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研究生:林承顥
研究生(外文):Lin, Cheng-Hao
論文名稱:運用基因演算法於模糊需求下空貨櫃存貨系統
論文名稱(外文):Empty Container Inventory System with Fuzzy Demand by Genetic Algorithm
指導教授:蘇健民蘇健民引用關係
指導教授(外文):Su, Chien-Min
口試委員:鍾玉科方信雄趙延丁蘇健民楊明峯
口試委員(外文):Chung, Yu-koFang, Hsin-HsiungChao, Yen-TingSu, Chien-MinYang, Ming-Feng
口試日期:2019-07-04
學位類別:碩士
校院名稱:國立臺灣海洋大學
系所名稱:運輸科學系
學門:運輸服務學門
學類:運輸管理學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:中文
論文頁數:51
中文關鍵詞:貿易不平衡空櫃調度存貨模型模糊理論基因演算法
外文關鍵詞:trade imblanceempty container dispatchinventory modelfuzzy theorygenetic algorithm
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在全球化的風潮下,國際貿易發展越來越快速,每個人在生活之中隨時都可能會接觸到國際貿易所帶來的便利。在國際貿易發展下,海運靠著低廉的成本以及龐大的貨運量成為貿易重要且不可或缺的手段之一。但由於各港口之間吞吐量的差異,導致貿易不平衡的情況發生。在出口量小於進口量的港口,會造成空貨櫃過多,衍生出貨櫃場站的貨櫃堆積問題;但在出口量大於進口量的港口,則會產生缺少貨櫃的問題,嚴重時則會造成貿易上的困難,影響到國際貿易的進行。
本研究透過空櫃調度的成本估算存貨模型來計算為了避免缺少貨櫃的問題,所需要耗費的空櫃調度總成本,在成本上考量了三種不同的貨櫃補充方法,分別對應到三種不同的成本,包括貨櫃持有成本、貨櫃訂購成本以及貨櫃租賃成本。此外,在本模型中加入了模糊理論,將需求假設成模糊需求數,決策者需要依據不同環境狀況設定範圍上下限進行成本估算,能幫助決策者更精準地估算總調度成本值。另一方面,本文另外為求取最適解加入基因演算法,與一般成本模型進行比較分析,探討在不同計算方式之下,各項演算法的差異與優缺點。最後,希望此成本模型可以提供給海運業者在業界進行使用,提供給決策者參考依據,幫助業界降低貨櫃調度成本。
Under the trend of globalization, the development of international trade is also becoming faster increasingly. Everyone in life may be exposed to the convenience brought by international trade. Under the development of international trade, shipping is one of the important and indispensable means of trade by means of low cost and enormous cargo volume. However, due to the difference in throughput between ports, trade imbalances have occurred. In ports with less export volume than imports, there will be too many empty containers, and the problem of container accumulation at the decentralized container yards; however, in ports with a larger export volume than imports, there will be a shortage of containers. In severe cases, it will cause trade difficulties and affect the operation of international trade.
In this research, through the cost estimation model of container dispatching, the total empty container dispatching cost is calculated in order to avoid the shortage of containers. In the model, by the addition of the fuzzy theory, we assume the fuzzy demand as the triangular fuzzy number, the decision maker needs to set the upper and lower limits of the different environmental conditions for cost estimation as well. On the other hand, this research adds a genetic algorithm to obtain the optimal solution, and compares it with the general cost model to explore the differences, advantages and disadvantages of each algorithm under different calculation methods. Finally, it is hoped that this cost model can be provided to the shipping operators for use in the industry, providing decision makers with reference basis to help the industry reduce container dispatching cost.
中文摘要………..……………………………………………………………………...I
Abstract………..………………………………………………………………………II
Contents………..…………………………………………………………………..…III
List of Figures………..…………………………………………………………...….IV
List of Tables………..………………………………………………………………...V
Chapter 1 Introduction………..……………………………………………………….1
1.1 Research background and motivation………..………………………………1
1.2 Research purpose………..……………………………………………………1
1.3 Research Process………..……………………………………………………2
Chapter 2 Literature Review………..…………………………………………………5
2.1 Empty container dispatch………..…………………………………………...5
2.2 Inventory model………..…………………………………………………….7
2.3 Fuzzy inventory model………..……………………………………………...8
2.4 Genetic algorithm………..………………………………………………….10
Chapter 3 Basic Model………..…………………………………………………...…16
3.1 Basic notations………..…………………………………………………….16
3.2 Basic assumptions………..…………………………………………………17
3.3 Basic modeling building………..…………………………………………...18
3.3.1 Objective function………..………………………………………….18
3.3.2 Constraints………..………………………………………………….18
3.4 The solution of basic model………..……………………...………………..23
Chapter 4 Fuzzy model………..…………………………………………….……….24
4.1 Fuzzy notations………..…………….……………………………………...24
4.2 Leasing strategy………..…………………….…………..………………….25
4.3 Fuzzy demand………..……………..………………………………………25
4.4 Fitness function of genetic algorithm……………………………………….28
Chapter 5 Numerical Analysis………..………………………………………...…….30
5.1 Parameters setting………..…………………………………………..……..30
5.2 Analysis of fuzzy model result………..…………………………..………...31
5.3 Sensitive analysis………..………………………………………………….34
5.4 Analysis of Genetic algorithm result………..…………...………………….40
Chapter 6 Conclusions……………………………………...……..…………………44
6.1 Conclusions………..………………………………………………………..44
6.2 Suggestions………..………………………………………………………...45
6.3 Future research………..…………………………………………………….46
Reference………..……………………………………………………………………47
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