Continuous growth in the volume of containers has placed significant burdens on the landside operations of
seaports. Many seaports are facing serious congestion both at gate and in yard due to a large number of trucks
arriving at terminal during peak periods. Meanwhile, serious congestion leads to longer truck waiting time,
higher air pollution and lower terminal operation efficiency. Therefore, how to decrease the truck turn time in the
terminal is an important issue for terminal operator, truck fleet and government regulators.
In response to heavy congestion, ports like Vancouver, Los Angeles and Long Beach have implemented truck
appointment system (Morals and Lord, 2006). Using this system, terminal operator limits the maximum number
of appointment quota during each period. Truckers can make reservations during which period they prefer.
International and domestic academics have done research both on the efficiency of truck appointment and truck
appointment model. However, truck appointment system is complex and it involves a lot of factors. The length of
truck queues is influenced by truck arrivals, the gate service rate and yard service rate. Moreover, the arrival of
trucks varies with time, which leads to that the traditional stationary queuing theory can t describe the process of
queuing.
In this paper, a model optimizing appointment quota of each period to minimize the truck turn time is
addressed. A Baskett Chandy Muntz Palacios (BCMP) queuing network is used to describe the process of trucks
at gate and yard. To solve the model, Genetic algorithm and a method based on the Pointwise Stationary Fluid
Flow Approximation (PSFFA) are designed. GA was used to search the optimal solution under the constraints of
adjustment quota and PSFFA was designed to calculate the truck waiting time. Finally the model is test based on
the data of a terminal of Tianjin.
The reminder of this paper is organized as follows. In Section 2, a literature review on approaches to alleviate
terminal congestion is provided. Section 3 describes the problem and proposes a truck appointment model.
Genetic algorithm and a method based on the PSFFA are designed to solve the model in Section 4. Numerical
experiments are conducted in section 5 to illustrate the validity of the methods. Conclusions are provided in
Section 6.
Continuous growth in the volume of containers has placed significant burdens on the landside operations ofseaports. Many seaports are facing serious congestion both at gate and in yard due to a large number of trucksarriving at terminal during peak periods. Meanwhile, serious congestion leads to longer truck waiting time,higher air pollution and lower terminal operation efficiency. Therefore, how to decrease the truck turn time in theterminal is an important issue for terminal operator, truck fleet and government regulators.In response to heavy congestion, ports like Vancouver, Los Angeles and Long Beach have implemented truckappointment system (Morals and Lord, 2006). Using this system, terminal operator limits the maximum numberof appointment quota during each period. Truckers can make reservations during which period they prefer.International and domestic academics have done research both on the efficiency of truck appointment and truckappointment model. However, truck appointment system is complex and it involves a lot of factors. The length oftruck queues is influenced by truck arrivals, the gate service rate and yard service rate. Moreover, the arrival oftrucks varies with time, which leads to that the traditional stationary queuing theory can t describe the process ofqueuing.In this paper, a model optimizing appointment quota of each period to minimize the truck turn time isaddressed. A Baskett Chandy Muntz Palacios (BCMP) queuing network is used to describe the process of trucksat gate and yard. To solve the model, Genetic algorithm and a method based on the Pointwise Stationary FluidFlow Approximation (PSFFA) are designed. GA was used to search the optimal solution under the constraints ofadjustment quota and PSFFA was designed to calculate the truck waiting time. Finally the model is test based onthe data of a terminal of Tianjin.The reminder of this paper is organized as follows. In Section 2, a literature review on approaches to alleviateterminal congestion is provided. Section 3 describes the problem and proposes a truck appointment model.Genetic algorithm and a method based on the PSFFA are designed to solve the model in Section 4. Numericalexperiments are conducted in section 5 to illustrate the validity of the methods. Conclusions are provided inSection 6.
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