Geodesic
Chance constraint programming problems with parameters as exponential random variable
Mathematica Applicanda, Tome 49 (2021) no. 1, pp. 15-30.

Voir la notice de l'article provenant de la source Annales Societatis Mathematicae Polonae Series

Robust decision-making under uncertainty is deemed to be a crucial factor in many discipline and application areas. Also, management and measurement of risk is an important issue in almost all areas that require decisions to be made under uncertain information. Chance constrained programming (CCP) has been used for modelling and analysis of risks in a number of application domains. This paper presents a deterministic reduction of linear and nonlinear chance constraint programming problem using simple mathematical and statistical tools, assuming the coefficients of the decision variables in the chance constraints as exponential random variables. After converting the proposed chance constraint programming problem into a deterministic problem, a standard generic package is used to find the compromise solution and also comparison with some other technique is considered. Then MATLAB programming code is used to verify the validity of the solution for the original chance constraints.
DOI : 10.14708/ma.v49i1.7051
Classification : 90C15, 49M37, 65K05.
Mots-clés : Stochastic programming, chance constraint programming, Exponential random variable, Hypo exponential random variable, deterministic implicative reduction, non-linear programming 
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Vaskar Sarkar. Chance constraint programming problems with parameters as exponential random variable. Mathematica Applicanda, Tome 49 (2021) no. 1, pp.  15-30. doi : 10.14708/ma.v49i1.7051. http://geodesic.mathdoc.fr/articles/10.14708/ma.v49i1.7051/

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