本文關鍵詞： 最小代價路徑 路徑規(guī)劃 頻繁路徑 頻繁模式 數(shù)據(jù)挖掘　出處：《廣西大學》2014年碩士論文　論文類型：學位論文

【摘要】：隨著物聯(lián)網(wǎng)的普及,物聯(lián)網(wǎng)逐漸應用于眾多領域,其中之一是物流領域。在物流領域中路徑規(guī)劃直接影響著物流成本與物流過程中的時間消耗。由于物流中的物品本身帶有實時的時空信息,這使得實時的路徑規(guī)劃與從物流數(shù)據(jù)中挖掘頻繁路徑成為可能。本文工作主要體現(xiàn)在兩個方面。 第一,針對基于物聯(lián)網(wǎng)的物流網(wǎng)絡中路徑代價的時變性,建立了一種時間依賴的物流網(wǎng)絡模型,并在此基礎上研究了實時的物流路徑規(guī)劃問題。針對現(xiàn)實中預測準確性隨時間推移而降低,以及在預知程度較低時無法獲得較好的路徑規(guī)劃等問題,提出了一種帶弧代價預知程度參數(shù)的最小時間路徑算法SWPL以及基于SWPL實時的逐步規(guī)劃的解決方案。該算法考慮了預測的精度問題,在傳統(tǒng)Di jkstra算法的基礎上引入了一種與時間相關的弧代價計算方法。實驗表明在預知程度較高與在預知程度較低但采用逐步規(guī)劃的解決方案都能取得良好的路徑規(guī)劃效果。 第二,在基于物聯(lián)網(wǎng)的物流中,會產(chǎn)生海量蘊含時空信息的物品移動數(shù)據(jù)。這些數(shù)據(jù)中包含著很多有助于提高物流科學管理的知識,目前從這些數(shù)據(jù)中找出這些有用的知識采用的主要是數(shù)據(jù)挖掘技術。其中頻繁路徑作為反映物流特征的重要知識之一,可為優(yōu)化物流的路徑規(guī)劃、研究物流的變化規(guī)律等提供重要的參考信息。頻繁路徑的獲取是通過頻繁序列模式挖掘算法,本文根據(jù)物流網(wǎng)絡及物流的特征設計了一種充分考慮物流網(wǎng)絡拓撲信息的頻繁路徑序列挖掘算法PMWTI。在該算法中引入了代價容忍度剪枝法,用于候選路徑序列的深度剪枝,以去除部分不可能是頻繁路徑序列的候選路徑序列,在一定程度上降低了候選路徑序列規(guī)模。實驗表明,相比沒有采用該剪枝方法的同等算法,PMWTI的頻繁路徑挖掘效率更高。 本文工作可為科學的物流管理提供參考,提出的方法可用于物流中實時的路徑規(guī)劃、物流路由的優(yōu)化、物流規(guī)律的發(fā)現(xiàn)等。
[Abstract]:With the popularity of the Internet of things, the Internet of things is gradually used in many fields, One of them is the field of logistics. In the field of logistics, path planning has a direct impact on logistics costs and time consumption in the process of logistics. This makes real-time path planning and mining frequent paths from logistics data possible. First, a time-dependent logistics network model is established for the time-variant of path cost in the logistics network based on the Internet of things. On this basis, the real time logistics path planning problem is studied. Aiming at the problem that the prediction accuracy decreases with time in reality, and the better path planning can not be obtained when the degree of prediction is low, and so on. In this paper, a minimum time path algorithm (SWPL) with arc-cost predictive degree parameter and a solution of step by step programming based on SWPL are proposed. The prediction accuracy is considered in this algorithm. Based on the traditional Di jkstra algorithm, a time-dependent arc cost calculation method is introduced. The experimental results show that good path planning results can be achieved in the solutions with higher prediction degree and lower prediction degree but with gradual planning. Second, in Internet-based logistics, there are massive amounts of goods moving data that contain space-time information. These data contain a lot of knowledge that can help improve the scientific management of logistics. At present, data mining technology is the main way to find the useful knowledge from these data, among which frequent paths, as one of the important knowledge reflecting logistics characteristics, can be used to optimize the path planning of logistics. Research on the changing rules of logistics provides important reference information. The acquisition of frequent paths is based on frequent sequential pattern mining algorithm. According to the characteristics of logistics network and logistics, this paper designs a frequent path sequence mining algorithm PMWTI, which fully considers the topological information of logistics network. In this algorithm, the cost tolerance pruning method is introduced for the deep pruning of candidate path sequences. In order to remove part of the candidate path sequence which is impossible to be a candidate path sequence, the size of candidate path sequence is reduced to a certain extent. Experiments show that the efficiency of frequent path mining is higher than that of PMWTI algorithm without this pruning method. This paper can provide reference for scientific logistics management, and the proposed method can be used in real-time path planning, optimization of logistics routing, discovery of logistics rules and so on.
【學位授予單位】：廣西大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP391.44;TN929.5;TP311.13

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