Helsinki University of Technology
Systems Analysis Laboratory
Robust Portfolio Modeling in the
Development of National Research Priorities
Ville Brummer and Ahti Salo
Systems Analysis Laboratory
Helsinki University of Technology
P.O. Box 1100, 02015 TKK, Finland
http://www.sal.tkk.fi
[email protected]
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Helsinki University of Technology
Systems Analysis Laboratory
RPM – Robust Portfolio Modelling
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Helsinki University of Technology
Systems Analysis Laboratory
Robust Portfolio Modelling (RPM)

Liesiö, Mild, Salo (2006). Preference Programming for Robust
Portfolio Modelling and Project Selection, forthcoming in EJOR

Projects X  {x1 ,..., x m }

Evaluation with regard to multiple criteria
j
– Score of project x with regard to the i-th criterion
T
– Criterion weights w  ( w1 ,..., wn )

vij  [v]ij
Additive representation of project value
n
V ( x)   wi vi
j
i 1
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Helsinki University of Technology
Systems Analysis Laboratory
Project Portfolios

Portfolio p = a subset of projects p  X

Portfolio value = sum of its projects’ values (Golabi et al. 1981)
V ( p, w, v) 
V ( x
j
)
x jp

Feasible portfolios p  PF satisfy linear constraints
– E.g., budget constrains

Maximize portfolio value
max V ( p, w, v)
pPF
– Zero-one linear programming problem (ZOLP)
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Helsinki University of Technology
Systems Analysis Laboratory
Incomplete Information in Portfolio Selection

Elicitation of complete information (point estimates) on weights
and scores may be costly or even impossible

Weights constrained by the DMs preference statements
– Several kinds of preference statements impose linear constraints on weights
– (Incomplete) rank-orderings on criteria (cf., Salo and Punkka, 2005)
– Interval SMART/SWING (Mustajoki et al., 2005)
S w  S w0   w | wi  0,  wi  1 

Intervals of project-specific scores Sv  {v  R mn | v  v  v}
– Lower and upper bounds on criterion-specific scores of each project

Information set S  Sw  Sv ; (w, v)  S
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Helsinki University of Technology
Systems Analysis Laboratory
Dominance Concept for Portfolios

Portfolio p dominates p’ on S, denoted by p S p ' , if
 V ( p, w, v)  V ( p' , w, v) for all ( w, v)  S

V ( p, w, v)  V ( p' , w, v) for some ( w, v)  S
Portfolio p’ can be discarded because p yields higher value!

Non-dominated portfolios (NDP)

PN (S )   p  PF | p '  PF s.t. p '
S
p
– Restrict attention to NDPs only
– All NDPs computed by a dedicated dynamic programming algorithm
– Multi-Objective Zero-One LP (MOZOLP) problem with interval-valued objective
function coefficients
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Helsinki University of Technology
Systems Analysis Laboratory
Recommendations at the Portfolio Level

Core Index of a project, CI ( x , S ) 
j


p  PN ( S ) | x j  p
PN ( S )
j
x
– Share of non-dominated portfolios (NDP) that contains

j
Core projects, i.e. CI ( x , S )  1, can be surely recommended
– Would belong to all NDP even if additional information is acquired

j
Exterior projects, i.e. CI ( x , S )  0, can be safely rejected
– Cannot enter any NDP even with additional information

Borderline projects, i.e. 0  CI ( x j , S )  1, need further analysis
– Negotiation / iteration zone for augmenting the set of core projects
– Narrower score intervals help reduce the set of borderline projects
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Systems Analysis Laboratory
– No other feasible portfolio gives higher
overall value with all feasible weights and
scores

Project’s Core Index (CI)
– Core proj. are included in all NDP (CI=1)
– Exterior proj. not included in any NDP
(CI=0)
– Borderline proj. included in some NDP
(0<CI<1)
A
*
B
C
Overall value at
extreme point 3
Non-dominated portfolios (NDP)
Overall value at
extreme point 2

Overall value at
extreme point 1
Non-dominated Portfolios and Core Index
B
C
A *
D
A *
E *
E *
D
C
D
E *
B
A
10
B
4
5
C
3
1
2
6
7
9
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Helsinki University of Technology
Systems Analysis Laboratory
Core Index Analysis

Core Index is a performance of a measure which accounts for
– Incomplete information on weights and scores
– Project cost and competing proposals
– Budget and other feasibility
constraints
Core projects

Helps classify projects
→ accept
Hundreds of
projects
Incomplete
information
Borderline proj
Multiple criteria
Portfolio-level
constraints
→ focus
Compute
non-dominated
portfolios
Exterior proj
→ reject
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Systems Analysis Laboratory
Case: Development of National Research Priorities
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Helsinki University of Technology
Systems Analysis Laboratory
Forest-Based Sector Technology Platform (FTP)

One of the over 30 European Technology Platforms
– Coordination of industry-lead European R&D activities
– Establishment of the European Research Area (ERA)

