An Efficient Genetic Algorithm with Uniform Crossover for the Multi-Objective Airport Gate Assignment Problem

2009 ◽  
pp. 71-89 ◽  
Author(s):  
Xiao-Bing Hu ◽  
Ezequiel Di Paolo
Keyword(s):  
Genetic Algorithm ◽  
Assignment Problem ◽  
Multi Objective ◽  
Uniform Crossover ◽  
Gate Assignment

Multi-Objective Fleet Assignment Problem Based on Self-Adaptive Genetic Algorithm

2013 ◽  
Vol 694-697 ◽  
pp. 2895-2900 ◽  
Author(s):  
Xiao Yang ◽  
Bo Jiang
Keyword(s):  
Genetic Algorithm ◽  
Carbon Emissions ◽  
Assignment Problem ◽  
Network Capacity ◽  
Total Carbon ◽  
Adaptive Genetic Algorithm ◽  
Multi Objective ◽  
Route Network ◽  
Assignment Model ◽  
Key Factor

Since the beginning of the twenty-first century, energy conservation has become the theme of the development of the world. China government set the emissions-reduction targets in various industries on the 12th Five-Year Plan. And the airlines were committed to reduce their carbon emissions. From an operational perspective, the airline model assignment problem is a key factor of the total carbon emissions on the entire route network. But the traditional aircraft assignment models approach did not account for this purpose to reduce carbon emissions. By constructing the multi-objective optimization models consider carbon emissions assignment model using a genetic algorithm, numerical example shows that the model is able to meet all aspects demand which include meeting route network capacity demand, minimizing operating costs and reducing total aircraft fleet carbon emissions.

Multi-objective optimization model for airport gate assignment problem

2021 ◽  
Vol 93 (2) ◽  
pp. 311-318
Author(s):  
Ramazan Kursat Cecen
Keyword(s):  
Assignment Problem ◽  
Air Traffic ◽  
Walking Distance ◽  
Content Type ◽  
Multi Objective ◽  
Air Traffic Controllers ◽  
Proposed Model ◽  
Milp Model ◽  
Gate Assignment ◽  
Short Time

Purpose The purpose of this paper is to provide feasible and fast solutions for the multi-objective airport gate assignment problem (AGAP) considering both passenger-oriented and airline-oriented objectives, which is the total walking distance from gate to baggage carousels (TWD) and the total aircraft fuel consumption during taxi operations (TFC). In addition, obtaining feasible and near-optimal solutions in a short time reduces the gate planning time to be spent by air traffic controllers. Design/methodology/approach The mixed integer linear programming (MILP) approach is implemented to solve the multi-objective AGAP. The weighted sum approach technique was applied in the model to obtain non-dominated solutions. Because of the complexity of the problem, the simulated annealing (SA) algorithm was used for the proposed model. The results were compared with baseline results, which were obtained from the algorithm using the fastest gate assignment and baggage carousel combinations without any conflict taking place at the gate assignments. Findings The proposed model noticeably decreased both the TWD and TFC. The improvement of the TWD and TFC changed from 22.8% to 46.9% and from 4.7% to 7.1%, respectively, according to the priorities of the objectives. Additionally, the average number of non-dominated solutions was calculated as 6.94, which presents many feasible solutions for air traffic controllers to manage ground traffic while taking the airline and passenger objectives into consideration. Practical implications The proposed MILP model includes the objectives of different stakeholders: air traffic controllers, passengers and airlines. In addition, the proposed model can provide feasible gate and baggage carousel assignments together in a short time. Therefore, the model creates a flexibility for air traffic controllers to re-arrange assignments if any unexpected situations take place. Originality/value The proposed MILP model combines the TWD and TFC together for the AGAP problem using the SA. Moreover, the proposed model integrates passenger-oriented and airline-oriented objectives together and reveals the relationships between the objectives in only a short time.

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