An Integrated Bi-Objective Mathematical Model for Minimizing Take-off Delay and Passenger Dissatisfaction

Document Type : Original Manuscript


Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran


As air transportation has increased in recent years, it is necessary for airport planners to optimally manage aircraft ground traffic on stands, taxiways and runways in order to minimize flight delay and passenger dissatisfaction. A closer look at the literature in this area indicates that most studies have merely focused on one of these resources which in a macroscopic level may result in aircrafts’ collision and ground traffic at the airport. In this paper, a new bi-objective Mixed-Integer Linear Programming (MILP) model is developed to help airport management to integrate Gate Assignment Problem (GAP) and Runway Scheduling Problem (RSP) considering taxiing operation for departing flights. The proposed model aims to help airport planners to 1) minimize any deviation from preferred schedule and 2) minimize transit passengers’ walking distance. Due to the complexity of the research problem, a Normalized Weighted Sum Method (NWSM) is applied to solve small-sized problems and two meta-heuristics, namely NSGA-II and MOGWO, are used for large-scale instances to generate Pareto optimal solutions. The performance of these algorithms is assessed by well-known coverage and convergence measures. Based on the most criteria, the results indicate that MOGWO outperforms NSGA-II.

Graphical Abstract

An Integrated Bi-Objective Mathematical Model for Minimizing Take-off Delay and Passenger Dissatisfaction


  • A novel mathematical model is represented in order to manage the ground traffic at a busy airport considering gates, runways and taxi network simultaneously
  • Two metaheuristic algorithms, i.e. NSGA-II and MOGWO, are applied to address large-scale problems, and they are then compared on 4 different performance measures
  • Implementing the mathematical approach presented in this article contributes to minimizing any deviation from preferred scheduling and also minimizing transit passengers discontent


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