation.
【學(xué)位授予單位】：深圳大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U492.3

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