Transcript Discrete - Event Simulation of Manufacturing Processes

SIMULATION SYSTEM
OF THE BALTIC CONTAINER
TERMINAL
Yuri Merkuryev and Vladimir Bardachenko
Department of Modelling and Simulation
Riga Technical University
Riga, Latvia
IST4Balt Workshop
April 6, 2005
Prof. Yuri Merkuryev
Baltic Container Terminal:
• fast growing container terminal
•
•
•
•
operates at the Riga Free Port
about 150 000 TEU per year
76 hectares, 420 m. long berth
terminal resources:
- a container quay (berth), operated by 3 quay cranes
- 20’ container import and export yards operated by
forklifts
- 40’ container import and export yards operated by
2 RMGs each
- internal transport to carry containers within the
territory of the BCT (trucks)
IST4Balt Workshop
April 6, 2005
Prof. Yuri Merkuryev
IST4Balt Workshop
April 6, 2005
Prof. Yuri Merkuryev
Material Flows:
Container
berth
Import cont.
storage
Freight
station
Export cont.
storage
Empty cont.
storage
In/Out
gate
Gen. cargo
storage
Railway
centre
Container flow
General cargo flow
IST4Balt Workshop
April 6, 2005
Prof. Yuri Merkuryev
Queuing Network Model:
Quay Cranes
Trucks
Yard Cranes
40’ Import
yard
Container
quay
40’ Export
yard
Railway Cranes
Railway
station
Forklifts
Documentation
Processing
20’ Import
yard
In/Out
gate
IST4Balt Workshop
20’ Export
yard
April 6, 2005
Prof. Yuri Merkuryev
Pool of Resources (processing 40’ containers):
- 3 quay cranes QC1, QC2, QC3
- 4 teams of trucks (with 3, 4, 5 and 6 identical trucks)
TT3, TT4, TT5, TT6
- 3 types of yard cranes YC1, YC2, YC3
- 36 sets of resources QCi-TTj-YCk
Each resource with its own initial cost, productivity (cycle
time), and depreciation period.
IST4Balt Workshop
April 6, 2005
Prof. Yuri Merkuryev
Quay cranes
QC
QC1
Truck workgroups
Forklifters
Tr_3
FL_1
20’ Yards
20’ Outbound
Vessel
QC2
Tr_4
FL_2
Yard2
20’ Inbound
Yard2
QC3
Tr_5
FL_3
Tr_6
40 Yards
40" Yard Cranes
YC1
40’ Outbound Yard2
YC2
40’ Inbound Yard2
YC3
IST4Balt Workshop
April 6, 2005
Prof. Yuri Merkuryev
QC
IST4Balt Workshop
Truck
April 6, 2005
YC
Prof. Yuri Merkuryev
Simulation of BCT operation:
Aims:
- Forecast: Evaluation of performance indicators
(productivity, economical efficiency, etc.) in a given situation
- What-if analysis: Analysis of operation in different
situations (e.g., for planning next-period operation)
- Optimisation: Searching for an optimal set of resources
(e.g., planning modernisation of resources)
- Visualisation: Presenting visually terminal operation (e.g.,
for marketing)
Tool: Arena Simulation System
IST4Balt Workshop
April 6, 2005
Prof. Yuri Merkuryev
Introducing the micro-level model
RESOURCES AND OPERATIONS
Figure portrays the structural scheme of resource
types necessary for vessel loading and discharge
processes
Hatch Covers
Restow
QC
Restow
Ship at Berth
20'- 40'
Import
and Export
containers
20' Import containers
QC
Tr
FL
20' Imp Yard 1
Loading
Hatch Covers
QC
Hatch
Covers
Restow
QC
Restow
Ship at Berth
40' Export containers
40' Export Yard 6
YC
Tr
QC
FL
Tr
QC
40' Import containers
20' Export containers
QC
IST4Balt Workshop
Tr
YC
40' Import Yard2
April 6, 2005
20' Exp Yard 5
Ship
at
Berth1
QC
Ship
at
Berth1
Hatch
Covers
Berth 1
Discharge
20'- 40'
Import
and Export
containers
Prof. Yuri Merkuryev
Simulation by Arena of some BCT micro-operations
M AERSK
M AERSK
M AERSK
M AERSK
M AERSK
M AERSK
M AERSK
M AERSK
M AERSK
M AERSK
M AERSK
M AERSK
M AERSK
M AERSK
M AERSK
40 ft c ontai ne rs
on board
Li fti ng c t off
bo ard
M
A
M
M
A
E
A
E
R
E
R
S
R
S
W aiting for truck
True
Lo adi ng on to
tru c k
Truc k ?
K
S
K
K
M
A
E
R
S
K
M
A
E
R
S
K
Th e QC
ap proac h es
v e s s el
Truc k
ap proac h es QC
4 0 ft c o n ta i n e rs
o n b o a rd
Fa l s e
L i fti n g c t o ff
b o a rd
M
M
A
M
A
E
E
A
R
R
E
S
S
R
Waiting for truc k
L o a d i n g o n to
tru c k
QCra n e ?
