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RPM – Robust Portfolio Modeling for Project Selection

RPM – Robust Portfolio Modeling for Project Selection. Pekka Mild, Juuso Liesiö and Ahti Salo Systems Analysis Laboratory Helsinki University of Technology P.O. Box 1100, 02150 TKK, Finland http://www.sal.tkk.fi firstname.lastname@tkk.fi . Problem framework.

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RPM – Robust Portfolio Modeling for Project Selection

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  1. RPM – Robust Portfolio Modeling for Project Selection Pekka Mild, Juuso Liesiö and Ahti Salo Systems Analysis Laboratory Helsinki University of Technology P.O. Box 1100, 02150 TKK, Finland http://www.sal.tkk.fi firstname.lastname@tkk.fi

  2. Problem framework • Choose a portfolio of projects from a large set of proposals • Projects evaluated on multiple criteria • Resource and other portfolio constraints • Reported applications in contexts such as • Corporate R & D (Stummer and Heidenberger, 2003) • Healthcare (Kleinmuntz and Kleinmuntz, 1999) • Infrastructure (Golabi et al., 1981; Golabi, 1987) • Software tools, e.g. • Catalyze Ltd (UK) / Hiview & Equity • Strata Decision Technology LLC / StrataCap® • Expert Choice® / EC Resource AlignerTM

  3. Additive representation of portfolio value • Projects with costs • Scores and weights • Feasible portfolios • Project value: weighted sum of scores • Portfolio value: sum of projects’ values • Maximize portfolio value

  4. Incomplete information in portfolio problems • Elicitation of complete information (point estimates) on weights and scores may be costly or even impossible • If we only have incomplete information, what portfolios and projects can be recommended? • We extend the solution concepts of Preference Programming methods (e.g., Salo and Hämäläinen, 1992; 2001) to portfolio problems • Provide guidance for focusing the elicitation efforts • Liesiö, Mild, Salo, (2005). Preference Programming for Robust Portfolio Modeling and Project Selection, conditionally accepted

  5. Modeling of incomplete information • Feasible weight set • Several kinds of preference statements impose linear constraints on weights • Rank-orderings on criteria (cf., Salo and Punkka, 2005) • Interval SMART/SWING (Mustajoki et al., 2005) • Interval scores • Lower and upper bounds on criterion-specific scores of each project • Information set • Feasible values for and

  6. Non-dominated portfolios • Incomplete information leads to value intervals on portfolios • Typically, no portfolio has the highest value for all feasible weights and scores • Portfolio dominateson S, denoted by ,iff • Non-dominated portfolios • Computed by dedicated dynamic programming algorithm • Multi-Objective Zero-One LP (MOZOLP) problem with interval coefficients

  7. Project-oriented analysis • Core Index of a project, • Share of non-dominated portfolios on S in which a project is included • Core projects, i.e. , can be surely recommended • Would belong to all ND portfolios even with additional information • Exterior projects, i.e. , can be safely rejected • Cannot enter any ND portfolio even with additional information • Borderline projects, i.e. , need further analysis • Negotiation / iteration zone for augmenting the set of core projects

  8. Sequential specification of information • Dominance relations depend on S • Loose statements often lead to a large number of ND portfolios • Complete information typically leads to a unique portfolio • Additional information to reduce • Modeled through a smaller weight set ( ) and/or narrower scoreintervals ( ) • No new portfolio can become non-dominated: • Elicitation efforts can be focused on borderline projects • Additional information can affect the status of borderline projects only • Narrower score intervals needed for borderline projects only

  9. RPM for project portfolio selection Selected Core projects  choose Large set of projects Multiple criteria Resource and portfolio constraints Add. core Preceding core proj. Additional information Decision rules, heuristics Loose statements on weights and scores Borderline projects focus on Borderline Not selected Add. exter. Compute non-dom. portfolios Negotiation, iteration Update ND portfolios Exterior proj.  discard Preceding exterior

  10. Application to road pavement projects (1/4) • Real data from Finnish Road Administration • Selection of the annual pavement program in one major road district • 223 project proposals • Generated by a specific road condition follow-up system • Coherent road segments  proposals are independent • Three technical measurement criteria on each project • Damage coverage in the proposed site • Annual cost savings attained by road users (if repaired) • Durability life of the repair • Budget of 16.3 M€, sufficient for funding some 160 projects

  11. Application to road pavement projects (2/4) • Illustrative ex post data analysis with RPM tools • Sequential weight information • Start with no information: • Rank-ordering stated by FINNRA experts: • Complete score information (point estimates) • Computations by PRO-OPTIMAL software • http://www.rpm.tkk.fi

  12. Application to road pavement projects (3/4) • No information, • 542 portfolios • 103 core projects • 16 exterior projects • 104 borderline proj., from which some 60 can be funded with remaining resources

  13. Application to road pavement projects (4/4) • Rank-ordering, • 109 portfolios • 127 core projects • 32 exterior projects • 64 borderline proj., from which some 30 can be funded with remaining resources

  14. Conclusions • Key features • Admits incomplete information about weights and projects • Accounts for competing projects, scarce resources and portfolio constraints • Determines all non-dominated portfolios • Robust decision recommendations • Core Index values for individual projects derived from portfolio level analyses • Decision rules for portfolios (e.g., maximin, minimax regret) • Benefits • May lead to considerable savings in the costs of preference elicitation • Enables sequential decision support process with useful tentative results • Applications in project portfolio management and technology foresight

  15. References • Golabi, K., (1987). Selecting a Group of Dissimilar Projects for Funding, IEEE Transactions on Engineering Management, Vol. 34, pp. 138 – 145. • Golabi, K., Kirkwood, C.W., Sicherman, A., (1981). Selecting a Portfolio of Solar Energy Projects Using Multiattribute Preference Theory, Management Science, Vol. 27, pp. 174-189. • Mustajoki, J., Hämäläinen, R.P., Salo, A., (2005). Decision Support by Interval SMART/SWING - Incorporating Imprecision in the SMART and SWING Methods, Decision Sciences, Vol. 36, pp. 317 - 339. • Kleinmuntz, C.E, Kleinmuntz, D.N., (1999). Strategic approach to allocating capital in healthcare organizations, Healthcare Financial Management, Vol. 53, pp. 52-58. • Stummer, C., Heidenberger, K., (2003). Interactive R&D Portfolio Analysis with Project Interdependencies and Time Profiles of Multiple Objectives, IEEE Trans. on Engineering Management, Vol. 50, pp. 175 - 183. • Salo, A. and R. P. Hämäläinen, (1992). Preference Assessment by Imprecise Ratio Statements, Operations Research, Vol. 40, pp. 1053-1061. • Salo, A. and Hämäläinen, R. P., (2001). Preference Ratios in Multiattribute Evaluation (PRIME) - Elicitation and Decision Procedures under Incomplete Information, IEEE Transactions on Systems, Man, and Cybernetics, Vol.3,pp. 533-545. • Salo, A. and Punkka, A., (2005). Rank Inclusion in Criteria Hierarchies, European Journal of Operations Research, Vol. 163, pp. 338 - 356

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