Passenger walking distance is an important index of the airport service quality. How to shorten the walking distance and balance the airlines' service quality is the focus of much research on airport gate assignment problems. According to the problems of airport passenger service quality, an optimization gate assignment model is established. The gate assignment model is based on minimizing the total walking distance of all passengers and balancing the average walking distance of passengers among different airlines. Lingo is used in the simulation of a large airport gate assignment. Test results show that the optimization model can reduce the average walking distance of passenger effectively, improve the number of flights assigned to gate, balance airline service quality, and enhance the overall service level of airports and airlines. The model provides reference for the airport gate preassignment.
Airport gate is a main component of airport resource. Rational and efficient gate assignment is an important way to improve airport operation efficiency and passenger service level. Airport gate is divided into contact gate (a gate with an aerobridge) and remote stand (on the apron). The type and layout lead to different distance from gate to security check, baggage hall, and transit counters. The distance between different areas has a direct impact on passenger activities in the terminal. How to optimize the gate assignment from the perspective of passengers becomes a hot research area at home and abroad.
At present, the main research findings of gate assignment from the perspective of passengers took the shortest passenger walking distance and the minimum embarking and transit time as objective function to optimize gate assignment; for example, Braaksma, [
Optimization gate assignment from the perspective of passengers can reduce passenger walking distance and improve passenger service levels to a certain extent. However, there are some deficiencies in research findings. Firstly, in some large hub airporsts, the proportion of transit passengers is large and the actual walking distance of transit passengers is not equal to the actual distance between two gates. The walking distance is related with the layout of transit counters and transit halls. In the research, ignoring transit passengers can cause the model to be inaccurate. Secondly, civil airport service quality issued by the Civil Aviation Administration in 2006 requires that the number of passengers embarking/disembarking through aerobridges should be above 80%. But in most current research, the proportion of passengers is not taken into account. Thirdly, most current researches do not consider the balance of passenger walking distance between different airlines, which can lead to reducing the passenger service level and can be unfair for some airlines, especially for small airlines.
Optimizing gate assignment can improve passengers’ satisfaction and balance the service quality of each airline. In this research, we propose a new model which is different from previous researches; the gates are categorized into contact gate and remote stand in the mode, the proportion of passenger embarking/disembarking through aerobridges is taken into account, and the model considers the fairness between airlines besides reducing the overall passengers’ walking distance.
The paper is organized as follows. The gate assignment model is detailed in Section
Gate assignment is to arrange a reasonable gate for each arrival-departure flight timely according to the flight plan, which is submitted by every airline. Safety operation of aircraft and gate is the premise of gate assignment.
Passenger walking distance in a large airport is composed of three parts: arrival passenger walking distance, departure passenger walking distance, and transit passenger walking distance. The arrival passenger walking distance refers to the distance from gate to baggage hall; the departure passenger walking distance refers to the distance from security check to gate; the transit passenger walking distance refers to the distance from gate to transit counter and then to the next flight gate. The arrival-departure transit passengers are known collectively as transit passengers in the paper. The walking distance of transit counter passengers includes the arrival passengers’ distance from gate to transit counter and the departure passengers’ distance from transit counter to gate.
Minimizing and balancing the walking distance of all passengers from different airlines are goals to model gate assignment in the paper. Then Lingo software is adopted to verify the effectiveness of a model in order to improve the service level of airport and airline.
Gate assignment is a continuous operation course. In order to reduce the scale of the problem, the paper selects some time intervals for gate assignment. The capacity of gates can meet the demand of all flights in the research time; it means that every flight can be assigned to a gate. The arrival-departure flight performed by the same aircraft is assigned to the same gate and it used the same flight number. All information, such as flight plan, aircraft basic information, the usage status of gates, and so on, is known in research time. Only the gate assignment of domestic flights is considered in the paper.
flight set, size of the aircraft which executes flight airline set, the flights set of airline gate set,
Assuming that the number of gates is
Consider
Minimizing the total walking distance of all passengers in research period is one of the goals in the paper.
Consider
According to the objective function (
Consider
Subject to
Equations (
Equation (
Equation (
Equation (
Equation (
The decision variables in the gate assignment model are 0 and 1, belonging to 0-1 planning of integer programming problem. Due to nonlinear constraints involved in the model, the model is called integer nonlinear programming (INLP). The paper uses Lingo software to simulate and verify the model. The Global (global optimization algorithm) and Multistart (more initial point algorithm) built-in Lingo are specifically used to solve nonlinear programming (Scharge [
The simulation data of domestic flights to be assigned in a typical time interval (8:00–11:00) in a large airport is shown in Tables
Domestic flight to be assigned from 8:00 to 11:00.