This particular Technology Platform initiated by
– European Confederation of Woodworking Industries
– Confederation of European Forest Owners
– Confederation of European Paper Industries

Over 30 countries involved
– Launched in 2003
– Long-term perspective (2030)
– Development of the Strategic Research Agenda (SRA) in 2005 in member
states and at the European level
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Helsinki University of Technology
Systems Analysis Laboratory
Strategic Research Agenda (SRA) for the FTP
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Organisation established
Value-chain leaders elected.
Setting up the guidelines
Step 1: Collection of inputs
Step 2: European priorization
Step 3: Strategic objectives and research themes
Step 4: Open discussion and finalising
Developing SRA document
Final SRA

1. Dec.
Step 1: Each country was requested to identify 10 -15 most
relevant research themes in view of national priorities
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Challenges
 New policy instrument  No established approaches
 A very wide range of issues to be covered
– Many stakeholder groups (e.g., pulp and paper industry, bioenergy, forestry)
– Long time scale  considerable uncertainties
 Tight timetable
– Only 7 weeks  Need for a structured decision support process
 Multiple interfaces to other policy processes
– E.g., preparation of Framework Program (FP7) in Europe

Forest sector is a key industry in Finland
– 24 % of exports
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Helsinki University of Technology
Systems Analysis Laboratory
Finnish Case: National SRA Process for the FTP

Systematic process to engage Finnish key stakeholders
– Development of the national SRA
– Linked explicitly to the Vision 2030 document at the European level

Five value chains
– Forestry, Wood Products, Pulp and Paper, Bio Energy, Specialties/ New
Businesses
– Independent but interrelated process for each value chain

Identification and assessment of research themes
– Internet questionnaires – MCDM analysis - interactive decision workshops

Synthetisation of national results at the end of the process
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Helsinki University of Technology
Systems Analysis Laboratory
Participants and Roles

Steering Group
– Coordinators and selected key persons (~ 10 people)

Coordinators
– Chairs of national value chain Working Groups (5 people)

TKK Group
– Research team of Prof. Ahti Salo at the Systems Analysis Laboratory / TKK

Respondents
– 20-30 participants within each value chain

Referees
– 6-10 participants within each value chain
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Systems Analysis Laboratory
Process Design
Process steps
Weeks
Key participants
I Step: Internet-based solicitation of
research themes
1-2
Respondents
II Step: Internet-based assessment of
research themes
3-4
Referees
III Step: Multi-criteria analysis of research
themes
4-5
TKK group
IV Step: Value chain workshops for the
formulation of relevant research areas
5-6
Value Chain Coordinators and invited
Respondents, Referees and other experts
VI Step: Steering Group workshop for the
formulation of Finnish SRA priorities
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Steering Group
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Helsinki University of Technology
Systems Analysis Laboratory
Task 1: Solicitation of Research Themes

Timetable: April 27 – May 8
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Participants: 20 -30 Respondents / Value chain
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Task: In each value chain, respondents proposed research
themes with the Opinions-Online decision support tool

Result: Total 146 research themes

Task 1

Example
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Systems Analysis Laboratory
Task 2: Assessment of Research Themes
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Timetable: Mid-may
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Participants: 6 -10 Referees / Value chain
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Task: In each value chain, referees assessed research themes
with Opinions-Online©

Result: Numerical assessment of research themes with regard
to different criteria

Task 2
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Systems Analysis Laboratory
Task 3: Analysis on the Results

Timetable: Mid-may

Participants: Research group at TKK

Task: TKK group analysed the results using RPMmethodology
– ”Research theme” as ”project” treated as equal unit
– Scores defined as average of criterion specific evaluations
– Highlight 7 the most interesting themes from the whole set (Budget:C(p)  7 )

Result: Shortlist of ’the most interesting’ themes on each
value chain

Example
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Systems Analysis Laboratory
Task 4: Value Chain Workshops

Timetable: May 23 -31

Participants: Value chain working groups
– Selected respondents, referees and other experts

Task: Value chain Working Groups discussed on the results
and identified most relevant research themes.

Result: 3-7 the most relevant themes from each value chain
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Systems Analysis Laboratory
Task 5: SRA Workshops

Timetable: June 8

Participants: SRA steering group (includes value chain
coordinators)

Task: Based on the results from previous tasks and especially
from value chain workshops, SRA steering group identified
the most relevant 15 research themes

Result: the most relevant research themes
– Taken forward to European level
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Conclusions

Systematic way of organising foresight processes
– Permits extensive stakeholder participation even with tight schedules
– Is transparent in terms of methodology
– Supports workshop discussions through MCDA analyses

Considerations
– Formal MCDA inputs are helpful but need to be complemented
– Supports discussions by synthesizing results based on Core Index values
– Makes it possible to consider multiple perspectives (criteria & their weights)

Applicable in several other contexts, too
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