Table Of ContentMODELS & METHODS FOR
PROJECT SELECTION
Concepts from Management Science, Finance and
Information Technology
INTERNATIONAL SERIES IN
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Liu, B. & Esogbue, AO. / DECISION CRITERIA AND OPTIMAL INVENTORY PROCESSES
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MODELS & METHODS FOR
PROJECT SELECTION
Concepts from Management Science, Finance
and Information Technology
by
Samuel B. Graves
Boston College
Jeffrey L. Ringuest
Boston College
with
Andres L. Medaglia
SPRINGER SCIENCE+BUSINESS MEDIA, LLC
Library of Congress Cataloging-in-Publication Data
A C.I.P. Catalogue record for this book is available from the Library of Congress.
Graves, Samuel B. & Ringuest, Jeffrey L. / MODELS & METHODS FOR PROJECT
SELECTION: Concepts /rom Management Science, Finance & Information Technology
ISBN 978-1-4613-5001-9 ISBN 978-1-4615-0280-7 (eBook)
DOI 10.1007/978-1-4615-0280-7
Copyright © 2003 by Springer Science+Business Media New York
Originally published by Kluwer Academic Publishers, New York in 2003
Softcover reprint of the hardcover 1s t edition 2003
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TABLE OF CONTENTS
DEDICATION v
TABLE OF CONTENTS VB
PREFACE Xl
CHAPTER!
THE LINEAR MULT IOBJECTIVE PROJECT SELECTION
PROBLEM
1.1 Introduction 1
1.2 An Example from the Literature 3
1.3 Towards a More General Multiobjective Formulation 7
1.4 A Second Example 9
1.5 Summary and Conclusions 11
References 15
CHAPTER 2
EVALUATING COMPETING INVESTMENTS
2.1 Introduction 19
2.2 Adjusting for Time Alone 19
2.3 Adjusting for Time and Risk 22
2.4 Conclusions 27
References 30
CHAPTER 3
THE LINEAR PROJECT SELECTION PROBLEM: AN
ALTERNATIVE TO NET PRESENT VALUE
3.1 Introduction 31
3.2 An Example 32
3.3 The Behavioral Implications ofNPV 33
3.4 Multiple Objective Decision Methods 35
3.5 Conclusions 38
References 40
CHAPTER 4
CHOOSING THE BEST SOLUTION IN A PROJECT SELECTION
PROBLEM WITH MULTIPLE OBJECTIVES
4.1 Introduction 41
4.2 Some Early Approaches 42
4.3 A Matching and Grouping Approach 46
4.4 A Stochastic Screening Approach 54
4.5 Conclusions 62
References 64
CHAPTERS
EVALUATING A PORTFOLIO OF PROJECT INVESTMENTS
5.1 Introduction 65
5.2 Examples 67
5.3 Conclusions 74
References 76
CHAPTER 6
CONDITIONAL STOCHASTIC DOMINANCE IN PROJECT
PORTFOLIO SELECTION
6.1 Introduction 77
6.2 The Model 78
6.3 Summary and Conclusions 89
References 93
CHAPTER 7
MEAN-GINI ANALYSIS IN PROJECT SELECTION
7.1 Introduction 95
7.2 The Model 101
7.3 Conclusions 114
References 117
CHAPTER 8
A SAMPLING-BASED METHOD FOR GENERATING
NONDOMINATED SOLUTIONS IN STOCHASTIC MOMP
PROBLEMS
8.1 Introduction 119
8.2 Stochastic, Nondominated Solutions 123
8.3 Sampling Approaches to Solving MOMP Problems 125
8.4 Computational Issues 126
8.5 Summary and Conclusions 134
Appendix 8.1
Example SAS Code 135
References 144
CHAPTER 9
AN INTERACTIVE MUL TIOBJECTIVE COMPLEX SEARCH FOR
STOCHASTIC PROBLEMS
9.1 Introduction 147
9.2 Direct Search Methods 149
9.3 Applying Complex Search to Multiobjective Mathema-
tical Programming.Problems 152
9.4 An Example of Multi objective Complex Search 155
9.5 Conclusions 158
References 160
V111
CHAPTER 10
AN EVOLUTIONARY ALGORITHM FOR PROJECT SELECTION
PROBLEMS BASED ON STOCHASTIC MULTIOBJECTIVE
LINEARLY CONSTRAINED OPTIMIZATION
10.1 Introduction 163
10.2 Stochastic Multiobjective Linearly Constrained
Programs 164
10.3 Multiobjective Evolutionary-Based Algorithm 166
10.4 Computational Examples 174
10.5 Summary and Conclusions 183
Appendix 10.1
Input File for the Algorithm Parameters for the
SMOLCP Example 185
Appendix 10.2
Java Program that Defines the First Objective Function in the
SMOLCP Example 187
References 188
INDEX 191
IX
PREFACE
The project selection problem is one that has been given much atten
tion in the literature. In the project selection problem the decision maker is
required to allocate limited resources across an available set of projects, for
example, research and development (R&D) projects, information technology
(IT) projects, or other capital spending projects. In choosing which projects
to fund, the decision maker must have some concrete objective in mind, e.g.,
maximization of profit or market share or perhaps minimization of time to
market. And in some cases the decision maker may wish to simultaneously
satisfy more than one of these objectives. Most often, these multiple objec
tives will be in conflict, resulting in a more complicated decision making task.
