Management and measurement of risk is an important issue in almost all areas that require decisions to be made under uncertain information. Chance Constrained Programming (CCP) have been used for modelling and analysis of risks in a number of application domains. However, the resulting mathematical problems are non-trivial to represent using algebraic modelling languages and pose significant computational challenges due to their non-linear, non-convex, and the stochastic nature. We develop and implement C++ classes to represent such CCP problems. We propose a framework consisting of Genetic Algorithm and Monte-Carlo simulation in order to process the problems. The non-linear and non-convex nature of the CCP problems are processed using Genetic Algorithm, whereas the stochastic nature is addressed through simulation. The computational investigations have shown that the framework can efficiently represent and process a wide variety of the CCP problems.

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Advances in hardware, software techniques, and solution methods have made stochastic programming (SP) a viable optimization tool. In the field of linear programming (LP) and integer programming (IP), algebraic modeling languages (AMLs) are well established as aids to prototyping and have led to considerable gains in modeling productivity [7]. Unfortunately, there are not many modeling systems and AMLs which support the creation and investigation of SP models. In this chapter, we address some of the challenges related to the investigation of SP models using the existing software tools and introduce SPInE (stochastic programming integrated environment). The original design of SPInE [15] has been revised and updated. Our design objective is to create a flexible software system based on AMLs which also embeds several solution algorithms. Recently, the AMPL and the MPL modeling systems have been extended to include SPInE's functionalities [22,23]. The chapter is organized as follows. Section 8.2 introduces the classes of SP models which are supported by our system. In this section we also introduce an example, which is used throughout the chapter to illustrate the features of SPInE. Section 8.3 focuses on SAMPL and SMPL, which are extensions of the AMPL and MPL modeling languages, respectively. In section 8.4 we discuss the rationale underlying the parameter passing interface which connects the special purpose scenario generators to the SPInE system. In section 8.5, we give an overview of the solution algorithms implemented in SPInE and consider the performance and scale-up properties of these algorithms. In section 8.6 we describe the software architecture of SPInE: the illustrative example given in section 8.2 is used to explain the investigation of SP models with the SPInE system. Our aim is to make SPInE