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Untitled Prezi

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Resource Allocation at Hong Kong International Terminals Integrated Simulation and Optimization Approach
Model in Arena
Group: K2
Supervisor: Dr. Karolina
Background
Research Objectives
Increase the competitiveness
- Examine quay allocation in order to minimize total penalty
- Examine the effects of two factors – buffer time and evaluation period – on total penalty

Research Method
Literature Review
Simulation Model
Located at the center of the fast developing Asia-pacific Region.
Top four busiest container ports.
Directly contribute 2.3% (US$5 billion) to HK GDP.
Account for 2.7% (95,000 jobs) of total employment.
The ports of Hong Kong
Encounter Challenge
Shenzhen Pillar
- Companies shift their business from Hong Kong container port to Shenzhen terminal service provider.
- Ranking of Hong Kong container ports has been dropped from rank 3 to rank 4.

Hong Kong International Terminals Limited (HIT)
One of the largest container port business trust. It plays an important role in continuing development of the port of Hong Kong.
Buffer Time
Evaluation period
Three values of evaluation period
- Evaluation period = 10 hours and 20 hours
- For evaluation period = null, no optimization will be conducted.
Minimize the penalty due to the delay for the loading and discharging time.
Schedule the quay cranes at each berth in different periods of time to minimize the total penalty.
Optimization model
The use of simulation is necessary because this study’s model have many random variables (uncertainty).
Integration between ARENA and VBA.
Simulation model
Multi-factor ANOVA
Examine the effects of two factors.
Similarities
Methodology
- Simulation and optimization
- Integration

Differences
Do not focus on the effect of buffer time and evaluation period on total penalty
Address berth allocation problem instead of crane allocation problem
Process flow
LAU Chi Wing, s126174
KWAN Cheuk Yiu, s126172
LEUNG Sui Wai, s126175
ZHANG Zheng, s126180

Submodel
Assumptions
1. No differences of the quay crane’s service time on small or the large vessels.
2. The pickup time of each container is around 6 minutes.
3. All the ships arrive on time.
4. Number of available berths is set to 10.
5. Numbers of available cranes is set to 40; all cranes are available at all times without breakdowns.
6. Disregard berthing/unberthing time.
7. The time taken for moving cranes can be ignored.

8. The characteristics of vessels are set based on the table below:

9. Lognormal distribution is used for the number of crane moves.
10. The probability distribution of the number of moves depends on the size of the ship.
11. The total time required to process a ship then depends on the number of moves and the number of cranes allocated to that ship.
12. The duration of loading and discharging depends only on the number of required lifts and the number of cranes allocated to the ship.
13. Each vessel requires exactly one berth, regardless of the ship size.
14. Minimum number of cranes allocated to each berth is 1.
15. No incidents of crane blocking are considered.
16. Maximum number of cranes assigned to each berth is not limited.
Optimization Model
Objective function
1. Non-linear objective function
2. Using Premium Solver in Excel
3. Achieving optimal solution
Findings
Identify 2 experimental factors (Buffer and Evaluation Period).
Analyze the effect of different buffer time and evaluation period on the total penalty by using Multi-factor ANOVA.
The p-value (A) =
0.0001

- buffer time has statistically significant effect on the total penalty.
The p-value (B) =
0.0000
- evaluation period has statistically significant effect on the total penalty.
The p-value (AB) =
0.0000
- the effect of buffer time on total penalty also depends on the evaluation period.
P-values only indicates that whether the effects are statistically significant.
Examine the magnitude of these effects.
Present the interaction plot of the effects of two factors on total penalty.
Conclusion
Increasing buffer size can minimize the penalty
The more frequently evaluation period, the better the resource have been utilized
But both of the buffer size and evaluation period must up to a point
Limitations of our study
Future studies
limitation (2) can be eliminated by setting the maximum number of quay cranes at each berth

limitation (3) could be addressed by solving a transshipment model for moving cranes between berths.
The effect of size of the implicit buffer
Etc.
1. Looking at the things happening in this time.
2. The maximum number of cranes have not been set, which can result in extreme solutions.
3. Time to move the cranes are ignored.
4. The duration of handling the vessel is assumed to depend only on the number of lifts and the number of cranes allocated to the vessel, as specified by Bierwirth (2009), the handling time may also be affected by
- location of the berth
- schedule of various resources
- loading time of different resources
p-value
At time = t1, reallocate 3 cranes from berth 2 to berth 1, c1 = 7, c2 = 1
5. Number of moves is assumed to follow lognormal distributions with parameters depending only on the size of the ship.
6. The pickup time of each container is assumed around 6 minutes.
7. Only look at evaluation period = 10 and 20 hours.
8. Explicit buffer size = 1, 2 hours
Group Members:
Alavarez et al., (2010)
implicit buffer
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