Heuristics for flights arrival scheduling at airports

Self Cite

Feature selection aims to choose a subset of features, out of the set of candidate features, such that the selected set best represents the whole in a particular aspect. We propose an optimization model for the problem of selecting features in the presence of two groups of data. The objectives include minimizing the number of selected features and maximizing similarities within the same group and differences between different groups. We use the lexicographic method and prove several properties of the problem. We show that obtaining even feasible solutions for the problem can be challenging. Therefore, we propose matheuristic algorithms. We test our algorithms on both randomly generated and real‐world datasets. The computational results indicate that the proposed matheuristics deliver quality solutions in a reasonable amount of time.

Section: The Proposed Solution Methodsmentioning

confidence: 99%

An optimization method for characterizing two groups of data

Salehipour

2022

Self Cite

“…In this section, we propose an R&S matheuristic algorithm for solving problem

1|(a_j, L_j, b_j)|C_{\max }

. The R&S has been shown to deliver quality solutions for challenging scheduling problems (Ahmadian et al., 2020; Khatami and Salehipour, 2020; Ahmadian and Salehipour, 2021; Ahmadian et al., 2021; Ahmadian and Salehipour, 2022). The operation of the R&S algorithm is similar to that of the fix‐and‐optimize algorithm of Helber and Sahling (2010).…”

Section: The Solution Methodsmentioning

confidence: 99%

The coupled task scheduling problem: an improved mathematical program and a new solution algorithm

Khatami

Salehipour

2022

Self Cite

The general single machine coupled task scheduling problem with the objective function of minimizing the makespan, which is strongly NP‐hard, aims to schedule a set of coupled task jobs on one machine such that the completion time of the last job is minimized. We propose a new mixed‐integer program (MIP) for the problem. We also propose a relax‐and‐solve (R&S) matheuristic algorithm as the solution method. We show that the new MIP outperforms the available models and improves the quality of solutions. Also, the proposed MIP significantly improves the average gap to the best known feasible solution of an existing binary search algorithm. We show that our R&S matheuristic produces new best solutions for almost 50% of the instances.

“…Ma et al (2021) propose two ILP models and a simulated annealing to optimise the departure runway scheduling at CDG while considering arrival runway crossing. Ahmadian and Salehipour (2022) present a matheuristics for the aircraft landing problem. This algorithm relies mainly on an iterative destruction of aircraft landing sub-sequences.…”

Section: Tactical Handling Of Airside Operationsmentioning

confidence: 99%

An ILP approach for tactical flight rescheduling during airport access mode disruptions

Scozzaro,

Mancel,

Delahaye

et al. 2023

Airport access mode disruptions, such as a subway shutdown, threaten the whole passenger door‐to‐door journey. When such a disruptive event occurs, knowledge of passengers' delays would help the airport operation centre to decide if a departure flight should be delayed. This paper proposes a tactical flight rescheduling at an airport to minimise the number of stranded passengers while considering operational constraints. An integer linear programming formulation of the problem is presented. Constraints such as terminal capacities, maximal runway throughput, minimum turnaround time or minimum transfer time for connecting passengers are considered. An exact and heuristic resolution is proposed and compared to a study case around Paris‐Charles de Gaulle airport. The new schedule satisfies the operational constraints and reduces up to 60% of the number of stranded passengers with moderate deviation from the initial planning.