The decision maker may be able to partially fund some projects, or
conversely some projects may involve a binary decision of fully funding or
not funding at all. The decision maker may also have to resolve issues of in
terdependency-that is that the value of funding an additional individual proj
ect may vary depending upon the success or failure of projects that are already
in the portfolio. The decision maker then must take all these factors into ac
count in seeking an appropriate project selection model, choosing a method
ology which evaluates the appropriate objective(s), subject to relevant re
source constraints as well as constraints relating to projects with binary (full
or none at all) funding restrictions.
There is a considerable body of literature describing an abundant va
riety of models designed for the project selection problem. For our purposes
here, the literature can be broken down into two main streams: that which we
will label the traditional management science stream and that which we will
call the financial modeling stream. The first stream, the management science
literature, derives largely from mathematical programming treatments along
with some use of classical decision theory. In order to use these approaches it
is usually assumed that the existing decision alternatives (projects) are rea
sonably well-known and that the necessary information for modeling these
alternatives is at hand at the initiation of the planning process. The majority
of the management science models treat the decision process of choosing a set
of new projects to form a wholly new portfolio. But some of the models we
will present also address the problem of adding one or more new projects to
an already existing project portfolio. Most of the research in this body of lit
erature is confined to decisions which are made at one point in time, that is,
the models are static in the sense that they represent a one-time decision to
assemble or analyze a given portfolio.
An important junction in the decision making process occurs when
the decision maker chooses the appropriate objective(s). If a single objective
(e.g., market share) is chosen, then the problem may be handled with ordinary
mathematical programming techniques that have been used in project plan
ning models for some time now. If, however, the decision maker wishes to
pursue several objectives simultaneously (e.g., maximization of revenue in
each of several future time periods), some form of multiobjective program
ming will be needed. It is our belief that this multiobjective case is the more
realistic one, thus, in this book, we will show several applications of multiob
jective programming to the project portfolio problem.
A key assumption of the mathematical programming models above is
that all relevant information about the projects is known. However, this may
not always be true. Some allocation decisions must be made in the presence
of uncertainty. Uncertainty may exist concerning the ultimate result of a proj
ect (e.g., the amount of revenue) or the success or failure likelihood may be
known only as a probability distribution. Uncertainty may be represented by
probability distributions around the coefficients in the objective function or in
the constraints. In this book we will illustrate treatments for each of the above
forms of uncertainty. We will, however, assume that adequate information is
available to represent these projects in the model. The required information
may be in the form, for example, of a probability of project success or a prob
ability distribution around a coefficient in the objective function (e.g. project
return).
When we are dealing with uncertainty and multiple objectives, we
may need to resort to the use of stochastic dominance criteria to screen a set
of solutions. Stochastic dominance is appropriate for all probability distribu
tions and is minimally restrictive with respect to thedecision maker's utility
function. In this monograph there are several forms of stochastic dominance,
which are of interest. First order stochastic dominance simply compares the
cumulative distribution functions for two projects and makes the choice on
this basis alone. The first order criterion is applicable to all decision makers
with monotone utility functions; that is, decision makers who prefer higher
returns to lower ones and/or those who prefer less risk to more risk. In some
instances, the first order criterion does not yield an unambiguous choice. In
these cases it may be necessary to resort to second order stochastic
dominance. The decision calculus here is based on the area between the two
cumulative distribution curves. This second-order criterion is appropriate for
a narrower class of decision makers, those who are risk-averse. We will also
in some cases apply a conditional stochastic dominance criterion. Conditional
stochastic dominance analysis identifies dominant and nondominant projects
conditioned on the projects, which make up the current portfolio. This
criterion requires no explicit knowledge of the decision maker's utility
function and is applicable to all risk averse decision makers. Finally, in some
cases we will apply a stochastic dominance criteria which compares
xii
