Abstract We consider the problem of scheduling under demand uncertainty a multiproduct batch plant represented through the State Task Network. Given a scheduling horizon consisting of several time-periods in which product demands are placed, the objective is to select a schedule that maximizes the expected profit. We present a multistage stochastic Mixed Integer Linear Programming (MILP) model,...

Risk-averse multistage stochastic programs appear in multiple areas and are challenging to solve. Stochastic Dual Dynamic Programming (SDDP) is a well-known tool address such problems under time-independence assumptions. We show how derive dual formulation for these apply an SDDP algorithm, leading converging deterministic upper bounds risk-averse problems.

• A stagewise decomposition algorithm for large-scale stochastic program is proposed. The approximates value functions by predetermined parametric forms. fits the gradients of function to those function. Numerical examples show computational efficiency proposed algorithm. called “value gradient learning” (VFGL) multistage convex programs. VFGL finds parameter values that best fit within a given...

Power generation expansion planning needs to deal with future uncertainties carefully, given that the invested generation assets will be in operation for a long time. Many stochastic programming models have been proposed to tackle this challenge. However, most previous works assume predetermined future uncertainties (i.e., fixed random outcomes with given probabilities). In several recent studi...

The stochastic nature of renewable energy sources has increased the need for intraday trading in electricity markets. Intradaymarkets provide possibility to market participants modify their positions based on updated forecasts. In this paper, we propose a multistage programming approach model Virtual Power Plant (VPP), comprising thermal, wind and hydro power plants, Continuous Intraday (CID) m...

In this paper we consider the notion of rectangularity of a set of probability measures, introduced in Epstein and Schneider [4], from a somewhat different point of view. We define rectangularity as a property of dynamic decomposition of a distributionally robust stochastic optimization problem and show how it relates to the modern theory of coherent risk measures. Consequently we discuss robus...

1 JITKA DUPAČOVÁ The paper gives a brief introduction into the problems of multistage stochastic program­ ming with emphasis on the modeling issues (Section 2) and on the contemporary numerical advances (Section 3). Extensive classified bibliography is contained in the last Section.

We propose a generic method for obtaining quickly good upper bounds on the minimal value of a multistage stochastic program. The method is based on the simulation of a feasible decision policy, synthesized by a strategy relying on any scenario tree approximation from stochastic programming and on supervised learning techniques from machine learning.