Memorias de investigación
Artículos en revistas:
A Comparative Analysis of Simulated Annealing and Variable Neighborhood Search in the ATCo Work-Shift Scheduling Problem
Año:2019
Áreas de investigación
  • Inteligencia artificial
Datos
Descripción
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.
Internacional
Si
JCR del ISI
Si
Título de la revista
Mathematics
ISSN
2227-7390
Factor de impacto JCR
1,105
Información de impacto
Base de datos: JCR, Categoría: Mathematics
Volumen
7
DOI
10.3390/math7070636
Número de revista
7
Desde la página
636
Hasta la página
654
Mes
JULIO
Ranking
75/313 (T1)
Esta actividad pertenece a memorias de investigación
Participantes
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Grupo de análisis de decisiones y estadística
  • Departamento: Inteligencia Artificial