American Institute of Mathematical Sciences

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January  2015, 11(1): 145-170. doi: 10.3934/jimo.2015.11.145

Modeling and solving alternative financial solutions seeking

Emmanuel Frénod 1, , Jean-Philippe Gouigoux 2, and  Landry Touré 2,

1. 

Université de Bretagne-Sud, UMR 6205, LMBA, F-56000 Vannes, France
2. 

MGDIS, Parc d'Innovation de Bretagne Sud, F-56038 Vannes, France, France

Received  April 2013 Revised  December 2013 Published  May 2014

In this paper we model the working of local community finances. As a result of this first step, we obtain a systemic model that is used to formalize the problem of Alternative Financial Solutions Seeking, which consists in building a collection of Alternative Multi-Year Prospective Budgets from two Multi-Year Prospective Budgets built by a finance expert. The modeling and formalization steps are led in a way that allows us to implement a software code for Alternative Financial Solutions Seeking based on a Genetic Like Algorithm.
Keywords: systemic modeling, optimization, Finance modeling, local communities, genetic algorithms..
Mathematics Subject Classification: Primary: 90C15, 62P20, 68Uxx, 93A30; Secondary: 91G4.
Citation: Emmanuel Frénod, Jean-Philippe Gouigoux, Landry Touré. Modeling and solving alternative financial solutions seeking. Journal of Industrial & Management Optimization, 2015, 11 (1) : 145-170. doi: 10.3934/jimo.2015.11.145
References:
[1]

La Qualité Comptable au service d'une gestion performante des collectivités locales - Guide des bonnes pratiques Num 18, Technical report,, Académie des sciences et techniques comptables financières., ().   Google Scholar

[2]

Annexe Num 1: Plan de comptes développé des communes de 500 habitants et plus au 1ier janvier 2009, Technical report, Plan M14 de Comptabilité, French State Secretary for Finance,, (, (2009).   Google Scholar

[3]

D. Beasley, D. Bull and R. Martin, An overview of genetic algorithms. part 1, fundamentals,, University Computing, 15 (1993), 58.   Google Scholar

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D. Beasley, D. Bull and R. Martin, An overview of genetic algorithms. part 2, research topics,, University Computing, 15 (1993), 170.   Google Scholar

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C. Castro, C. Antònio and L. Sousa, Optimisation of shape and process parameters in metal forging using genetic algorithms,, Journal of Materials Processing Technology, 146 (2004), 356.  doi: 10.1016/j.jmatprotec.2003.11.027.  Google Scholar

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L. Davis, Handbook of Genetic Algorithms,, Van Nostrand Reinhold, (1991).   Google Scholar

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K. De Jong, Proceedings of the Evolutionary Algorithms in Engineering Computer Science (EUROGEN99), chapter Evolutionary computation: Recent developments and open issues, 43-54,, University of Jyvskyl Finland, (1999).   Google Scholar

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E. Fama, Market efficiency, long-term returns, and behavioral finance,, Journal of Financial Economics, 49 (1998), 283.  doi: 10.2139/ssrn.15108.  Google Scholar

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P. Fourie and A. Groenwold, The particle swarm optimization algorithm in size and shape optimization,, Structural and Multidisciplinary Optimization, 23 (2002), 259.  doi: 10.1007/s00158-002-0188-0.  Google Scholar

[10]

D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning,, 1st edition, (1989), 07.   Google Scholar

[11]

V. Goodman and J. G. Stampfli, The Mathematics of Finance: Modeling and Hedging,, American Mathematical Society, (2001).   Google Scholar

[12]

K. Ilinski, Physics of Finance : Gauge Modelling in Non-Equilibrium Pricing,, Wiley, (2001).   Google Scholar

[13]

C. Mattheck and S. Burkhardt, A new method of structural shape optimization based on biological growth,, International Journal of Fatigue, 12 (1990), 185.  doi: 10.1016/0142-1123(90)90094-U.  Google Scholar

[14]

R. Musgrave, The Theory of Public Finance : A Study in Public Economy,, McGraw-Hill, (1959).   Google Scholar

[15]

