本文選題：電動(dòng)特種車輛　切入點(diǎn)：決策——規(guī)劃法　出處：《中國(guó)民航大學(xué)》2017年碩士論文

【摘要】：隨著我國(guó)民航事業(yè)迅速發(fā)展,機(jī)場(chǎng)客流量逐漸增加,停機(jī)坪特種車輛數(shù)量也在增加,特種車輛調(diào)度失誤導(dǎo)致的航班延誤的影響也逐漸增大。因此,特種車輛調(diào)度好壞至關(guān)重要。2015年3月民航局機(jī)場(chǎng)特種車輛“油改電”工作正式啟動(dòng),部分機(jī)場(chǎng)開始引入電動(dòng)特種車輛�？深A(yù)見,未來機(jī)場(chǎng)電動(dòng)特種車輛會(huì)逐漸普及,這將給調(diào)度問題提出新的要求和挑戰(zhàn)。由于電動(dòng)特種車輛自身結(jié)構(gòu)特點(diǎn),其調(diào)度方法與傳統(tǒng)燃油特種車輛有所區(qū)別。車聯(lián)網(wǎng)技術(shù)和車輛調(diào)度智能算法為解決這一問題提供了新的思路。本文針對(duì)機(jī)坪電動(dòng)特種車輛的調(diào)度問題提出決策——規(guī)劃法,該調(diào)度方法將調(diào)度問題分為兩層:決策層和路徑規(guī)劃層。決策層通過決策算法決策出適合執(zhí)行任務(wù)的車輛,將決策出的任務(wù)車輛送入路徑規(guī)劃層。路徑規(guī)劃層將任務(wù)車輛執(zhí)行任務(wù)歸結(jié)為n個(gè)城市的TSP問題(Travelling Salesman Problem),通過組合優(yōu)化算法構(gòu)建滿足約束條件的優(yōu)化目標(biāo)函數(shù),對(duì)決策層輸出的任務(wù)車輛任務(wù)行駛路徑進(jìn)行路徑規(guī)劃,求解全局最優(yōu)路徑。由于ID3(Iterative Dichotomiser 3)算法原理簡(jiǎn)單易懂,建樹所花時(shí)間少,本文中的決策層通過ID3算法構(gòu)建了電動(dòng)特種車輛任務(wù)分配模型。為了提高收斂到全局最優(yōu)解的概率和優(yōu)化求解速度,本文提出一種改進(jìn)的Hopfield神經(jīng)網(wǎng)絡(luò)算法——全反饋Hopfield神經(jīng)網(wǎng)絡(luò)(FFCHNN),通過分析其仿真結(jié)果證明該網(wǎng)絡(luò)求解TSP問題時(shí)比傳統(tǒng)Hopfield神經(jīng)網(wǎng)絡(luò)的性能要好,效率更高。本文采用該算法作為路徑規(guī)劃層對(duì)任務(wù)車輛進(jìn)行路徑規(guī)劃的優(yōu)化組合算法。最后,通過決策——規(guī)劃法解決電動(dòng)工具車調(diào)度實(shí)例,通過驗(yàn)證表明該方法能夠較為快速精準(zhǔn)決策適合執(zhí)行任務(wù)的車輛,并快速獲得出任務(wù)車輛最短路徑,為解決機(jī)場(chǎng)電動(dòng)特種車輛調(diào)度問題提供參考。
[Abstract]:With the rapid development of China's civil aviation industry, the airport passenger flow is increasing gradually, the number of special vehicles on the apron is also increasing, and the impact of flight delay caused by special vehicle scheduling errors is gradually increasing. Special vehicle scheduling is of great importance. In March 2015, the civil aviation bureau airport special vehicle "oil and electricity" work officially started, some airports began to introduce electric special vehicles. It is foreseeable that airport electric special vehicles will gradually become popular in the future. This will bring new requirements and challenges to the scheduling problem. Due to the structural characteristics of special electric vehicles, The method of dispatching is different from that of traditional special fuel vehicles. The technology of vehicle networking and the intelligent algorithm of vehicle scheduling provide a new way of thinking to solve this problem. In this paper, a decision-planning method is proposed for the scheduling of special electric vehicles on the aerodrome. The scheduling method divides the scheduling problem into two layers: decision layer and path planning layer. The task vehicle is put into the path planning layer. The path planning layer reduces the task execution of the mission vehicle to the TSP problem of n cities. The optimization objective function satisfying the constraint condition is constructed by combinatorial optimization algorithm. In order to solve the global optimal path, the path planning of the task vehicle path output from the decision level is carried out. Because the principle of the ID3(Iterative Dichotomiser 3 algorithm is simple and easy to understand, it takes less time to build a tree. In order to improve the probability of convergence to the global optimal solution and optimize the solution speed, the decision layer in this paper constructs the task assignment model of special electric vehicle by ID3 algorithm. In this paper, an improved Hopfield neural network algorithm, full feedback Hopfield neural network, is proposed. The simulation results show that the performance of the neural network is better than that of the traditional Hopfield neural network in solving the TSP problem. This paper uses this algorithm as the optimal combination algorithm for path planning of task vehicles. Finally, the decision-planning method is used to solve the scheduling example of electric tool vehicles. The verification results show that the proposed method can make accurate and fast decision on the vehicles that are suitable for the task and obtain the shortest path of the vehicle quickly. It provides a reference for solving the scheduling problem of special electric vehicles in the airport.
【學(xué)位授予單位】：中國(guó)民航大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：V351.35;TP183

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