Ca rry i n g c t
K
K
S
K
M
A
E
R
S
K
M
A
E
R
S
K
Tru e
Th e QC
a p p ro a c h e s v e s s e l
Truc k waiting for
YC
YC d i s c h a rg i n g
tru c k
Tru c k ?
Tru e
Ya rd c ra n e
a p p ro a c h i n g
Tru c k a p p ro a c h e s
QC
W a y b a c k ti m e
IST4Balt Workshop
April 6, 2005
Prof. Yuri Merkuryev
Example of sub-model of basic technological chains of
container import chain and export chain
IST4Balt Workshop
April 6, 2005
Prof. Yuri Merkuryev
implementation
IST4Balt Workshop
April 6, 2005
Prof. Yuri Merkuryev
Example of processes parameters monitoring with
accuracy up to 1 second during discharge in BCT Model
IST4Balt Workshop
April 6, 2005
Prof. Yuri Merkuryev
APPLICATION CASE 1:
NET PRODUCTIVITY
AS A FUNCTION
OF NUMBER
OF TRUCKS
IST4Balt Workshop
April 6, 2005
Prof. Yuri Merkuryev
Dependence of average values of net productivity
(Z-scale, NPm)
on number of trucks Ntrucks (X-scale, from 1 to 6) and
3D Surface Plot (NP as fnct TR_Tt 5v*7526c)
average
fullmodel
truck
cycle time
Ttrucks
(Y-scale, 0-1000 sec)
NP of the
= Distance
Weighted
Least Squares
Net
Productivity
(m/h)
extremums
Truck
cycle
(sec)
IST4Balt Workshop
Number
of trucks
April 6, 2005
30
25
20
15
10
5
Prof. Yuri Merkuryev
Histograms of Net Productivity,
obtained with different number of trucks
Net Productivity
distribution as
a function
of trucks
number
6 trucks
5 trucks
Number
of NP
observations
4 trucks
50
40
30
20
10
0
3 trucks
IST4Balt Workshop
April 6, 2005
8
5
2
Net Productivity (m/h)
1 truck
11
14
17
20
23
26
29
32
35
2 trucks
Number
of trucks
Prof. Yuri Merkuryev
APPLICATION CASE 2:
RESOURCE
OPTIMIZATION
IST4Balt Workshop
April 6, 2005
Prof. Yuri Merkuryev
Methodology of Resource Pooling:
- Arena-based simulation of BCT operation for each set
of resources
- Simulation runs: for 154 ships, using statistical
distributions for different types of containers (20’, 40’,
import, export, restows and hatches)
- Validation: using a Kolmogorov-Smirnov test for
simulated and observed data
IST4Balt Workshop
April 6, 2005
Prof. Yuri Merkuryev
Illustration of methodology for estimating resource
distribution efficiency
Histogram (Real 142 hips
S of BCT 16v *142c)
Histogram (Real 142 hips
S of BCT 16v *142c)
24
30
22
28
Kolmogorov-Smirnov test for similarity is positive.
Model's output has the same distribution as the real output
Histogram (Real 142 hips
S of BCT 16v *142c)
40
35
26
20
18
12
10
30
14
12
8
10
6
8
20
15
10
6
4
4
2
5
2
0
0
0
8
16
25
33
41
49
58
66
74
82
91
99
0
0
107
11
Imp20
Histogram (Real 142 hips
S of BCT 16v *142c)
22
34
45
56
67
79
90
101
112
124
135
0
146
12
25
37
49
62
74
86
98
111
123
135
148
160
Exp20
Histogram (Real 142 hips
S of BCT 16v *142c)
Imp40
Histogram (Real 142 hips
S of BCT 16v *142c)
120
140
30
120
26
28
100
24
22
100
20
18
80
No of obs
No of obs
No of obs
80
60
60
16
14
12
40
10
40
Input –
output
task
explanation
35
30
25
20
15
10
8
5
6
20
Categorized Histogram
Variable: NetProd
Category : 0 NetProd
real = 142*1*normal(x, 21.9756, 1.8038)
Category : 1 NetProd
model = 142*1*normal(x, 21.5928, 2.055)
25
16
No of obs
20
14
No of obs
22
16
No of obs
No of obs
24
18
20
4
2
0
0
0
2
4
6
8
10
12
14
16
18
0
0
7
15
22
30
37
Hatches
44
52
59
66
74
81
89
96
0
11
23
Restow
34
46
57
68
80
91
102
114
125
137
0
148
14
Exp40
16
18
20
22
24
26
28
14
Category: 0
Input = 154 Ships with various statictical
16
18
20
22
24
26
28
Category: 1
NetProd
distributions of the differenr containers
Output = Net
3D Scatterplot (Resource Combinations 3v *36c)
M AERSK
M AERSK
M AERSK
M AERSK
BCT
BCTModel
Model
M AERSK
6, 98, 110
5, 98, 110
6, 90, 110
4, 98, 110
5, 90, 110
3, 98, 110
4, 90, 110
6, 80, 110
3, 90, 110
model
parameters
5, 80, 110
4, 80, 110
3, 80, 110
6, 98, 90
5, 98, 90
6, 90, 90
5, 90, 90
6, 98, 80
4, 90, 90
6, 80, 90
5, 98, 80
3, 90, 90
5, 6,
80,90,
9080
4, 98, 80
4, 80,
9080
5, 90,
3, 98, 80
3, 80,
90 80
4, 90,
