Open Access Open Access  Restricted Access Subscription or Fee Access

A Hybrid Heuristic Method to Solve an Assignment Problem of Human Resource

(*) Corresponding author

Authors' affiliations



In this paper, we are going to present a hybrid heuristic method for solving the assignment problem of human resources with constraints for a multi-site enterprise that is a NP-hard combinatorial optimization problem. This method combines two approaches: the first one is the flowgraph approach for modeling and constructing the space solution. The Second one is the genetic approach to explore the optimal solution in the space solution. This hybridization allows us to obtain a good approximation of the optimum solution in a short time through a best choice of parameter's problem. The obtained results are being evaluated and compared with other results obtained by the ordinary Genetic Algorithm.
Copyright © 2015 Praise Worthy Prize - All rights reserved.


Hybrid Genetic; Heuristic; Graph Flow; Assignment Problem; Human Resources

Full Text:



Tkatek Said, Abdoun O., Abouchabaka J., Rafalia N.,“A Meta-heuristically Approach of the Spatial Assignment Problem of Human Resources in Multi-sites Enterprise”, International Journal of Computer Applications, Volume 77, N 7, September 2013.

Tkatek Said, Abdoun O., Abouchabaka J. “A Genetic Approach for a Reassignment Problem of Human Resources under Objective Constraint “Journée Scientifique en Sciences Appliquées JSSA'14 à Larache,Maroc.

H.Surbhi “Application of Genetic Algorithms in Machine learning” International Journal of Computer Science and Information Technologies, Vol. 2 (5) , 2011, 2412-2415 (IJCSIT)

C.Chu & E.Beasley,”A Genetic Algorithm For The Generalized Assignment Problem” Computers Ops Res. Vol. 24, No. 1, Pp. 17-23, 1997.

K.Harada&S.Yokoham&S.Kyoto, ”Hybridization of Genetic Algorithm and Local Search in Multiobjective Function” Optimization:GECCO’06, 2006, Seattle, Washington, USA.

Goldberg A., RAO S., « Length functions for flow computations », rapport n 97-055, 1997, NEC Research Institute, Inc.

M.Giovanni Felici, “Resource assignment with preference conditions“, European Journal of Operational Research – 2007.

F, Fulkerson D., Flows in Networks, Princeton University Press, 1962.

P.Audebaud&Souquet Amédée &RadetFrancois-Gérard « Les algorithmes génétiques » TE. 2004.

E.Agustin-Blas, S.Salcedo-Sanz& G. Ortiz-Garcia, &A.Portilla, « A hybrid grouping genetic algorithm for assigning students to preferred laboratory groups » Expert Systems with Applications 36 (2009) 7234–7241.

C.Patrick, D.Pataya “Hybrid Genetic Algorithms: Modeling and Application to The Quadratic Assignment Problem” Faculty of Business Administration, Assumption University Bangkok, Thailand

Misevicius “An improved hybrid genetic algorithm: new results for the quadratic assignment problem” Knowledge-Based Systems 17 (2004) 65–73, March 2004

Chi-Ming Lin a,b “Multi-criteria human resource allocation for solving multistage combinatorial optimization problems using multiobjective hybrid genetic algorithm” Expert Systems with Applications 34 (2008) 2480–2490

Hao. JK, “Métaheuristiques pour l’optimisation combinatoire et l’affectation sous contraintes”. Revue d’Intelligence Artificielle Vol : No. 1999

Eiselt H.A., Marianov V., “Employee positioning and workload allocation”, Computers & Operations Research, Vol. 35, No. 2, pp. 513-524.

C.Filho” Optimisation à base de flot de graphe pour l’acquisition d’informations 3D à partir de séquences d’images” ARTIS - GRAVIR / IMAG – INRIA.

Q.Yang&H.Guozhu&L.Li “Application of Genetic Algorithm on Human Resources Optimization” ICCTAE international Conference on Computer and Communication Technologies in Agriculture Engineering 2010.

M.Mirabi, A novel hybrid genetic algorithm to solve the sequence-dependent permutation flow-shop scheduling problem, The International Journal of Advanced Manufacturing Technology, March 2014, Volume 71, Issue 1, pp 429-437.

O. Roeva and S.Fidanova and M.Paprzycki “Influence of the Population Size on the Genetic Algorithm Performance in Case of Cultivation Process Modeling “ Proceedings of the 2013 Federated Conference on Computer Science and Information Systems pp. 371–376.

A. Piszcz and T. Soule, “Genetic programming: Optimal population sizes for varying complexity problems”, In Proceedings of the Genetic and Evolutionary Computation Conference, 2006, pp. 953–954.

F. G. Lobo and D. E. Goldberg, “The parameter less genetic algorithm in practice, Information Sciences Informatics and Computer Science, Vol. 167, No. 1-4, 2004, pp. 217–232.

F. G. Lobo and C. F. Lima, A review of adaptive population sizing schemes in genetic algorithms, In Proceedings of the Genetic and Evolutionary Computation Conference, 2005, pp. 228–234.

Nemhauser& Wolsey“ Minimum cost capacity installation for multi commodity network flows “, Programming 81 (1998) 177 199.

O. Abdoun, Abouchabaka J. and C. Tajani,”Hybridizing PSM and RSM Operator for Solving NP-Complete Problems: Application to Traveling Salesman Problem”. International Journal of Computer Science Issues, Vol. 9, No. 1.

O. Abdoun and J. Abouchabaka, “A Comparative Study of Adaptive Crossover Operators for Genetic Algorithms to Resolve the Travelling Salesman Problem”. International Journal of Computer Applications, Vol.31, N.11.

O. Abdoun, J. Abouchabaka and C. Tajani, “Analyzing the Performance of Mutation Operators to Solve the Traveling Salesman Problem”. International Journal of Emerging Sciences. Vol. 2. No.1.

Goldberg, A. V., and Tarjan, R. E.,A New Approach to the Maximum Flow Problem, Proceedings of the 18th ACM STOC, pp. 136–146, 1986.

P. A. Diaz-Gomez and D. F. Hougen, “Initial Population for Genetic Algorithms: A Metric Approach”, Proceedings of the 2007 International Conference on Genetic and Evolutionary Methods, GEM 2007, June 25-28, 2007, Las Vegas, Nevada, USA, Hamid R.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2023 Praise Worthy Prize