本文選題：隨機(jī)車輛路徑 + 軍事物流配送��； 參考：《西華大學(xué)》2017年碩士論文

【摘要】：隨著信息化戰(zhàn)爭時(shí)代的到來,軍隊(duì)對(duì)高效后勤運(yùn)輸保障的需求更加迫切。近幾場(chǎng)高技術(shù)條件下的局部戰(zhàn)爭表明,沒有高效、快速、強(qiáng)有力的運(yùn)輸保障能力,就難以在戰(zhàn)爭中取得主動(dòng)權(quán),就難以做到克敵制勝。軍隊(duì)的物流配送運(yùn)輸是軍事運(yùn)輸保障的重要組成部分,擔(dān)負(fù)著為分布在不同地點(diǎn)的部隊(duì)輸送給養(yǎng)物資、武器裝備等的重要職責(zé)。然而,目前軍隊(duì)的物流配送依然依靠決策者的經(jīng)驗(yàn)安排,配送形式依然采用單純的點(diǎn)對(duì)點(diǎn)的形式,具有較大的任意性和不科學(xué)性,造成了極大的運(yùn)力資源浪費(fèi)。此外,在戰(zhàn)爭等應(yīng)急情況下,運(yùn)輸時(shí)間的不確定對(duì)配送路徑的影響巨大,不合理的運(yùn)輸安排可能導(dǎo)致軍事行動(dòng)的遲滯甚至整個(gè)軍事活動(dòng)的失敗。因此,為了避免軍事運(yùn)力資源的浪費(fèi),為了在應(yīng)急狀態(tài)下更好地完成軍事物流配送運(yùn)輸任務(wù),對(duì)軍事物流配送運(yùn)輸進(jìn)行研究,很有必要。本文首先對(duì)隨機(jī)車輛路徑問題進(jìn)行了介紹并通過分析對(duì)比精確算法、傳統(tǒng)啟發(fā)式算法和亞啟發(fā)式算法的優(yōu)缺點(diǎn),決定了使用蟻群算法作為本文的求解算法。其次,本文通過挖掘軍事物流配送的含義、特征、分類等對(duì)軍事物流配送運(yùn)輸問題的類型進(jìn)行了解析,從而得到了建模思路。進(jìn)而,結(jié)合車輛路徑問題思想對(duì)平時(shí)軍事物流配送運(yùn)輸問題建立了模型,同時(shí)利用隨機(jī)機(jī)會(huì)約束規(guī)劃和悲觀時(shí)間理論建立了應(yīng)急狀態(tài)下關(guān)于隨機(jī)運(yùn)輸時(shí)間的軍事物流配送運(yùn)輸模型。然后,根據(jù)所建模型選用蟻群算法對(duì)問題進(jìn)行求解,使用了基于遺傳算法解碼思想的求解車輛路徑問題的蟻群算法,并根據(jù)所分析的蟻群算法優(yōu)缺點(diǎn)做出了狀態(tài)轉(zhuǎn)移公式以及信息素更新方式的針對(duì)性改進(jìn)。最后,通過MATLAB編程實(shí)現(xiàn)算法,結(jié)合文獻(xiàn)算例,對(duì)平時(shí)軍事物流配送運(yùn)輸模型進(jìn)行了求解,并通過得出的結(jié)果與文獻(xiàn)中結(jié)果進(jìn)行對(duì)比,證明了算法是有效和可行的,同時(shí),利用該算法求解了應(yīng)急狀態(tài)下的軍事物流配送運(yùn)輸模型,得到了優(yōu)化路徑。因此,本文方法能夠在軍隊(duì)制定應(yīng)急物流配送運(yùn)輸預(yù)案時(shí)以及在對(duì)應(yīng)急狀態(tài)下軍事物流配送運(yùn)輸車輛路徑進(jìn)行規(guī)劃時(shí)提供一定的參考。
[Abstract]:With the arrival of the information war era, the military demand for efficient logistics transportation support is more urgent. Recent local wars under high-tech conditions have shown that without efficient, fast, and powerful transportation support, it is difficult to gain initiative in the war and to defeat the enemy. The logistics distribution and transportation of the army is an important part of the military transportation support, which is responsible for transporting supplies, weapons and equipment for the troops distributed in different places. However, at present, the logistics distribution of the army still depends on the experience arrangement of the decision makers, and the distribution form is still in the form of simple point-to-point, which is arbitrary and unscientific, resulting in a great waste of transportation resources. In addition, under the emergency situation such as war, the uncertain transportation time has a great influence on the distribution route, and unreasonable transportation arrangement may lead to the delay of military action and even the failure of the whole military activity. Therefore, in order to avoid the waste of military transportation resources, in order to complete the task of military logistics distribution transportation better under the emergency condition, it is necessary to study the military logistics distribution transportation. This paper first introduces the stochastic vehicle routing problem and analyzes and compares the advantages and disadvantages of accurate algorithm, traditional heuristic algorithm and sub-heuristic algorithm, which determines the use of ant colony algorithm as the solution algorithm in this paper. Secondly, this paper analyzes the types of military logistics transportation problems by excavating the meaning, characteristics and classification of military logistics distribution, and then gets the modeling ideas. Furthermore, combining the idea of vehicle routing problem, the paper establishes a model of military logistics distribution transportation problem in peacetime. At the same time, by using stochastic opportunity constraint programming and pessimistic time theory, the military logistics transportation model about stochastic transportation time in emergency state is established. Then, according to the model, ant colony algorithm is selected to solve the problem, and the ant colony algorithm based on genetic algorithm decoding idea is used to solve the vehicle routing problem. According to the advantages and disadvantages of the ant colony algorithm, the state transfer formula and pheromone updating method are improved. Finally, the algorithm is realized by MATLAB programming, and the model of military logistics and transportation in peacetime is solved with the example of literature. The results obtained are compared with the results in the literature, and it is proved that the algorithm is effective and feasible, at the same time, The algorithm is used to solve the military logistics transportation model under the emergency state, and the optimized path is obtained. Therefore, this method can provide a certain reference when the military formulates the emergency logistics delivery transportation plan and plans the military logistics distribution transportation vehicle route under the emergency state.
【學(xué)位授予單位】：西華大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：E91;E234

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