6, 80, 80
3, 90, 80
5, 80, 80
4, 80, 80
3, 80, 80
4, 98, 90
3, 98, 90
Productivity NP (m/h)
NPi as function of resource 3D point
NPi as function of resource 3D point
NPi as function of resource 3D point
NPi as function of resource 3D point
NPi as function
NPiofasresource
function 3D
of resource
point
3D point
NPi as function
NPiofasresource
function 3D
of resource
point
3D point
NPi as function
NPiofasresource
function 3D
of resource
point
3D point
NPi as function
NPiofasresource
function 3D
of resource
point
3D point
NPi as function
NPiofasresource
function 3D
of resource
point
3D point
NPi as function
NPiofasresource
function 3D
of resource
point
3D point
NPi as function
NPiofasresource
function 3D
of resource
point
3D point
NPi as function
NPiofasresource
function 3D
of resource
point
3D point
NPi as function
NPiofasresource
function 3D
of resource
point
3D point
NPi as function of resource 3D point
NPi as function of resource 3D point
NPi as function of resource 3D point
NPi as function of resource 3D point
NPi as function of resource 3D point
NPi as function of resource 3D point
The 3D Set of
possible resource
assignments – model
parameters
IST4Balt Workshop
April 6, 2005
Prof. Yuri Merkuryev
Analysis of efficiency criterion values and their 95%
confidence intervals for all combinations of resources.
The global optimum is found for the workgroup of 4 trucks, QC1, YC1.
Plot of Means and Conf. Intervals (95.00%)
Optimal resource
pool consist of
100
QC1+YC1+4Tr
Criterion dependencies
2 from YC
QCrane1
(blue)
90
80
70
Values
60
QC1
80
50
40
Trucks:
3
Yard
Crane1 Workshop
IST4Balt
YC1
4
QC2
trucks
QC3
90
98
4
6
5
(Ty_set: 80 )
Trucks:
3
4
6
5
(Ty_set:
90 )
April
6, 2005
YC2
Trucks:
3
4
6
5
Prof.
YC3 (Ty_set:
110 )Yuri Merkuryev
Simulation results: Average EE values in descending order
100
Criterion value, EUR
90
80
70
60
50
40
0
5
10
15
20
25
30
35
40
Resource combination Nr.
IST4Balt Workshop
April 6, 2005
Prof. Yuri Merkuryev
Simulation results:
9 best resource sets → Mann-Whitney U-criterion
→ 5 resource sets:
1. QC1-TT4-YC1
2. QC1-TT4-YC2
3. QC2-TT4-YC1
4. QC2-TT4-YC2
5. QC3-TT4-YC3
IST4Balt Workshop
April 6, 2005
Prof. Yuri Merkuryev
Simulation results:
3D Surf ace Plot (1statTr_4 10v *1386c)
Criterion(4 trucks) as function from QC(Tq_set) and YC(ty_set
)
= Distance Weighted Least Squares
91.0042
88
84
80
76
IST4Balt Workshop
April 6, 2005
Prof. Yuri Merkuryev
The Optimal Set of Resources:
- Includes TT4
- To decide on QC and YC from 5 candidate sets, an
additional criterion should be considered,
e.g., min relative cost of resources (i.e., total costs
divided by EE)
IST4Balt Workshop
April 6, 2005
Prof. Yuri Merkuryev
The Optimal Set of Resources:
Type (NP)
Cost
QC3-TT4-YC3
YC1 (45)
875000
YC2 (40)
820000
QC1 (45)
1000000
21307
(99%),
EE=88 EUR
21415
(100%),
EE=85 EUR
QC2 (40)
920000
20632
(96%),
EE=87 EUR
20233
(94%),
EE=86 EUR
QC3 (37)
870000
YC3 (33)
770000
18851
(88%),
EE=87 EUR
Relative costs of candidate sets of resources
IST4Balt Workshop
April 6, 2005
Prof. Yuri Merkuryev
Acknoledgements:
1. The presented research was performed within the
BALTPORTS-IT project “Simulation and IT-Solutions:
Applications in the Baltic Port Areas of the Newly
Associated States” of the IST Programme of the
European Commission.
2. Support of the BCT Managerial staff is highly
appreciated.
IST4Balt Workshop
April 6, 2005
Prof. Yuri Merkuryev
Conclusions:
1. A simulation model of the Baltic Container Terminal
was considered.
2. Simulation could be applied for assisting terminal
management in solving the following tasks: forecast,
what-if-analysis, optimisation, visualisation of
operation.
3. In the presented study on simulation-based
optimisation, the optimal set of resources includes the
less expensive and less productive resources YC3 and
QC3.
IST4Balt Workshop
April 6, 2005
Prof. Yuri Merkuryev