Next Article in Journal
A Fast Derivative-Free Iteration Scheme for Nonlinear Systems and Integral Equations
Next Article in Special Issue
Multi-Attribute Decision-Making Methods as a Part of Mathematical Optimization
Previous Article in Journal
On the Efficacy of Ensemble of Constraint Handling Techniques in Self-Adaptive Differential Evolution
Previous Article in Special Issue
Some Hesitant Fuzzy Hamacher Power-Aggregation Operators for Multiple-Attribute Decision-Making
Open AccessArticle

A Comparative Analysis of Simulated Annealing and Variable Neighborhood Search in the ATCo Work-Shift Scheduling Problem

Departamento de Inteligencia Artificial, E.T.S.I. Informáticos, Universidad Politécnica de Madrid, Campus de Montegancedo S/N, 28660 Boadilla del Monte, Spain
*
Authors to whom correspondence should be addressed.
Mathematics 2019, 7(7), 636; https://doi.org/10.3390/math7070636
Received: 25 June 2019 / Revised: 15 July 2019 / Accepted: 16 July 2019 / Published: 17 July 2019
(This article belongs to the Special Issue Optimization for Decision Making)
This paper deals with the air traffic controller (ATCo) work shift scheduling problem. This is a multi-objective optimization problem, as it involves identifying the best possible distribution of ATCo work and rest periods and positions, ATCo workload and control center changes in order to cover an airspace sector configuration, while, at the same time, complying with ATCo working conditions. We propose a three-phase problem-solving methodology based on the variable neighborhood search (VNS) to tackle this problem. The solution structure should resemble the previous template-based solution. Initial infeasible solutions are built using a template-based heuristic in Phase 1. Then, VNS is conducted in Phase 2 in order to arrive at a feasible solution. This constitutes the starting point of a new search process carried out in Phase 3 to derive an optimal solution based on a weighted sum fitness function. We analyzed the performance in the proposed methodology of VNS against simulated annealing, as well as the use of regular expressions compared with the implementation in the code to verify the feasibility of the analyzed solutions, taking into account four representative and complex instances of the problem corresponding to different airspace sectorings. View Full-Text
Keywords: air traffic management; work-shift scheduling problem; variable neighborhood search; performance analysis air traffic management; work-shift scheduling problem; variable neighborhood search; performance analysis
Show Figures

Figure 1

MDPI and ACS Style

Tello, F.; Jiménez-Martín, A.; Mateos, A.; Lozano, P. A Comparative Analysis of Simulated Annealing and Variable Neighborhood Search in the ATCo Work-Shift Scheduling Problem. Mathematics 2019, 7, 636. https://doi.org/10.3390/math7070636

AMA Style

Tello F, Jiménez-Martín A, Mateos A, Lozano P. A Comparative Analysis of Simulated Annealing and Variable Neighborhood Search in the ATCo Work-Shift Scheduling Problem. Mathematics. 2019; 7(7):636. https://doi.org/10.3390/math7070636

Chicago/Turabian Style

Tello, Faustino; Jiménez-Martín, Antonio; Mateos, Alfonso; Lozano, Pablo. 2019. "A Comparative Analysis of Simulated Annealing and Variable Neighborhood Search in the ATCo Work-Shift Scheduling Problem" Mathematics 7, no. 7: 636. https://doi.org/10.3390/math7070636

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop