Irrigation water management is crucial for agricultural production and livelihood security in many regions and countries throughout the world. Over the past decades, controversial and conflictladen water-allocation issues among competing municipal, industrial and agricultural interests have raised increasing concerns. Particularly, growing population, varying natural conditions and shrinking wa...

Optimisation under uncertainty has always been a focal point within the Process Systems Engineering (PSE) research agenda. In particular, efficient manipulation of large amount data for uncertain parameters constitutes crucial condition effectively tackling stochastic programming problems. this context, work proposes new data-driven Mixed-Integer Linear Programming (MILP) model Distribution & M...

Stochastic integer programming is more complicated than stochastic linear programming, as will be explained for the case of the two-stage stochastic programming model. A survey of the results accomplished in this recent field of research is given.

This document shows how to model two-stage stochastic linear programming problems in a GAMS environment. We will demonstrate using a small example, how GAMS can be used to formulate and solve this model as a large LP or using specialized stochastic solvers such as OSL-SE and DECIS. Finally a tailored implementation of the Benders Decomposition algorithm written in GAMS is used to solve the model.

The purpose of the paper is to present existing and discuss modified optimization models and solution techniques which are suitable for engineering decision-making problems containing random elements with emphasis on two decision stages. The developed approach is called two-stage stochastic programming and the paper links motivation, applicability, theoretical remarks, transformations, input da...