本文選題：集裝箱碼頭　切入點：人工蜂群算法　出處：《大連海事大學(xué)》2016年碩士論文

【摘要】：全球經(jīng)濟(jì)貿(mào)易的飛速發(fā)展,帶來了物流業(yè)務(wù)量的急劇增長,據(jù)統(tǒng)計90%以上的國際貿(mào)易貨物都要經(jīng)過港口的中轉(zhuǎn)運輸,這之中的大部分都要通過集裝箱運輸來實現(xiàn)的。集裝箱碼頭為了提高自身的經(jīng)濟(jì)效益,需要充分利用碼頭的各種資源設(shè)備,其中承擔(dān)大部分碼頭水平運輸任務(wù)的集裝箱卡車的作業(yè)組織工作更是影響碼頭整體效率的關(guān)鍵技術(shù)之一。集卡路徑優(yōu)化問題是集卡作業(yè)組織中的一個重要組成部分。該問題是一個典型的復(fù)雜組合優(yōu)化問題,對其進(jìn)行優(yōu)化求解,可有效提高集裝箱碼頭集卡的作業(yè)效率,并降低集卡作業(yè)運營的成本,從而有利于碼頭整體經(jīng)濟(jì)效益的提升。本文針對集卡的路徑優(yōu)化問題,分析對比兩種集卡作業(yè)模式,建立了面向"作業(yè)面"的基于成本的集卡路徑優(yōu)化模型。前人對集卡路徑問題的求解多懫用數(shù)學(xué)規(guī)劃等方法,存在著局部收斂、魯棒性差等缺陷。本文采用較為新穎的人工蜂群算法(ABC)來對模型進(jìn)行求解,期待獲得較好的結(jié)果。人工蜂群算法具有結(jié)構(gòu)簡單,容易實現(xiàn)等優(yōu)點,但作為群智能算法的一種,也存在易早熟、收斂速度慢等缺點,并且在以往的研究和應(yīng)用中,蜂群算法多用于連續(xù)問題的求解。為了更好地解決上述屬于離散規(guī)劃的集卡路徑優(yōu)化問題,本文對基本蜂群算法進(jìn)行了相關(guān)改進(jìn),提出一種自適應(yīng)遺傳蜂群算法(AGA-ABC)。其主要思想是,將遺傳算法中的交叉和變異算子引入基本蜂群算法,對其加以改造,使得蜂群算法適用于求解離散優(yōu)化問題。同時引入自適應(yīng)因子,使算法在早期能有效避免早熟而后期又能加快算法向全局最優(yōu)處收斂,從而提高算法的整體性能。為驗證提出算法的性能,采用該算法對不同規(guī)模的經(jīng)典TSP問題進(jìn)行了測試與求解,所得結(jié)果驗證了其可行性和優(yōu)越性。進(jìn)一步,將所提算法應(yīng)用到建立的集卡路徑優(yōu)化模型中,分別在進(jìn)口、出口雙船舶到港和進(jìn)出口單船舶到港,兩種工況下進(jìn)行了仿真測試,并對優(yōu)化結(jié)果進(jìn)行了相關(guān)分析。結(jié)果表明,提出的算法能在滿足各種性能約束的前提下,有效的對集卡行駛路徑進(jìn)行優(yōu)化,得到上述問題的令人滿意的工程優(yōu)化結(jié)果。工作表明,提出的算法對集卡路徑優(yōu)化問題是有效的,可以得到較優(yōu)越的優(yōu)化結(jié)果,以作為實際集卡作業(yè)組織的參考。本文的研究具有一定的理論意義和應(yīng)用價值。
[Abstract]:The rapid development of the global economy and trade, bring the rapid growth of the logistics business volume, according to statistics, more than 90% of international trade in goods must pass through the port of transshipment, the majority must realize through the container transport container terminal. In order to improve their economic benefits, to make full use of various resources equipment terminal. The container truck transport task level wharf to bear most of the operation organization work is one of the key technologies affecting the efficiency of the wharf. The whole truck path optimization problem is an important part of the truck operation organization. This problem is a typical combinatorial optimization problem, for solving it, can effectively improve the container terminal the optimal operation efficiency, and reduce the truck operation cost of operation, which is conducive to the overall economic efficiency improved. Aiming at the truck dock The path optimization problem, comparison and analysis of two kinds of truck operation mode, for the establishment of "operation" truck path optimization model based on cost. To solve the problem of the previous truck path multi Zhi by mathematical programming method, the existence of local convergence, defects and poor robustness. This paper uses artificial bee colony algorithm is novel (ABC) to solve the model, to obtain better results. The artificial bee colony algorithm has the advantages of simple structure, easy to implement, but as a kind of swarm intelligence algorithm, is also easy to premature and slow convergence speed and other shortcomings, and in the previous research and applications, bee colony algorithm usually used to solve continuous problems in order to better solve the above belongs to the discrete planning path optimization of container trucks, some improvements on the basic bee colony algorithm, proposed an adaptive genetic ant colony algorithm (AGA-ABC). The main idea is to, The genetic algorithm crossover and mutation operator into the basic ABC algorithm to transform it, makes the colony algorithm suitable for solving discrete optimization problems. At the same time, the introduction of adaptive factor, so that the algorithm can effectively avoid the premature convergence and the later stage can accelerate the convergence to the global optimum in the early stage, so as to improve the overall performance of the algorithm. The proposed algorithm in order to verify the performance of the classical TSP problem, the different scale of the algorithm is tested and solved, and the results verify the feasibility and superiority. Further, the proposed algorithm is applied to establish the truck path optimization model, respectively in the import, export and import and export to Hong Kong ship double single ship to Hong Kong. Two conditions to carry out the simulation test, and the optimization results are analyzed. The results show that the proposed algorithm can satisfy the performance constraints in the premise, effective for truck driving Path optimization, the problem of satisfactory optimization results. Engineering work shows that the proposed algorithm is effective for path optimization of container trucks, can get better optimization results, as the actual truck operation organization. This study has a certain theoretical significance and application value.

【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：U691.3;TP18

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