本文選題：集裝箱碼頭　切入點：堆場　出處：《大連海事大學》2017年碩士論文

【摘要】：隨著全球經(jīng)濟的快速發(fā)展,國際和區(qū)域間的貨運貿(mào)易越來越頻繁,集裝箱吞吐量需求日益增加,從而導致碼頭堆場的空間資源與集裝箱吞吐量的矛盾也不斷加劇。由于拓展港口碼頭的空間資源耗時耗力并且需要花費大量資金,所以提高堆場現(xiàn)有空間資源利用率已成為解決該矛盾的一種經(jīng)濟而有效的手段。出口箱堆存箱位的分配直接影響著船舶停港時間、裝卸船效率乃至整個港口生產(chǎn)效率的高低。故而在有限的空間資源下,研究出口箱箱位分配問題已經(jīng)成為港航運輸和自動化集裝箱碼頭等相關(guān)領(lǐng)域的研究熱點問題之一。本文通過優(yōu)化出口箱在堆場中的堆存箱位,達到充分利用堆場空間資源的目的。根據(jù)堆場出口箱箱位分配的基本原則,在不同目的港和重量等級混合堆存模式下,考慮總翻箱量,集裝箱均衡分配和運輸距離等問題,以及其他相關(guān)約束條件,建立了堆場出口箱箱位優(yōu)化分配問題的數(shù)學模型。因該問題是復(fù)雜離散組合優(yōu)化問題,具有NP難度,很難用傳統(tǒng)方法進行求解。本文采用較為新穎的螢火蟲算法進行求解,為了避免算法過早陷入局部最優(yōu),同時為了增加種群的多樣性、加快收斂速度,提出一種并行自適應(yīng)螢火蟲算法(PAFA)。其主要思想是基于離散問題的實質(zhì)和螢火蟲算法的基本原理,引入遺傳算法的交叉、變異算子,先將標準螢火蟲算法進行離散化處理,對個體間的距離等進行重新定義,使其適用于離散問題的求解;進而基于自適應(yīng)的思想,將位置更新改造為自適應(yīng)的位置更新方式;同時采用并行策略,將種群分成兩個子群體,一個子群體側(cè)重向最優(yōu)個體的學習以加快收斂,另一個子群體在算法不同時期兼顧搜索和開發(fā),兩者定期進行信息交流,以提高算法的整體性能。文中通過標準TSPLIB測試庫中兩個旅行商問題對所提算法進行了測試與驗證,結(jié)果表明了其解決離散組合優(yōu)化問題的可行性和有效性。進一步,以大連大窯灣集裝箱港區(qū)相關(guān)航線的堆場出口箱箱位分配問題為工程背景,將提出算法應(yīng)用到前述的數(shù)學模型之中,對兩種簡化工況下的箱位分配問題進行了仿真與測試。結(jié)果表明,本文所提算法對求解該問題有效,給出了較好的箱位分配方案,能夠提高堆場空間資源利用率,且降低了作業(yè)和運輸成本。本文的研究工作為后續(xù)深入研究箱位分配問題,乃至所提算法的工程實用化提供了啟發(fā)和借鑒,具有一定的理論意義和應(yīng)用價值。
[Abstract]:With the rapid development of the global economy, international and interregional freight trade is becoming more and more frequent, and the demand for container throughput is increasing day by day. As a result, the contradiction between the space resources of the terminal yard and the throughput of the container is becoming more and more serious, because the expansion of the space resources of the port terminal is time-consuming and time-consuming and requires a lot of money. Therefore, increasing the utilization rate of existing space resources in the yard has become an economic and effective means to solve this contradiction. The efficiency of loading and unloading ships and even the production efficiency of the whole port. Therefore, in the limited space resources, The research on the allocation of export container space has become one of the hot issues in the related fields, such as port and shipping transportation and automatic container terminal, etc. In this paper, by optimizing the storage space of export container in the yard, In order to make full use of the space resources of the yard, according to the basic principle of the allocation of the container space at the outlet of the yard, under the mixed storage mode of different destination ports and weight classes, the problems of the total turnover volume, the balanced distribution of the containers and the transportation distance are considered. The mathematical model of the optimal allocation of the box space at the exit of the yard is established, which is a complex discrete combinatorial optimization problem with NP difficulty. In this paper, a novel firefly algorithm is used to solve the problem, in order to avoid prematurely falling into local optimum, to increase population diversity and to speed up convergence. A parallel adaptive firefly algorithm (PAFAA) is proposed, which is based on the essence of the discrete problem and the basic principle of the firefly algorithm. The crossover and mutation operators of genetic algorithm are introduced to discretize the standard firefly algorithm. The distance between individuals is redefined to make it suitable for solving discrete problems. Then, based on the adaptive idea, the position update is transformed into an adaptive position updating method. At the same time, the parallel strategy is adopted. The population is divided into two subpopulations, one focusing on learning from the optimal individual to accelerate convergence, the other taking into account search and development at different stages of the algorithm, where information is exchanged on a regular basis. In order to improve the overall performance of the algorithm, the proposed algorithm is tested and verified by two traveling salesman problems in the standard TSPLIB test library. The results show that the proposed algorithm is feasible and effective in solving discrete combinatorial optimization problems. Based on the problem of allocation of export container space in Dalian Dayaowan container port area, the proposed algorithm is applied to the above mathematical model. The simulation and test of the box allocation problem under two simplified working conditions are carried out. The results show that the algorithm proposed in this paper is effective in solving the problem, and a better box allocation scheme is given, which can improve the utilization ratio of space resources in the storage yard. The research work in this paper provides inspiration and reference for the further study of box allocation and even the practical application of the proposed algorithm, which has a certain theoretical significance and application value.
【學位授予單位】：大連海事大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U691.3;TP18

【參考文獻】

相關(guān)期刊論文 前10條

1 方必武;王波;劉滌塵;羅金號;馬恒瑞;陳思遠;;基于搜索+調(diào)整的兩階段螢火蟲算法求解機組組合問題[J];電力系統(tǒng)保護與控制;2016年23期

2 范厚明;姚茜;馬夢知;;多場橋分區(qū)域平衡策劃下的集裝箱堆場箱位分配問題[J];控制與決策;2016年09期

3 董文波;周康;劉朔;高全勝;;基于多目標VRP的離散型螢火蟲算法研究[J];武漢輕工大學學報;2016年02期

4 劉嬋娟;胡志華;;集裝箱堆場出口箱箱位分配多目標優(yōu)化模型[J];大連理工大學學報;2015年06期

5 劉鵬;劉弘;鄭向偉;丁艷輝;;基于改進螢火蟲算法的動態(tài)自動聚集路徑規(guī)劃方法[J];計算機應(yīng)用研究;2011年11期

6 劉長平;葉春明;;一種新穎的仿生群智能優(yōu)化算法:螢火蟲算法[J];計算機應(yīng)用研究;2011年09期

7 衛(wèi)家駿;;出口集裝箱堆場位置的優(yōu)化[J];重慶交通大學學報(自然科學版);2010年03期

8 劉艷;周鵬飛;康海貴;;集裝箱堆場箱位分配模糊優(yōu)化研究[J];水運工程;2009年11期

9 徐亞;陳秋雙;龍磊;楊立志;劉麗蕓;;集裝箱倒箱問題的啟發(fā)式算法研究[J];系統(tǒng)仿真學報;2008年14期

10 高鵬;金淳;韓慶平;;提箱作業(yè)優(yōu)化問題的嵌套啟發(fā)式算法[J];系統(tǒng)管理學報;2008年02期

，

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