Mathematical Programming Under Uncertainty

In today’s world, many decision-making problems are surrounded by conditions of uncertainty. The main source of these conditions is the lack of information for decision-making. In classical approaches to mathematical programming, it is assumed that the problem data is predetermined; however, this assumption is irrational in practice. In most decision-making issues, we encounter some kind of inaccuracy and ambiguity in the data. Over the past few decades, various approaches to mathematical modeling of such problems have been proposed, each of which tries to manage and control the situation to help managers make realistic decisions due to different sources of uncertainty. Three types of stochastic, fuzzy, and robust planning are among the most important approaches that have been considered by researchers and modelers in this field and are highly efficient in solving organizational problems. This book describes the basics and application of these approaches based on the latest scientific achievements.