We present a framework for solving some types of 0 − 1 multi-stage scheduling/planning problems under uncertainty in the objective function coefficients and the right-hand-side. A scenario analysis scheme with full recourse is used. The solution offered for each scenario group at each stage takes into account all scenarios but without subordinating to any of them. The constraints are modelled by a splitting variables representation via scenarios. So, a 0 − 1 model for each scenario is consid-ered plus the non-anticipativity constraints that equate the 0 − 1 variables from the same group of scenarios in each stage. The mathematical representation of the model is very amenable for the proposed framework to deal with the 0 − 1 character of the...
This thesis investigates the following question: Can supervised learning techniques be successfully ...
The field of multi-stage stochastic programming provides a rich modelling framework to tackle a broa...
The paper suggests a possible cooperation between stochastic programming and optimal control for the...
Preprint submitted to Computers & Operations ResearchIn this paper we present a parallelizable schem...
This dissertation addresses the modeling and solution of mixed-integer linear multistage stochastic ...
We present an algorithmic approach for solving two-stage stochastic mixed 0-1 problems. The first st...
The objective of this paper is to describe a method of implementing multi-period two- and multi-stag...
Stochastic programming is concerned with decision making under uncertainty, seeking an optimal polic...
Stochastic programming is concerned with decision making under uncertainty, seeking an optimal polic...
To model combinatorial decision problems involving uncertainty and probability, we extend the stocha...
In this work we extend to the multistage case two recent risk averse measures for two-stage stochast...
A parallel matheuristic algorithm is presented as a spin-off from the exact Branch-and-Fix Coordinat...
International audienceWe consider an uncapacitated multi-item multi-echelon lot-sizing problem withi...
We present a framework for solving the strategic problem of assigning retailers to facilities in a m...
In the present paper, we study general two-stage stochastic mixed 0-1 problems, in the context of cl...
This thesis investigates the following question: Can supervised learning techniques be successfully ...
The field of multi-stage stochastic programming provides a rich modelling framework to tackle a broa...
The paper suggests a possible cooperation between stochastic programming and optimal control for the...
Preprint submitted to Computers & Operations ResearchIn this paper we present a parallelizable schem...
This dissertation addresses the modeling and solution of mixed-integer linear multistage stochastic ...
We present an algorithmic approach for solving two-stage stochastic mixed 0-1 problems. The first st...
The objective of this paper is to describe a method of implementing multi-period two- and multi-stag...
Stochastic programming is concerned with decision making under uncertainty, seeking an optimal polic...
Stochastic programming is concerned with decision making under uncertainty, seeking an optimal polic...
To model combinatorial decision problems involving uncertainty and probability, we extend the stocha...
In this work we extend to the multistage case two recent risk averse measures for two-stage stochast...
A parallel matheuristic algorithm is presented as a spin-off from the exact Branch-and-Fix Coordinat...
International audienceWe consider an uncapacitated multi-item multi-echelon lot-sizing problem withi...
We present a framework for solving the strategic problem of assigning retailers to facilities in a m...
In the present paper, we study general two-stage stochastic mixed 0-1 problems, in the context of cl...
This thesis investigates the following question: Can supervised learning techniques be successfully ...
The field of multi-stage stochastic programming provides a rich modelling framework to tackle a broa...
The paper suggests a possible cooperation between stochastic programming and optimal control for the...