Optimizing airport slot scheduling problem using optimization algorithms

Abstract

The primary objective of airport management worldwide is always to make it easier to provide transportation services and minimize latency. This could be accomplished by controlling the movement of travelers through the airport's different checkpoints for passports, baggage handling, customs, and both departure and arrival lobbies. As one of the biggest passenger terminals around the world and among the most attractive destinations for visitors during the Hajj pilgrimage, this paper concentrates on enhancing the movement of travelers in the King Abdulaziz International Airport's pilgrimage station in the Kingdom of Saudi Arabia. Several optimization methods are used to better schedule the phases within the airport terminals as well as the assignment of arriving flights to vacant airport portals. These include the differential evolution algorithm (DEA), harmony search algorithm, genetic algorithm (GA), flower pollination algorithm (FPA), and black widow optimization algorithm. The findings demonstrated the potential sites for the development of airport stages, which may assist decision-makers in improving operational efficiency in the future. The simulation results showed that GA was more efficient in most of the experiments than the alternative algorithms for small population sizes in terms of the quality of the solutions obtained and the convergence rates. In contrast, DEA performed better in the larger population sizes. The outcomes also showed that FPA performed better than its rivals in identifying the optimal solution in terms of the overall duration of passenger waiting time.

A Study on Disrupted Flight Recovery Based on Logic-Based Benders Decomposition Method

“…Equations ( 22) and ( 23) are the objective functions, which require the flight delay cost to be the minimum; Equation (24) indicates that the actual departure time of the flight must not be earlier than the original planned departure time; Equation (25) indicates that the actual flight departure time is not earlier than the moment when all aircraft begins to execute the flight; Equation (26) indicates that the actual landing time of the flight is not later than the latest time when all the aircraft finish the flight; Equation (27) indicates that the flight delay time cannot be greater than 180 min; Equations ( 28) and (29) indicate that, if the planned departure of the flight is to close the airport, the actual departure time of the flight must be before the airport closure start time or after the airport closure period; Equation (30) indicates that, if the aircraft maintenance mission is executed, the departure time of all flights must be after the maintenance end time or the landing time must be before the maintenance start time; Equation (31) is the time connection constraint, indicating that the two consecutive flights executed by the same aircraft have an overtime of no less than 30 min.…”

“…Huang based theirs on a copy evaluation method which uses a primal-dual approach to analyze and measure the quality of flight and maintenance copies [30]. Subsequently, many heuristic algorithms have been used to solve the flight recovery problem, such as the Genetic Algorithm (GA) [31,32] and the Greedy randomized adaptive search procedure (GRASP) [32], among others.…”