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Stochastic Programming and Scenario Generation: Decision Modelling Simulation and Information systems Perspective

Stochastic Programming brings together models of optimum resource allocation and models of randomness and thereby creates a robust decision making framework. The models of randomness with their finite, discrete realisations are known as Scenario Generators. In this thesis we consider alternative approaches to Scenario Generation (SG) in a generic form which can be used to formulate (a) two stage (static) and (b) multistage dynamic SP models. We also investigate the modelling structure and software issues of integrating a scenario generator with an optimisation model to construct Stochastic Programming recourse problems. We consider how the Expected Value (EV) and Stochastic Programming (SP) decision model results can be evaluated within a descriptive modelling framework of simulation. Illustrative examples and computational results are given in support of our investigation. We set out a blueprint for a SG model library; its open architecture and how it is used in decision models and in simulation.
The major contribution of this research reported in this thesis is the development of a two phase modelling paradigm which connects forward looking (ex ante) decision making with wait and see (ex post) results analysis. The research also connects these modelling paradigms to the analytic information systems, which are used by decision makers of diverse organisations. This latter aspect, which integrates information engineering with analytics decision models taking into consideration uncertainty and risk, is a contribution to knowledge. The scenario generation, the simulation extension to SAMPL and the design of the SG library encapsulate a number of novel features and are also a contribution to knowledge.

Mostra/Nascondi contenuto.
10 1. Stochastic Programming Background 1.1. Decision Models and Descriptive Models The success of Linear Programming and Mixed Integer Programming has in turn fuelled considerable interest in the study of Stochastic Programming (SP) and more recently Stochastic Mixed Integer Programming. (SMIP). SP has wide ranging applications in situations where uncertainty and risk are taken into consideration in the planning process. A natural evolution of the SP and SMIP models are to bring together (optimum) decision making with simulation evaluation. Two basic modelling paradigms come together in stochastic programming. These are: (a) model of optimum resource allocation and (b) model of randomness respectively. It is well established that in the realm of OR/MS and its contribution to managerial decision making four categories of models are of interest. For a detailed discussion see Mitra (1988): ξ Descriptive Models as defined by a set of mathematical relations, which simply predicts how a physical, industrial or a social system may behave. ξ Normative Models constitute the basis for (quantitative) decision making by a superhuman following an entirely rational that is, logically scrupulous set of arguments. Hence quantitative decision problems and idealised decision makers are postulated in order to define these models. ξ Prescriptive Models involve systematic analysis of problems as carried out by normally intelligent persons who apply intuition and judgement. Two distinctive features of this approach are uncertainty analysis and preference (or value or utility) analysis. ξ Decision Models are in some sense a derived category as they combine the concept underlying the normative models and prescriptive models.

Tesi di Dottorato

Dipartimento: Mathematics

Autore: Nico Di Domenica Contatta »

Composta da 140 pagine.

 

Questa tesi ha raggiunto 873 click dal 28/06/2005.

 

Consultata integralmente 4 volte.

Disponibile in PDF, la consultazione è esclusivamente in formato digitale.