ABSTRACT
Chapter 10 continues the treatment of uncertainty and focuses on robust optimization (RO); the decision maker assumes and develops different scenarios in line with the uncertain environment, which requires stochastic treatment beyond the arithmetic mean or the expectation value. Stochastic programming (SP) and related attributes for decision-making are presented, noting that the generalization of deterministic models to the stochastic framework drives either qualitative or quantitative improvements. In addition, robustness both on models and on solutions is achieved through RO approaches; namely, RO is able to promote both the assessment and the treatment of risk. A case study is described, including pertinent RO procedures associated with typical economic estimators and industry-based parameters;, additionally, tables summarizing the approaches and methods described throughout the book are presented.
