Accordingly, we develop a multi-stage stochastic program, and model disruptions' effect on facilities' capacity.
In the discrete-time case, the problem is formulated as a multi-stage stochastic optimization model.
The problem is formulated as a multi-stage stochastic mixed-integer linear programming problem.
A multi-stage stochastic programming is proposed from the demand side in order to understand the rational decisions.
To tackle this problem, a scenario based multi-stage stochastic mixed integer linear programming (MILP) formulation is proposed.
A scenario based multi-stage stochastic mixed integer linear programming (MILP) model is employed to address the problem.
Although two-stage and multi-stage stochastic programming are among the key methodologies to address multi-period problems under uncertainty, they might not provide adequate solutions under limited flexibility by resulting in either fully static or dynamic policies.
In this paper, we present a multi-stage stochastic optimization formulation to consider potential future demand scenarios (obtained from past data).
We propose a multi-stage stochastic mixed-integer programming approach to the problem as well as a Lagrangian Heuristic procedure to attain reasonably well bounded feasible solutions.
This paper proposes a multi-stage stochastic model of a PEV aggregation agent to participate in day-ahead and intraday electricity markets.
The problem is formulated in a stochastic mixed integer linear programming (SMILP) decision making form as a multi-stage stochastic program.
