This brief but very well-written new book seems primarily designed as a supplementary, quantitatively-oriented text for use in a first-year, graduate-level course on capital investment planing. While the first part of the book presents clear but cursory coverage of most of the standard topics traditionally covered in such course, the books main strength and distinctive feature, constituting more than half of the whole volume, is its second part, giving relatively detailed coverage of mathematical programming models for capital budgeting and related topics.
The general first part consists of just four chapters: 1) Introduction, 2) Evaluation of Capital Investments, 3) Single-project Risk Analysis, and 4) Risk from the Company and Share holder Perspective. Among the standard matters briefly but competently treated here are time value of money concepts and simple formulas, identification of relevant cash flows, project ranking, minimum attractive rates of return, sensitivity analysis, portfolio theory, the capital asset pricing model and arbitrage pricing theory. The discussions are clear, and ample use is made of numerical illustrations, but, understandable for such brief treatments, they are not deep in exploring underlying assumptions or potential extensions.
The books second part, on mathematical programming models, is very different, and its relatively thorough coverage of this subject, largely neglected in other capital budgeting books, is the main reason I have specified it as a supplementary text in my basic, graduate-level engineering economy course. The section consists of seven chapters: 5) Linear Programming Models for Capital Budgeting, 6) Dual Linear Programming and Capital Budgeting, 7) Duality in Horizon Models for Capital Budgeting,
Integer Programming Models for Capital Budgeting, 9) Goal-programming Models for Capital Budgeting, 10) Simulation and Capital Budgeting, and 11) Applying Some Advanced Mathematical programming Models to Capital Budgeting. Again, at least for the most part, the writing is clear and concise. The basic ideas of linear programming and Weingartners classic applications of it to the Lorie- Savage and horizon posture models are well-explained as are further applications of integer, goal, chance-contrained and quadratic programming. An additional, little-known procedure, developed by one of the authors for using Monte Carlo simulation in concert with a deterministic mathematical programming model, is also interestingly discussed.