A multiobjective evolutionary algorithm for the task based sailor assignment problem

Authors:
Dipankar Dasgupta;Fernando Nino;Deon Garrett;Koyel Chaudhuri;Soujanya Medapati;Aishwarya Kaushal;James Simien
Affiliations:
University of Memphis, Memphis, TN, USA;National University of Colombia, Bogota, Colombia;University of Memphis, Memphis, TN, USA;University of Memphis, Memphis, TN, USA;University of Memphis, Memphis, TN, USA;University of Memphis, Memphis, TN, USA;Navy Personnel Research, Studies, and Technology, Millington, TN, USA
Venue:
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Year:
2009

Citing 5
Cited 0

An approximation algorithm for the generalized assignment problem

Mathematical Programming: Series A and B
Genetic algorithms for the sailor assignment problem

GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
An efficient approximation for the generalized assignment problem

Information Processing Letters
A comparison of multiobjective evolutionary algorithms with informed initialization and kuhn-munkres algorithm for the sailor assignment problem

Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper investigates a multiobjective formulation of the United States Navy's Task based Sailor Assignment Problem and examines the performance of a multiobjective evolutionary algorithm (MOEA), called NSGA-II, on large instances of this problem. Our previous work [3, 5, 4], consider the sailor assignment problem (SAP) as a static assignment, while the present work assumes it as a time dependent multitask SAP, making it a more complex problem, in fact, an NP-complete problem. Experimental results show that the presented genetic-based solution is appropriate for this problem.