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Models & Methods for Project Selection

Concepts from Management Science, Finance and Information Technology

Produktform: E-Buch Text Elektronisches Buch in proprietärem

systematically examines in this book treatment the latest work in the field of project selection modeling. The models presented are drawn from mathematical programming, decision theory, and finance. These models are examined in two categorical streams: the management science stream and the financial model stream. The book describes the assumptions and limitations of each model and provides appropriate solution methodologies. Its organization follows three main themes: * Chapters 1-3 investigate the effect of the choice of optimization criteria on the results of the portfolio optimization problem. This group of chapters examines the multiobjective linear programming approach, discusses the appropriate methods for adjusting for time and risk in the project selection problem, and expands on the discussion of optimization models and NPV. * Chapters 4-7 deal with uncertainty in the project selection problem. The models developed in this section are based on probability distribution assumptions or estimates and deal with uncertainty in some aspect of the project selection model. * These chapters deal with problems of non-linearity and interdependence as they arise in the project selection problem. The ability to handle non-linear problems allows the application of the methodology to a far wider range of problems. Similarly, the ability to model interdependence between projects - as in the Information Technology models - is an important step in generalization. Chapters 8, 9 and 10 present solution methodologies, which can be used to solve these most general project selection models.weiterlesen

Dieser Artikel gehört zu den folgenden Serien

Elektronisches Format: PDF

Sprache(n): Englisch

ISBN: 978-1-4615-0280-7 / 978-1461502807 / 9781461502807

Verlag: Springer US

Erscheinungsdatum: 06.12.2012

Seiten: 193

Autor(en): Samuel B. Graves, Jeffrey L. Ringuest

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