A Simulation Based Analysis on Setting Pontoons in a Regional Container Hub Port

2014 ◽  
Vol 505-506 ◽  
pp. 884-893
Author(s):  
Bo Qiu ◽  
Zhi Hong Jin
Keyword(s):  
Genetic Algorithm ◽  
Process Simulation ◽  
Decision Model ◽  
Container Terminal ◽  
Waiting Times ◽  
Operational Cost ◽  
Shipping Company ◽  
Operational Costs ◽  
Modified Genetic Algorithm ◽  
Simulation Based

Dock congestion has been becoming increasingly troublesome problem in some regional hub ports in China, resulting in additional operational costs to feeder line shipping companies. To reduce the waiting times at the hub and to lower the operational cost, some companies place a pontoon close to the hub port as floating container terminal to berth smaller containerships. We discuss the justification for setting pontoons close to the hub from the viewpoint of a shipping company, propose a berthing decision model and describe a modified genetic algorithm to optimize the process. Simulation tests show that pontoon setting and rational scheduling can greatly alleviate congestion at the regional hub.

scholarly journals Noisy Optimization of Dispatching Policy for the Cranes at the Storage Yard in an Automated Container Terminal

2021 ◽  
Vol 11 (15) ◽  
pp. 6922
Author(s):  
Jeongmin Kim ◽  
Ellen J. Hong ◽  
Youngjee Yang ◽  
Kwang Ryel Ryu
Keyword(s):  
Container Terminal ◽  
Scoring Function ◽  
Weight Vector ◽  
Container Terminals ◽  
Exploration And Exploitation ◽  
Noisy Optimization ◽  
Automated Container Terminal ◽  
Simulation Based ◽  
The Cost ◽  
Operation Schedule

In this paper, we claim that the operation schedule of automated stacking cranes (ASC) in the storage yard of automated container terminals can be built effectively and efficiently by using a crane dispatching policy, and propose a noisy optimization algorithm named N-RTS that can derive such a policy efficiently. To select a job for an ASC, our dispatching policy uses a multi-criteria scoring function to calculate the score of each candidate job using a weighted summation of the evaluations in those criteria. As the calculated score depends on the respective weights of these criteria, and thus a different weight vector gives rise to a different best candidate, a weight vector can be deemed as a policy. A good weight vector, or policy, can be found by a simulation-based search where a candidate policy is evaluated through a computationally expensive simulation of applying the policy to some operation scenarios. We may simplify the simulation to save time but at the cost of sacrificing the evaluation accuracy. N-RTS copes with this dilemma by maintaining a good balance between exploration and exploitation. Experimental results show that the policy derived by N-RTS outperforms other ASC scheduling methods. We also conducted additional experiments using some benchmark functions to validate the performance of N-RTS.

Global geometry optimization of (Ar)n and B(Ar)n clusters using a modified genetic algorithm

1996 ◽  
Vol 104 (7) ◽  
pp. 2684-2691 ◽  
Author(s):  
Susan K. Gregurick ◽  
Millard H. Alexander ◽  
Bernd Hartke
Keyword(s):  
Genetic Algorithm ◽  
Geometry Optimization ◽  
Global Geometry ◽  
Modified Genetic Algorithm
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