臺灣博碩士論文加值系統

定期航運亞洲 – 美國，亞洲 – 歐洲等主要航線貨櫃運量在過去幾年大幅增加，航商為達到規模經濟以降低單位運輸成本，建造大型貨櫃船投入各主要航線營運，造成主要航線靠港的改變與港口碼頭作業的瓶頸，然而建造港口貨櫃碼頭必須投入相當大的資本，因此如何提高碼頭貨櫃場的儲位利用率、裝卸機具生產力為重要的研究課題。

在貨櫃場作業中，出口貨櫃儲備、裝船作業因貨櫃、船舶資訊完整，已經有完善的電腦資訊系統可供使用；但在進口貨櫃的提櫃作業因無法確定貨主或拖車公司提櫃時間，貨主提櫃較屬於隨機的情況，提櫃時間無法有效掌控，無法預知其提櫃順序，因此在貨櫃場常產生許多翻櫃，增加提櫃等候的時間與無效率的翻櫃作業。

本研究主要目的為處理進口貨櫃隨機堆疊所造成的問題，第一步設計貨主提櫃預約系統，讓貨主可以預約提櫃時間，櫃場可根據提櫃時間預作堆疊；第二步是預測貨主提櫃的先後順序，因為大部分貨主可能不先作預約，對於這些貨主提櫃行為我們經由以往的資料分析，預測貨主提櫃的先後，整合這兩部份的貨主提櫃順序即可在船舶卸櫃後在貨櫃場預作較佳的分類堆疊。

Liner shipping provides regular services between specified ports according to timetables advertised in advance. Services in which a variety of goods are packed into standard-size containers that are transported by containerships are, in principle, open to all shippers and seem like public transport services. Containers came into the shipping market almost five decades ago. World trade, especially the Asia - US and Asia - Europe trade, has grown dramatically over the last decade. Container traffic has increased at a high rate as well. Liner carriers have responded by ordering more and much larger containerships. To achieve the economy of scale, the size of containerships has significantly increased during the last decade. The consequence of deploying larger containerships is that container terminal operations have become more bottlenecks so that it is essential to improve the productivity of various container handling operations. Also, because terminal construction requires a great amount of investment, it is important to efficiently utilize the resources of container terminals, especially stowage space and container handling equipments.

Port container terminals have been playing an important role in global transportation and serve as multi-modal interfaces between sea and land transportation. Import containers are those containers that arrive at container terminals from overseas and transship to their destination through inland transport, feeders, or wait for being picked up by the consignees. These arrivals are somewhat predictable to some extent. The departure time of import containers, however, is likely to be unpredictable. Therefore, this container stacking problem in a container yard can be considered to be more difficult than the stowage planning for container ship loading as there can be uncertainty about which container will be picked up before another. For import containers, this uncertainty exists because trucks arrive more or less randomly to pick up a specific container. As efficient use of limited storage space, nowadays stacking containers on the ground is most common in many port terminals.

To provide the maritime transportation with a good solution to improve the container terminal operations the design of a booking system for import container become a necessary tool in order to collect information in advance and in that way reduce the uncertainty of containers pick up time. Assigning categories based on the historical data and the booking system is possible to categorize containers and create a container stack model to assign the most suitable position of containers in a container block. This research has as main objectives the reduction of reshuffle of containers, optimal use of storage space, efficient and timely container delivery. Algorithm is design to show the idea and multivariate techniques are applied in order to prove that which variables are the decisive ones to stack containers.

Abstract (Chinese) i
Abstract ii
Acknowledgements iv
Contents vi
List of Tables ix
List of Figures x

Chapter

1 Introduction 1
1.1 Research motivation and background 1
1.1.1 Container terminal 2
1.1.2 Classification and storage in the container terminal 4
1.1.3 World container transportation 8
1.2 Scope of the research 10
1.3 Research purposes and justification 12
1.4 Research framework and overview of dissertation 15

2 Literature Review 18
2.1 Definitions, basic concepts and characteristics 18
2.2 Container terminal operations research 21
2.2.1 Container stacking 21
2.2.2 Outbound container 22
2.2.3 Inbound container 24
2.3 Multivariate analysis 26
2.3.1 Basic concepts of multivariate analysis 27
3 The Booking System for a Container Yard 31
3.1 Booking system stages and process 32
3.2 Booking system main features 35
3.2.1 Time windows/time slots 35
3.2.2 Time reservation 36
3.2.3 Time to booking 36
3.2.4 Canceling reservations 36
3.2.5 Processing reservations 37
3.2.6 Computer system requirements. 37
3.2.7 Early arrival of the outside trucks 37
3.2.8 Booking for trucks hauling long distance 37
3.2.9 Over booking a container 38
3.3 Container booking system customer implications 38
3.4 Container stacking using a booking system 40
3.4.1 Assumptions 41
3.4.2 Notations 42
3.4.3 Booking system algorithm 42
3.5 Similar experiences applying container booking system 47

4 Key Factors Influencing Time of Container Delivery 49
4.1 Key factors influencing the pick up time 49
4.1.1 Pick up time 49
4.1.2 Container type 50
4.1.3 Consignee 50
4.1.4 Kinds of cargo 50
4.1.5 Free time limit 50
4.2 Subcategorizing in priorities 51
4.3 Container terminal historical data 54
4.3.1 Data set variables 54
4.3.2 Data set gathering time 55
4.3.3 Data set sample 55

5 Statistical Analysis and Results 58
5.1 Group division by means 59
5.1.1 Consignee/days 59
5.1.2 Cargo/days 63
5.2 Ranking by 2 factors days/amount of containers 64
5.2.1 Consignee ranking 65
5.2.2 Kind of cargo ranking 69
5.3 Discriminant function analysis 73
5.3.1 Rules and equation results 75
5.3.2 Setting priorities with Z scores 77

6 Conclusions and Recommendations 79
6.1 Conclusions 80
6.2 Recommendations 82

Bibliography 84

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