H. S. Rosen, Public finance,, The Encyclopedia of Public Choice, (2004), 252.  doi: 10.1007/978-0-306-47828-4_21.  Google Scholar

[16]

Chen S.-H. (ed.), Genetic Algorithms and Genetic Programming in Computational Finance,, Kluwer Academic Publishers, (2002).   Google Scholar

[17]

C. Soh and J. Yang, Fuzzy controlled genetic algorithm search for shape optimization,, Journal of Computing in Civil Engineering, 10 (1996), 143.  doi: 10.1061/(ASCE)0887-3801(1996)10:2(143).  Google Scholar

[18]

C. Tiebout, A pure theory of local expenditures,, Journal of Political Economy, 64 (1956), 416.  doi: 10.1086/257839.  Google Scholar

show all references

References:
[1]

La Qualité Comptable au service d'une gestion performante des collectivités locales - Guide des bonnes pratiques Num 18, Technical report,, Académie des sciences et techniques comptables financières., ().   Google Scholar

[2]

Annexe Num 1: Plan de comptes développé des communes de 500 habitants et plus au 1ier janvier 2009, Technical report, Plan M14 de Comptabilité, French State Secretary for Finance,, (, (2009).   Google Scholar

[3]

D. Beasley, D. Bull and R. Martin, An overview of genetic algorithms. part 1, fundamentals,, University Computing, 15 (1993), 58.   Google Scholar

[4]

D. Beasley, D. Bull and R. Martin, An overview of genetic algorithms. part 2, research topics,, University Computing, 15 (1993), 170.   Google Scholar

[5]

C. Castro, C. Antònio and L. Sousa, Optimisation of shape and process parameters in metal forging using genetic algorithms,, Journal of Materials Processing Technology, 146 (2004), 356.  doi: 10.1016/j.jmatprotec.2003.11.027.  Google Scholar

[6]

L. Davis, Handbook of Genetic Algorithms,, Van Nostrand Reinhold, (1991).   Google Scholar

[7]

K. De Jong, Proceedings of the Evolutionary Algorithms in Engineering Computer Science (EUROGEN99), chapter Evolutionary computation: Recent developments and open issues, 43-54,, University of Jyvskyl Finland, (1999).   Google Scholar

[8]

E. Fama, Market efficiency, long-term returns, and behavioral finance,, Journal of Financial Economics, 49 (1998), 283.  doi: 10.2139/ssrn.15108.  Google Scholar

[9]

P. Fourie and A. Groenwold, The particle swarm optimization algorithm in size and shape optimization,, Structural and Multidisciplinary Optimization, 23 (2002), 259.  doi: 10.1007/s00158-002-0188-0.  Google Scholar

[10]

D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning,, 1st edition, (1989), 07.   Google Scholar

[11]

V. Goodman and J. G. Stampfli, The Mathematics of Finance: Modeling and Hedging,, American Mathematical Society, (2001).   Google Scholar

[12]

K. Ilinski, Physics of Finance : Gauge Modelling in Non-Equilibrium Pricing,, Wiley, (2001).   Google Scholar

[13]

C. Mattheck and S. Burkhardt, A new method of structural shape optimization based on biological growth,, International Journal of Fatigue, 12 (1990), 185.  doi: 10.1016/0142-1123(90)90094-U.  Google Scholar

[14]

R. Musgrave, The Theory of Public Finance : A Study in Public Economy,, McGraw-Hill, (1959).   Google Scholar

[15]

H. S. Rosen, Public finance,, The Encyclopedia of Public Choice, (2004), 252.  doi: 10.1007/978-0-306-47828-4_21.  Google Scholar

[16]

Chen S.-H. (ed.), Genetic Algorithms and Genetic Programming in Computational Finance,, Kluwer Academic Publishers, (2002).   Google Scholar

[17]

C. Soh and J. Yang, Fuzzy controlled genetic algorithm search for shape optimization,, Journal of Computing in Civil Engineering, 10 (1996), 143.  doi: 10.1061/(ASCE)0887-3801(1996)10:2(143).  Google Scholar

[18]

C. Tiebout, A pure theory of local expenditures,, Journal of Political Economy, 64 (1956), 416.  doi: 10.1086/257839.  Google Scholar

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