Import / Export O/D in Korea: Survey

1
Phase II

• Import / Export O/D in Korea Survey

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DATA COLLECTION

• Containers cargo O/D data in 2001 between all of
  local seaports and the local areas were surveyed
  in 2003 2004 every 5 years
• Containers cargo QUANTITIES data in 2001 from
  the MOMAF
• Added the data of the Inchoen seaport survey in
  2004

Cargo

• Many kinds of COSTS are available in the website
  such as
• Global Enterprises, LTD _at_ http//www.global.co.kr/
  dis_index.html
• Hanjin Shipping _at_ http//www.hanjin.com/home/main.
  jsp
• Dongnama Shipping Co., LTD _at_ http//www.dnal.com/e
  ng/office/chg/chg_korea.jsp
• Etc.

Cost

• MOMAFs PORTMIS
• Transit time and variance real data of various
  combinations of modes are few available so far

Time and risk factor
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SURVEY I IN THE INCHEON SEAPORT IN 2004 2005
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TARGET IMPORT/EXPORT FULL-CONTAINER THROUGHPUT
IN S. KOREA
Survey II. Every 5 Years Whole Nation Survey
Data 2002-3
Cap Region. Seoul, Incheon, Kyounggi
Province C.C. Chungcheong Y.N. Youngnam H.N.
Honam
2001, Thousand TEU
Import Export
Transshipment
Costal
Total Domestic
9,990
Full
Empty
Import export
6,590
Cap Region
Y.N.
H.N.
C.C.
Full container
4,770
(32.2)

• We consider the 76 of total Capital Regional
  cargos, 1,170 thousands TEUs, in the pre-analysis
  since it is confined to five international trade
  areas as foreign O/Ds and three seaports as key
  logistic points in domestic as well as it
  excludes the cargo quantities by rail and costal
  transportation in order to increase efficiencies
  of analyses and easily to build the mathematical
  model for solving the problems. However, when we
  optimize the system later on, we will focus on
  the whole Capital Regional cargo amount
  regardless of the cargoes taken by truck,
  shipping or railway at present.

It includes 5.7 thousand TEU, that is, the
cargo of Kangwon province. Source Korea
Maritime Institute (2003.2) Korea Container
Terminal Authority
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GREAT INEFFICIENCY IN TRANSPORTATION SYSTEM
2001, Percent
Container throughput rates of the Cap. region by
the domestic seaports
Key findings
100 4,772,438 TEUs

• 73 of total cargo in/out the capital region is
  32.2 of total domestic cargo in/out S. Korea,
  flows through the Busan (mostly) and Kwangyang (a
  little) seaports, while the Incheon seaport,
  which is the nearest to the capital region,
  carried only 27 in/out.

Kyeong-Nam
Busan
Cheon-Buk
Cheon-Nam
Kyeong-Buk
Chung-Nam
Chung-Buk
Kang-Won
Capital Region
73
1,538 TEUs
Great inefficiency
Busan
Kwang- yang
Other
Incheon
Total
Source KMI
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CONTAINERS CARGO FLOWS OF THE CAPITAL REGION,
SOUTH KOREA IN 2001
Import Export
26.7 of total cargo
Incheon
Seoul
Pyeongtaek
Incheon
Seaports abroad
Capital Region
Seoul, Incheon, and 31 smaller counties and towns
Gunsan
Truck
Ulsan
China Europe U.S.A Japan S.E Asia (F.E Asia)
Rail 10.7 Coastal 2.3
Masan
Busan
Kwangyang
69.6 of total cargo
3.5 of total cargo
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Incheon (26.1), Pyeongtaek (0.6) Total
cargos of containers between seaports and the
current capital region is about 28 of the
national total cargo flows in/out Source KMI
(Korea Maritime Institute) DB in 2001
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CONTAINER THROUGHPUT BETWEEN THE CAPITAL REGION
AND THE FOREIGN O/D COUNTRIES
Thousand TEU, Percent, 2001
Key findings
By Busan port
Import
Export

• In case of the U.S. and Europe, most of all
  (about 92) containers were carried through the
  Busan seaport
• In Asia, some cargos (58) moved in/out by the
  Busan seaport and others (42) by the Incheon
  seaport.
• Especially, in case of China having the largest
  quantities (27.2 of total), the Busan seaport
  (58) were more used for transporting the
  container than the Incheon seaport (42) even if
  the Incheon seaports shipping distance from
  China is much less than the Busan seaport.
• In case of the Southeast Asia, the portion (55)
  of using the Inchoen seaport is a little higher
  than the Busan.

