Stochastic Optimization Models for Contingency Allocation in Project Management

152 Pages·2016·2.62 MB·English

by  Barbara Maria Thibadeau

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Doctoral Dissertations Graduate School
12-2006
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Barbara Maria Thibadeau
University of Tennessee - Knoxville
Follow this and additional works at: https://trace.tennessee.edu/utk_graddiss
Part of the Management Sciences and Quantitative Methods Commons
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Thibadeau, Barbara Maria, "Stochastic Optimization Models for Contingency Allocation in Project
Management. " PhD diss., University of Tennessee, 2006.
https://trace.tennessee.edu/utk_graddiss/2045
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To the Graduate Council:
I am submitting herewith a dissertation written by Barbara Maria Thibadeau entitled "Stochastic
Optimization Models for Contingency Allocation in Project Management." I have examined the
final electronic copy of this dissertation for form and content and recommend that it be
accepted in partial fulfillment of the requirements for the degree of Doctor of Philosophy, with a
major in Management Science.
Chanaka Edirisinghe, Major Professor
We have read this dissertation and recommend its acceptance:
Charles E. Noon, Russell Zaretzki, Adedeji Badiru
Accepted for the Council:
Carolyn R. Hodges
Vice Provost and Dean of the Graduate School
(Original signatures are on file with official student records.)
To the Graduate Council:
I am submitting herewith a dissertation written by Barbara Maria Thibadeau entitled
“Stochastic Optimization Models for Contingency Allocation in Project Management”.
I have examined the final electronic copy of this dissertation for form and content and
recommend that it be accepted in partial fulfillment of the requirements for the degree of
Doctor of Philosophy in Management Science.
Chanaka Edirisinghe
Major Professor
We have read this dissertation
and recommend its acceptance:
Charles E. Noon
Russell Zaretzki
Adedeji Badiru
Accepted for the Council:
Anne Mayhew
Vice Chancellor and
Dean of Graduate Studies
(Original signatures are on file with official student records.)
Stochastic Optimization Models for
Contingency Allocation in Project
Management
A Dissertation
Presented for the
Doctor of Philosophy
Degree
The University of Tennessee, Knoxville
Barbara Maria Thibadeau
December 2006
Copyright (cid:13)c 2006 by Barbara Maria Thibadeau
All rights reserved.
ii
Dedication
This dissertation is dedicated to my most beloved husband. I could never have done this
without you.
I love you.
iii
Acknowledgments
I would like to thank my parents, who showed me that dreams are achievable and who
taught me never to give up. I would like to thank them for instilling in me a belief in God,
without whom none of this would have been possible. I would like to thank my children
who, even though they think it’s crazy to keep going to school, are the best fan club any
Mom could hope to have.
I would like to thank everyone at work. Special thanks belong to Ian Anderson, Kent
Crawford, Lewis Galyon, Barry Groth, John Haines, Todd Holzer and Wayne Steffey. You
guys are great!
IwouldliketothankeveryoneintheManagementScienceDepartment. Inparticular, I
wouldliketothankJaneMoser,whosekindwordsoftenmademyday,andDr. Edirisinghe,
who, I swear, has the patience of Job. No matter how stupid and tedious my questions
might have been, he was always what I think a teacher should be- encouraging and helpful.
I have benefited greatly from his keen insight and systematic approaches.
iv
Abstract
Inmanycases, risksthatthreatensuccessfulprojectcompletioncanbemitigated(orelimi-
nated) with a proper contingency allocation strategy and exacerbated by an improper one.
Yet,despitethisimportance,thereisadearthofresearchonthecostcontingencyallocation
problem in academic literature. This thesis looks at the cost contingency allocation prob-
lem from two different perspectives and two distinct solutions are introduced: a stochastic
linear programming model that addresses a short term strategy and convex-concave utility
model that provides a long-term planning solution.
Theprojectmanager’sabilitytoapproverandomrequestsforcontingencyisdependent
on several constraints, including the amount of funding that is available and the current
contingency balance. The stochastic short-term contingency allocation model developed in
this thesis delivers, to the decision maker, a single allocation strategy (feasible under all
potential futures) where uncertainty is specified via a well-chosen set of scenario paths for
thefuture. Comparisonofthemodel’ssolutiontothatofatypicalmyopicperiod-by-period
approach shows that the model’s solution is superior to that of the standard practice.
This short-term model is then extended to develop a long range contingency allocation
plan under the concept of expected utility theory. The hypothesis that the ‘risk-seeking’
and ‘risk-averse’ behaviors modeled by convex and concave utility functions are those ex-
hibitedbyaprojectissubstantiatedbyactualprojectdata. Thesedatashowthatthe‘risk-
seeking’behaviorthatisanassetatthebeginningofaprojectcouldleadtoover-allocation
ofcontingencyearlyinaprojectandjeopardize completionifa‘risk-averse’behavioris not
