Multi-Objective Optimization of Pilots’ FFS Recurrent Training Problem


Two multi-objective programming models are built to describe Pilots’ full flight simulator (FFS) recurrent training (PFRT) problem. There are two objectives for them. One is the best matching of captains and copilots in the same aircraft type. The other is that pilots could attend his training courses at proper month. Usually the two objectives are conflicting because there are copilots who will promote to captains or transfer to other aircraft type and new trainees will enter the company every year. The main theme in the research is to find the final non-inferior solutions of PFRT problem. Graph models are built to help to analyze the problem and we convert the original problem into a longest-route problem with weighted paths. An algorithm is designed with which we can obtain all the non-inferior solutions by a graphic method. A case study is present to demonstrate the effectiveness of the algorithm as well.

Share and Cite:

M. Gao, "Multi-Objective Optimization of Pilots’ FFS Recurrent Training Problem," Engineering, Vol. 4 No. 10, 2012, pp. 662-667. doi: 10.4236/eng.2012.410084.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] B. C. Pan and Z. Y. Song, et al., “The Research on Optimization Model for Recurrent Training Plan of Aviator Full Flight Simulators,” Chinese Journal of Management Science. Vol. 16, No. 2, 2008, pp. 69-75.
[2] W. B. Liu, S. Z. Zhang and B. L. Shi, “Solution of Pilot Simulator Timetable Problem Based on Genetic Algorithm,” Computer Engineering. Vol. 37, No. 15, 2011, pp.140-142.
[3] I. H. Toroslu and Y. Arslanoglu, “Genetic algorithm for the personnel assignment problem with multiple objectives,” Information Sciences.Vol. 177, No. 3, 2007, pp. 787-803.
[4] A. M. Geoffrion, “Proper Efficiency and the Theory of Vector Maximization,” Journal of Mathematical Analysis and Applications, Vol. 22, No. 3, 1968, pp. 618-630.
[5] J. F. Cordeau and M. Maischberger, “A parallel iterated tabu search heuristic for vehicle routing problems,” Computers & Operations Research. Vol. 39, No. 9, 2012, pp. 2033-2050.
[6] Q. H. Wu and J. K. Hao, et al., “Multi-neighborhood tabu search for the maximum weight clique problem,” Annals of Operations Research, Vol. 196, No 1, 2012, pp. 611-634.
[7] R. Kia and A. Baboli, et al., “Solving a group layout design model of a dynamic cellular manufacturing system with alternative process routings, lot splitting and flexible reconfiguration by simulated annealing,” Computers & Operations Research. Vol. 39, No. 11, 2012, pp. 2642-2658.
[8] Kalyanmoy Deb, “Multi-Objective Optimization using EvolutionaryAlgorithms,” New York, 2001.
[9] M. Cruz-Ramirez and C. Hervas-Martinez, et al., “Multi-objective evolutionary algorithm for donor-recipient decision system in liver transplants,” European Journal of Operational Research. Vol. 222, No. 2, 2012, pp. 317-327.
[10] A. Thapar and D. Pandey, et al., “Satisficing solutions of multi-objective fuzzy optimization problems using genetic algorithm,” Applied Soft Computing, Vol. 12, No. 8, 2012, pp. 2178-2187.
[11] E. G. Talbi and M. Basseur, et al., “Multi-objective optimization using metaheuristics: non-standard algorithms,” International Transactions in Operational Research, Vol. 19, No. 1-2, 2012, pp. 283-305.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.