U.S.
Europe
China
Japan
S.E. Asia
Japan
China
By Incheon port
S.E. Asia
U.S.
U.S.
Europe
Europe
China
Japan
S.E. Asia
Import
Export
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It includes the cargo of a few Far East Asia
(Hong Kong, Mongol, etc.) in exporting portion.
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THE RESULTS FROM THE ANALYSIS OF CURRENT STATUS
(1/2)
Table 2.5 Results of current status (Billions
KRW, hhmmss)
Inland intermodal routings Inland intermodal routings Inland intermodal routings International intermodal routings International intermodal routings International intermodal routings
Cost Average Time Variability Cost Time Variability
Import 213.6 55948 n/a
Export 284.8 63510
Total 498.4 61928
These results are from analysis for about 1,170
thousand TEUs which were handled just in
road. This value of time means the
cargo-weighted average transportation time (per
TEU) for all traveled routes.
Table 2.6 Details of the current time analysis
between the metropolitan areas and seaports
Estimated current time for total container cargos Estimated current time for total container cargos Estimated current time for total container cargos Estimated current time for total container cargos
Only road Road Railway Coastal Total
Quantity (1,000 TEU) 1,170 1,332 169 37 1,538
Quantity (1,000 TEU) 100 () 86.6 () 11.0 () 2.4 () 100 ()
Total time 73981081515 87726543920 18971545059 12408263449 107938360524
Average time (per TEU) 6.32 (hours) (61928) 6.32 (hours) (61928) 11.22 (hours) (111250) 33.62 (hours) (333657) 7.74 (hours) (74400)
Total time ? (Unit time per route)
(quantities in TEU per route) Average time per
TEU Total time Quantity, for example, 61928
(73981081515) (1,170). This value is
the weighted average transportation time per TEU
for portions of cargo by transportation modes.
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THE RESULTS FROM THE ANALYSIS OF CURRENT STATUS
(2/2)
Millions USD
531
793
1,142
1,430
Table 2.7 Estimating inland transportation cost
2001 2004 2008 2011
Quantities (1,000 TEUs) Quantities (1,000 TEUs) 1,538 2,297 3,308 4,067
Cost (Billions KRW) Road (86.6) 567.3 847.2 1,220.4 1,500.2
Cost (Billions KRW) Rail (11) 53.5 79.9 115.0 141.4
Cost (Billions KRW) Coastal (2.4) 16.0 23.9 34.4 42.4
Cost (Billions KRW) Total 636.8 951.0 1,369.8 1,684.0
Total cost in Millions USD Total cost in Millions USD 530.7 792.5 1,141.5 1,430.3
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2001
2004
2008
2011
The Estimations of future quantities are
obtained by the interpolation method, and
furthermore we find the future costs under
assumption that the future ratios of container
cargo traffic by transportation mode will be
equal to those of year 200l.
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Phase II

• Build Model
• Model I Minimize Total Logistics Cost
• Model II Find Where SSS can be more
• competitive

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Phase II

• Model I
• Minimize Total Logistics Cost

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RESEARCH MOTIVATION
So, we have ...

• Begun to explore more diverse transportation
  network for the capital regions cargoes among
• Coastal shipping,
• Railway system through ICD,
• Truck and also air transportation

Our Vision ...

• Korean governments strategy
• To build a logistic hub of the Northeast Asia in
  Korea and
• To make a great deal of efforts

However, our questions are ...

• How to develop the logistics/transportation
  system or infrastructure?
• How to reduce the current logistics cost?
• How much can we reduce costs?
• Which is better choices among trans. modes and
  nodes as a key points?
• Uni- or Inter-modal?

Korea situation

• High logistics cost
• Inefficient transportation system

?
. . .

• Road and damage to major highways between Seoul
  and Pusan
• Environmental degradation,
• Inefficient infrastructure investment and
• Notably trucker drivers illegal and intentional
  strike

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RESEARCH PROPOSE
1
Current analysis

• We, first, analyze the current cargo flows and
  logistics costs analysis focused between the
  metropolitan area and seaports (including the
  import and export)

3
4
Suggesting the best node and route
Finding the Gap

• Third, we analyze the effect of cost-reduction
  through the comparison bet. current status and
  two (uni- or inter-modal) optimal cases

• Fourth, this study quantitatively suggests which
  seaports should be developed from the perspective
  of minimizing the logistics costs, time, and risk
  factor

2
Finding Optimal cases

• Second, the study finds not only optimal
  seaports location as one of key nodes, with
  minimum logistics costs, but also optimal routes

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BASIC MATHEMATICAL MODEL 0-1 INTEGER GP
FORMULATION
Fitness Function (Objective function)
Minimize (w1d1 w2d2 w3d3 )
Three Aim Constraints Conflicting objectives
(trade-off)
d1 0

1
COST
d2 0
2
TIME
d3 0
3
RISK FACTOR (or TIME-VARIANCE)
Other System Constraints
, for every route (i, j, k) or (k, j, i)
xj, ymjk, ymkj 0 or 1
, for every route (j, k)
, for every (k, j)
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GENETIC ALGORITHMS AS A SOLVING METHOD
GAs Process
Basis and Key characteristics

• Darwins basic principles Survival of the
  fittest
• An iterative, heuristic searching algorithms
• Developed by John Holland in 1970s
• Global Search ability (Multi-points search, not
  single point)
• Fitness function do not need linearity
• Deal with qualitative variables

Genetic operators
Reproduction

• Through reproduction, the GAs produces new
  generations of improved solutions by selecting
  parents with higher fitness ratings

Crossover

• Forms new offspring between two randomly selected
  good parents
• Operates by choosing a random position in the
  gene string of the parents and exchanging the
  segments

Mutation

• Operates randomly and infrequently by changing
  the gene string of the chromosomes
• To prevent found solutions from falling into
  local solution area

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Phase II

• Model II
• Find Where SSS can be
• more competitive

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Geographic and economic conditions for developing
SSS

• The goal is to attain the cheapest, fastest and
  most reliable transport conditions
• The effort for intermodality is increasing
  benefits and decreasing transshipment costs
• New handling techniques reduced costs, times and
  risks of transshipment

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Condition for SSS and critical threshold

• Different modes have a different competitiveness
  for different distance
• Terminal cost roadltrailltsea
• Line haul cost roadgtrailgtsea
• Such framework point out the condition for SSS
  competitiveness

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Illustrate the total cost of structure
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Mathematical definition for geographical condition

• (1) Only by road
• (2) By SSS
• Condition (1)gt(2)
• Where y is road distance between A and B, x is
  sea transfer distance between A and B

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Future Plan

• Future Collect Data
• - Customs Agency Data to analyze
  shipmentwise
• details (Sep. Oct. 2006)
• 2. Build Model (Oct. Nov. 2006)
• 3. Test Model (Nov. - Dec. 2006)

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