Flight no. | Type | Airline | Arr. time | Dep. time | Number of arr. passengers | Number of dep. passengers | Number of transit passengers | Total passengers |
---|---|---|---|---|---|---|---|---|
F101 | M | A1 | 08:00 | 08:55 | 35 | 48 | 174 | 257 |
F102 | M | A2 | 08:15 | 09:20 | 129 | 142 | 36 | 307 |
F103 | L | A4 | 08:30 | 09:50 | 132 | 136 | 169 | 437 |
F104 | M | A3 | 08:45 | 09:55 | 97 | 101 | 86 | 284 |
F105 | M | A1 | 09:00 | 10:10 | 106 | 89 | 128 | 323 |
F106 | L | A2 | 09:10 | 10:30 | 206 | 189 | 64 | 459 |
F107 | M | A1 | 09:15 | 10:20 | 72 | 96 | 72 | 240 |
F108 | S | A2 | 09:30 | 10:15 | 41 | 46 | 98 | 185 |
F109 | M | A3 | 09:40 | 10:40 | 128 | 114 | 29 | 271 |
F110 | M | A4 | 10:00 | 11:30 | 154 | 146 | 65 | 365 |
F111 | S | A3 | 10:05 | 10:55 | 49 | 63 | 32 | 144 |
F112 | M | A2 | 10:20 | 11:20 | 143 | 136 | 40 | 319 |
F113 | M | A3 | 10:30 | 11:25 | 98 | 92 | 108 | 298 |
F114 | L | A2 | 10:35 | 11:55 | 246 | 238 | 63 | 547 |
F115 | L | A4 | 10:55 | 12:00 | 182 | 168 | 57 | 407 |
F116 | M | A2 | 11:00 | 11:50 | 118 | 115 | 20 | 253 |
Note: L, M, and S represent large, middle, and small aircrafts, respectively. A1~A4 represent different airlines.
Data of available gates.
Gate no. | Gate size | Distance to the baggage hall (unit: m) | Distance to the security check points (unit: m) | Distance to the transit counter (unit: m) | Average distance (unit: m) | Contact gate or remote stand |
---|---|---|---|---|---|---|
G001 | M | 150 | 245 | 215 | 203.3 | C |
G002 | L | 240 | 270 | 245 | 251.7 | C |
G003 | M | 220 | 260 | 230 | 236.7 | C |
G004 | M | 190 | 235 | 210 | 211.7 | C |
G005 | L | 135 | 170 | 115 | 140.0 | C |
G006 | S | 530 | 585 | 440 | 518.3 | C |
G007 | M | 520 | 580 | 425 | 508.3 | C |
G008 | L | 400 | 220 | 230 | 283.3 | C |
G009 | L | 920 | 960 | 975 | 951.7 | R |
G010 | L | 1000 | 1100 | 1050 | 1050.0 | R |
Note: L, M, and S represent large, middle, and small gates, respectively
The paper uses Lingo to simulate the results of the random assignment, the objective function (
Comparison of simulation results between random assignment, optimization
Results |
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Random assignment | 2089750 | 0.238 | 0.831 | 410.1 | 351.1 | 351.9 | 461.2 | 507.6 | 156.5 |
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1244430 | 0.299 | 1.000 | 244.2 | 206.9 | 243.6 | 317.3 | 210.2 | 110.4 |
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2165555 | 0.003 | 0.800 | 425.0 | 423.8 | 424.4 | 426.1 | 425.7 | 2.3 |
With Table When When The three values of
Comparison of
Comparison of
From the above simulation results, the three groups all have shortcomings. To find a set of ideal solution, the paper takes the objective function (
Comparison of simulation results under five different conditions.
Results |
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Random assignment | 2089750 | 0.238 | 0.831 | 410.1 | 351.1 | 351.9 | 461.2 | 507.6 | 156.5 |
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1244430 | 0.299 | 1.000 | 244.2 | 206.9 | 243.6 | 317.3 | 210.2 | 110.4 |
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2165555 | 0.003 | 0.800 | 425.0 | 423.8 | 424.4 | 426.1 | 425.7 | 2.3 |
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1368320 | 0.041 | 0.964 | 268.5 | 278.1 | 267.7 | 275.6 | 257.6 | 20.5 |
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1272690 | 0.199 | 1.000 | 249.7 | 204.0 | 252.0 | 222.5 | 299.3 | 95.3 |
According to Table
Comparison value of
Comparison value of
Flight Gantt chart of the random gate assignment and the situation of
The flight Gantt chart of random gate assignment.
The flight Gantt chart of
Distribution of passengers and the average distance of gate.
It is convenient for passengers to embark/disembark the aircraft through aerobridge because the distance is close and passengers will not be influenced by weather. The average distance from gate to baggage hall, security check, and transit counter is shorter; the total walking distance of passengers assigned to the gate is shorter. Thus the passenger will feel comfortable. We can draw the conclusions from Figure
In summary, the simulation optimization results can not only reduce the average passenger walking distance effectively and improve passing rate, but also reduce the difference of average walking distance of passengers among airlines and enhance the overall passenger service quality of airports and airlines.
The paper presents a new idea for the airport gate assignment problem. Unlike the previous researches, it takes the restraint of passenger passing rate and airlines’ fairness into account under the premise of airport safety operation. Combining with the objective of minimizing the whole passengers’ walking distances, the paper builds a multiobjective optimization model of gate assignment. Lingo software is used to verify the effectiveness of model by simulating a large airport gate assignment. According to the test results, we can draw some conclusions. The assignment can ensure the passengers passing rate by setting ( The two objectives are interactional between each other. And decision makers can get a set of suitable results by adjusting the value range of the second objective. Compared to the random assignment, this model can reduce the whole passengers’ walking distances and improve the fairness between airlines at the same time. The research scope of the paper is only part of the domestic flights. How to combine with international flights and effective resource schedule should be further researched.
This work was supported by the National Natural Science Foundation of China and Civil Aviation Administration of China (no. U1333117), China Postdoctoral Science Foundation (no. 2012M511275), and the Fundamental Research Fund for the Central Universities (nos. NS2013067, NN2012019, and NS2012115).