The doctoral dissertations of the former Helsinki University of Technology (TKK) and Aalto University Schools of Technology (CHEM, ELEC, ENG, SCI) published in electronic format are available in the electronic publications archive of Aalto University - Aaltodoc.

Portfolio Decision Analysis for Robust Project Selection and Resource Allocation

Juuso Liesiö

Dissertation for the degree of Doctor of Science in Technology to be presented with due permission of the Faculty of Information and Natural Sciences for public examination and debate in Auditorium E at Helsinki University of Technology (Espoo, Finland) on the 14th of November, 2008, at 12 noon.

Overview in PDF format (ISBN 978-951-22-9598-2)   [205 KB]
Dissertation is also available in print (ISBN 978-951-22-9597-5)


Organizations must take decisions on how to allocate resources to 'go/no-go' projects to maximize the value of their project portfolio. Often these decisions are complicated by several value criteria, multiple resource types and exogenous uncertainties that influence the projects' values. Especially when the number of projects is large, the efficiency of the resource allocation and the quality of the decision making process are likely to benefit from systematic use of portfolio decision analysis.

This Dissertation develops and applies novel methods to manage uncertainty in decision analytic models for project portfolio selection. These methods capture incomplete information through sets of feasible model parameter values and use dominance relations to compare portfolios. Based on the computation of all non-dominated portfolios, these methods identify i) robust portfolios that perform well across the range of feasible parameter values and ii) projects that should surely be selected or rejected in the light of the incomplete information.

These methods have several implications for project portfolio decision support. Explicit consideration of incomplete information contributes to the reliability of analysis, which is likely to increase the use of portfolio decision analysis in new contexts. Furthermore, cost and time savings in data elicitation may be achieved, because these methods can give robust decision recommendations based on incomplete data and identify projects for which additional information is beneficial. Finally, these methods support consensus building within organizations as different views about projects' quality or exogenous uncertainties can be considered simultaneously to identify projects on which further negotiations should be focused.

This thesis consists of an overview and of the following 5 publications:

  1. Juuso Liesiö, Pekka Mild, and Ahti Salo. 2007. Preference programming for robust portfolio modeling and project selection. European Journal of Operational Research, volume 181, number 3, pages 1488-1505. © 2007 Elsevier Science. By permission.
  2. Juuso Liesiö, Pekka Mild, and Ahti Salo. 2008. Robust portfolio modeling with incomplete cost information and project interdependencies. European Journal of Operational Research, volume 190, number 3, pages 679-695. © 2008 Elsevier Science. By permission.
  3. Mats Lindstedt, Juuso Liesiö, and Ahti Salo. 2008. Participatory development of a strategic product portfolio in a telecommunication company. International Journal of Technology Management, volume 42, number 3, pages 250-266. © 2008 Inderscience Enterprises. By permission.
  4. Ahti Salo and Juuso Liesiö. 2006. A case study in participatory priority setting for a Scandinavian research program. International Journal of Information Technology & Decision Making, volume 5, number 1, pages 65-88. © 2006 World Scientific Publishing Company. By permission.
  5. Juuso Liesiö and Ahti Salo. 2008. Scenario-based portfolio selection of investment projects with incomplete probability and utility information. Helsinki University of Technology, Systems Analysis Laboratory Research Reports, E23. © 2008 by authors.

Keywords: decision analysis, project portfolio selection, multi-objective optimization, multi-attribute value theory, utility theory, incomplete information, scenarios, risk measures, robustness

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© 2008 Helsinki University of Technology

Last update 2011-05-26