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

Modeling and solving alternative financial solutions seeking

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.
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

[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

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

[1]

Xu Zhang, Xiang Li. Modeling and identification of dynamical system with Genetic Regulation in batch fermentation of glycerol. Numerical Algebra, Control & Optimization, 2015, 5 (4) : 393-403. doi: 10.3934/naco.2015.5.393

[2]

Baba Issa Camara, Houda Mokrani, Evans K. Afenya. Mathematical modeling of glioma therapy using oncolytic viruses. Mathematical Biosciences & Engineering, 2013, 10 (3) : 565-578. doi: 10.3934/mbe.2013.10.565

[3]

Ronald E. Mickens. Positivity preserving discrete model for the coupled ODE's modeling glycolysis. Conference Publications, 2003, 2003 (Special) : 623-629. doi: 10.3934/proc.2003.2003.623

[4]

Brandy Rapatski, James Yorke. Modeling HIV outbreaks: The male to female prevalence ratio in the core population. Mathematical Biosciences & Engineering, 2009, 6 (1) : 135-143. doi: 10.3934/mbe.2009.6.135

[5]

Christina Surulescu, Nicolae Surulescu. Modeling and simulation of some cell dispersion problems by a nonparametric method. Mathematical Biosciences & Engineering, 2011, 8 (2) : 263-277. doi: 10.3934/mbe.2011.8.263

[6]

Cicely K. Macnamara, Mark A. J. Chaplain. Spatio-temporal models of synthetic genetic oscillators. Mathematical Biosciences & Engineering, 2017, 14 (1) : 249-262. doi: 10.3934/mbe.2017016

[7]

Jon Aaronson, Dalia Terhesiu. Local limit theorems for suspended semiflows. Discrete & Continuous Dynamical Systems - A, 2020, 40 (12) : 6575-6609. doi: 10.3934/dcds.2020294

[8]

Habib Ammari, Josselin Garnier, Vincent Jugnon. Detection, reconstruction, and characterization algorithms from noisy data in multistatic wave imaging. Discrete & Continuous Dynamical Systems - S, 2015, 8 (3) : 389-417. doi: 10.3934/dcdss.2015.8.389

[9]

Seung-Yeal Ha, Shi Jin. Local sensitivity analysis for the Cucker-Smale model with random inputs. Kinetic & Related Models, 2018, 11 (4) : 859-889. doi: 10.3934/krm.2018034

[10]

Thierry Cazenave, Ivan Naumkin. Local smooth solutions of the nonlinear Klein-gordon equation. Discrete & Continuous Dynamical Systems - S, 2021, 14 (5) : 1649-1672. doi: 10.3934/dcdss.2020448

[11]

Namsu Ahn, Soochan Kim. Optimal and heuristic algorithms for the multi-objective vehicle routing problem with drones for military surveillance operations. Journal of Industrial & Management Optimization, 2021  doi: 10.3934/jimo.2021037

[12]

Ardeshir Ahmadi, Hamed Davari-Ardakani. A multistage stochastic programming framework for cardinality constrained portfolio optimization. Numerical Algebra, Control & Optimization, 2017, 7 (3) : 359-377. doi: 10.3934/naco.2017023

[13]

Luke Finlay, Vladimir Gaitsgory, Ivan Lebedev. Linear programming solutions of periodic optimization problems: approximation of the optimal control. Journal of Industrial & Management Optimization, 2007, 3 (2) : 399-413. doi: 10.3934/jimo.2007.3.399

[14]

Zhiming Guo, Zhi-Chun Yang, Xingfu Zou. Existence and uniqueness of positive solution to a non-local differential equation with homogeneous Dirichlet boundary condition---A non-monotone case. Communications on Pure & Applied Analysis, 2012, 11 (5) : 1825-1838. doi: 10.3934/cpaa.2012.11.1825

[15]

Hong Seng Sim, Wah June Leong, Chuei Yee Chen, Siti Nur Iqmal Ibrahim. Multi-step spectral gradient methods with modified weak secant relation for large scale unconstrained optimization. Numerical Algebra, Control & Optimization, 2018, 8 (3) : 377-387. doi: 10.3934/naco.2018024

[16]

Abdulrazzaq T. Abed, Azzam S. Y. Aladool. Applying particle swarm optimization based on Padé approximant to solve ordinary differential equation. Numerical Algebra, Control & Optimization, 2021  doi: 10.3934/naco.2021008

[17]

Mohsen Abdolhosseinzadeh, Mir Mohammad Alipour. Design of experiment for tuning parameters of an ant colony optimization method for the constrained shortest Hamiltonian path problem in the grid networks. Numerical Algebra, Control & Optimization, 2021, 11 (2) : 321-332. doi: 10.3934/naco.2020028

[18]

Reza Lotfi, Yahia Zare Mehrjerdi, Mir Saman Pishvaee, Ahmad Sadeghieh, Gerhard-Wilhelm Weber. A robust optimization model for sustainable and resilient closed-loop supply chain network design considering conditional value at risk. Numerical Algebra, Control & Optimization, 2021, 11 (2) : 221-253. doi: 10.3934/naco.2020023

2019 Impact Factor: 1.366

Metrics

  • PDF downloads (41)
  • HTML views (0)
  • Cited by (0)

[Back to Top]