S. K. Bordoloi and H. Matsuo, Human resource planning in knowledge-intensive operations: A model for learning with stochastic turnover, European Journal of Operational Research, vol.130, issue.1, pp.169-189, 2001.
DOI : 10.1016/S0377-2217(00)00049-7

M. Burger, A. Lorz, and M. Wolfram, On a Boltzmann Mean Field Model for Knowledge Growth, SIAM Journal on Applied Mathematics, vol.76, issue.5, pp.1799-1818, 2016.
DOI : 10.1137/15M1018599
URL : https://hal.archives-ouvertes.fr/hal-01163491

J. V. Den-bergh, J. Belin, P. D. Bruecker, E. Demeulemeester, and L. D. Boeck, Personnel scheduling: A literature review, European Journal of Operational Research, vol.226, issue.3, pp.367-385, 2013.
DOI : 10.1016/j.ejor.2012.11.029

U. Klehe, J. Zikic, A. E. Vianen, and I. E. Pater, Career adaptability, turnover and loyalty during organizational downsizing, Journal of Vocational Behavior, vol.79, issue.1, pp.217-229, 2011.
DOI : 10.1016/j.jvb.2011.01.004
URL : http://geb.uni-giessen.de/geb/volltexte/2012/8523/pdf/Klehe-career_adaptability.pdf

T. Komarudin, M. De-feyter, G. Guerry, and . Vanden-berghe, Balancing desirability and promotion steadiness in partially stochastic manpower planning systems, Communications in Statistics - Theory and Methods, vol.20, issue.6, pp.1805-1818, 2016.
DOI : 10.1057/palgrave.jors.2602522

J. S. Henderson, Stochastic optimal control of internal hierarchical labor markets, Journal of Optimization Theory and Applications, vol.3, issue.1, pp.99-115, 1980.
DOI : 10.1007/BF00934592

E. Ribes, K. Touahri, and B. Perthame, Employee turnover prediction and retention policies design: a case study, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01556746

R. Qin, D. A. Nembhard, and W. L. Ii, Workforce flexibility in operations management, Surveys in Operations Research and Management Science, pp.19-33, 2015.
DOI : 10.1016/j.sorms.2015.04.001

B. Wild and C. Schneewei, Manpower capacity planning ??? A hierarchical approach, International Journal of Production Economics, vol.30, issue.31, pp.95-106, 1993.
DOI : 10.1016/0925-5273(93)90084-X

P. R. Harper, N. H. Powell, and J. E. Williams, Modelling the size and skill-mix of hospital nursing teams, Journal of the Operational Research Society, vol.18, issue.4, pp.768-779, 2010.
DOI : 10.7748/ns2004.03.18.28.13.c3572

B. Perthame, Transport equations in biology, Frontiers in Mathematics, 2007.

M. E. Gurtin and R. C. Maccamy, Non-linear age-dependent population dynamics, Archive for Rational Mechanics and Analysis, vol.54, issue.3, pp.281-300, 1974.
DOI : 10.1007/BF00250793
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.176.2992

M. Farkas, On the stability of stationary age distributions, Applied Mathematics and Computation, vol.131, issue.1, pp.107-123, 2002.
DOI : 10.1016/S0096-3003(01)00131-X

M. Doumic, B. Perthame, E. Ribes, D. Salort, and N. Toubiana, Toward an integrated workforce planning framework using structured equations, European Journal of Operational Research, vol.262, issue.1, pp.217-230, 2017.
DOI : 10.1016/j.ejor.2017.03.076
URL : https://hal.archives-ouvertes.fr/hal-01343368

F. Bouchut, Nonlinear stability of finite volume methods for hyperbolic conservation laws and well-balanced schemes for sources, Series Frontiers in Mathematics, 2004.

R. J. Leveque, Numerical methods for conservation laws, 1992.

L. Scrucca, GA: A package for genetic algorithms in R, Journal of Statistical Software, vol.53, issue.4, pp.1-37, 2013.

M. Kumar, M. Husian, N. Upreti, and D. Gupta, Genetic algorithm: Review and application, International Journal of Information Technology and Knowledge Management, vol.2, issue.2, pp.451-454, 2010.

C. B. Lucasius, J. , and G. Kateman, Understanding and using genetic algorithms Part 1. Concepts, properties and context, reprint in: Carlos B. Lucasius, Jr., Towards Genetic Algorithm Methodology in Chemometrics, pp.1-33, 1993.
DOI : 10.1016/0169-7439(93)80079-W

C. Prendergast, The Provision of Incentives in Firms, Journal of Economic Literature, vol.37, issue.1, pp.7-63, 1999.
DOI : 10.1257/jel.37.1.7