adopted by the project once the designs and specific project details have been more firmly
established. A novel, additive project utility function comprised of performance, scope and
schedule utility functions is introduced and the convex-concave utility-based contingency
allocation model introduced provides a long term contingency allocation plan that maxi-
mizes this utility. Both developed models are empirically validated using data from two
Department of Energy projects.
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Contents
1 Introduction 1
1.1 History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Headlines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2.1 What is a Successful Project? . . . . . . . . . . . . . . . . . . . . . . 3
1.2.2 What is the Reality? . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Contributors to Project Failure . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3.1 Schedule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3.2 Cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3.3 New Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.3.4 Concurrency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.3.5 Scope Creep. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.3.6 Lack of Explicit Objectives . . . . . . . . . . . . . . . . . . . . . . . 8
1.3.7 Political Will . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.3.8 People . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.4 Focus of this Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.4.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.4.2 Organization and Objectives . . . . . . . . . . . . . . . . . . . . . . 10
2 Literature Review - Current Modeling Solutions and Their Drawbacks 14
2.1 Choice of Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.1.1 Management Science Models . . . . . . . . . . . . . . . . . . . . . . 14
2.2 Models Used in the Management of Projects . . . . . . . . . . . . . . . . . . 15
2.3 Management Science Resource Allocation Models . . . . . . . . . . . . . . . 17
2.3.1 Project Selection Models. . . . . . . . . . . . . . . . . . . . . . . . . 18
2.3.2 Schedule Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.3.3 Utility Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.3.4 Scarce Implementation of Existing Models . . . . . . . . . . . . . . . 23
2.3.5 Uncertainty in Project Management . . . . . . . . . . . . . . . . . . 26
2.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
vi
3 Chapter 1: A Short-Term Contingency Allocation Stochastic Program-
ming Model 32
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.1.1 Contingency Requests . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.1.2 The Contingency Allocation Problem . . . . . . . . . . . . . . . . . 35
3.1.3 Earned Value Management Terminology . . . . . . . . . . . . . . . . 35
3.2 Uncertainty Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.3 Contingency Allocation Model (CA Model) . . . . . . . . . . . . . . . . . . 41
3.3.1 Decision Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.3.2 Model Under ∆ -Policy . . . . . . . . . . . . . . . . . . . . . . . . . 44
0
3.3.3 Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.4 Application of the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
3.4.1 Scenario Paths and Scenario Sets . . . . . . . . . . . . . . . . . . . . 50
3.4.2 Scenario Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.4.3 Case Study Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . 61
3.4.4 Mitigation of End-Effects . . . . . . . . . . . . . . . . . . . . . . . . 61
3.4.5 Specific Sample Spaces (Scenario Sets) Used in this Analysis . . . . 62
3.4.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4 Chapter 2: A Long-Term Convex-Concave Utility Model 70
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
4.2 Utility Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
4.3 Violations and Alternatives . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
4.3.1 Observed Violations . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
4.3.2 Multiple Utility Functions . . . . . . . . . . . . . . . . . . . . . . . . 75
4.3.3 Escalation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
4.4 A Multi-Attribute Project Utility Function . . . . . . . . . . . . . . . . . . 77
4.5 Actual Project Data Viewed from Utility Perspective . . . . . . . . . . . . . 83
4.5.1 Convex-Concave . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
4.5.2 Actual Allocation of the Contingency Reserve in the Utility Context 87
4.6 A Utility Based Contingency Allocation Model . . . . . . . . . . . . . . . . 88
4.6.1 Comparison with the Linear Utility Model . . . . . . . . . . . . . . . 89
4.7 The Convex-Concave Uncertainty Model . . . . . . . . . . . . . . . . . . . . 92
4.7.1 Random Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
4.7.2 Decision Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
4.7.3 Decision Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
4.8 Specifying a Project Specific Utility Function . . . . . . . . . . . . . . . . . 101
4.8.1 Analytic Hierarchy Process . . . . . . . . . . . . . . . . . . . . . . . 101
4.8.2 Project Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . 102
4.9 Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
vii