A new heuristic approach for Stochastic Programming Problems (SPP) is presented. Here the heuristic idea is based on an analogy of SPP with the problem of determination of the centre of gravity in certain physical systems. A general purpose algorithm is presented for SPPs with discrete random coefficients. An analysis of the efficiency and reliability of this heuristic approach is presented along with computational results on some test problems
In practice we often have to solve optimization problems with several criteria. These problems are c...
We propose an alternative apporach to stochastic programming based on Monte-Carlo sampling and stoch...
Stochastic programming is the subfield of mathematical programming that considers optimization in th...
10.1016/j.ejor.2004.11.006European Journal of Operational Research1723761-782EJOR
This report presents an approach to stochastic programming. It treats mainly the difficulties arisin...
Abstract. The stochastic versions of classical discrete optimal control problems are formulated and ...
Stochastic methods are present in our daily lives, especially when we need to make a decision based ...
A heuristic algorithm is proposed for a class of stochastic discrete-time continuous-variable dynami...
We propose an alternative approach to stochastic programming based on Monte-Carlo sampling and stoch...
A new heuristic method is presented for solving a class of constrained probabilistic optimization pr...
This book presents the details of the BONUS algorithm and its real world applications in areas like ...
The purpose of the paper is to introduce various stochastic programs and related deterministi
We consider stochastic programming problems with probabilistic constraints involving random variable...
Abstract Stochastic Programming (SP) was first introduced by George Dantzig in the 1950’s. Since tha...
This text gives a comprehensive coverage of how optimization problems involving decisions and uncert...
In practice we often have to solve optimization problems with several criteria. These problems are c...
We propose an alternative apporach to stochastic programming based on Monte-Carlo sampling and stoch...
Stochastic programming is the subfield of mathematical programming that considers optimization in th...
10.1016/j.ejor.2004.11.006European Journal of Operational Research1723761-782EJOR
This report presents an approach to stochastic programming. It treats mainly the difficulties arisin...
Abstract. The stochastic versions of classical discrete optimal control problems are formulated and ...
Stochastic methods are present in our daily lives, especially when we need to make a decision based ...
A heuristic algorithm is proposed for a class of stochastic discrete-time continuous-variable dynami...
We propose an alternative approach to stochastic programming based on Monte-Carlo sampling and stoch...
A new heuristic method is presented for solving a class of constrained probabilistic optimization pr...
This book presents the details of the BONUS algorithm and its real world applications in areas like ...
The purpose of the paper is to introduce various stochastic programs and related deterministi
We consider stochastic programming problems with probabilistic constraints involving random variable...
Abstract Stochastic Programming (SP) was first introduced by George Dantzig in the 1950’s. Since tha...
This text gives a comprehensive coverage of how optimization problems involving decisions and uncert...
In practice we often have to solve optimization problems with several criteria. These problems are c...
We propose an alternative apporach to stochastic programming based on Monte-Carlo sampling and stoch...
Stochastic programming is the subfield of mathematical programming that considers optimization in th...