Pekka Mild

Preference Programming for Robust Portfolio Modeling and Project Selection

In decision analysis, difficulties of obtaining complete information about model parameters make it advisable to seek robust solutions that perform reasonably well across the full range of feasible parameter values. In this paper, we develop the Robust Portfolio Modeling (RPM) methodology which extends Preference Programming methods into portfolio problems where a subset of project proposals are funded in view of multiple evaluation criteria. We also develop an algorithm for computing all non-dominated portfolios, subject to incomplete information about criterion weights and project-specific performance levels. Based on these portfolios, we propose a project-level index to convey (i) which projects are robust choices (in the sense that they would be recommended even if further information were to be obtained) and (ii) how continued activities in preference elicitation should be focused. The RPM methodology is illustrated with an application using real data on road pavement projects.

Robust portfolio modeling with incomplete cost information and project interdependencies

Robust portfolio modeling (RPM) [Liesio, J., Mild, P., Salo, A., 2007. Preference programming for robust portfolio modeling and project selection. European Journal of Operational Research 181, 1488-1505] supports project portfolio selection in the presence of multiple evaluation criteria and incomplete information. In this paper, we extend RPM to account for project interdependencies, incomplete cost information and variable budget levels. These extensions lead to a multi-objective zero-one linear programming problem with interval-valued objective function coefficients for which all non-dominated solutions are determined by a tailored algorithm. The extended RPM framework permits more comprehensive modeling of portfolio problems and provides support for advanced benefit-cost analyses. It retains the key features of RPM by providing robust project and portfolio recommendations and by identifying projects on which further attention should be focused. The extended framework is illustrated with an example on product release planning.

Combining a Multiattribute Value Function with an Optimization Model: An Application to Dynamic Resource Allocation for Infrastructure Maintenance

A key decision in infrastructure management is the allocation of resources to maintenance activities that consist of periodic rehabilitation actions and routine day-to-day operations. These activities improve the quality of different assets and operations. They also differ in terms of their objectives, costs, and life-cycle characteristics; yet they all impact the same infrastructure system and compete for resources from the same budget. To support the allocation of resources to these activities, we present a generic resource allocation model that we developed for the Finnish Road Administration (Finnra) by building and interlinking (i) a preference model, which yields the aggregate value of maintenance activities by applying multiattribute value functions to the quality distributions of assets; (ii) a life-cycle model, which captures the deterioration-improvement dynamics associated with the maintenance activities; and (iii) an optimization model, which generates funding recommendations for maximizing the aggregate long-term value of maintenance investments. The optimization results were explored in facilitated workshops where “on-the-fly” computations gave senior managers insights into how the recommendations depended on preferences and budget levels. The case study was awarded for an outstanding achievement in Finnras research program, and it was also recognized as a Finalist for the Decision Analysis Society Practice Award in 2007.

Selecting infrastructure maintenance projects with Robust Portfolio Modeling

Project portfolios for the annual maintenance of infrastructure assets may contain dozens of projects which are selected out of hundreds of candidate projects. In the selection of these projects, it is necessary to account for multiple evaluation criteria, project interdependencies, and uncertainties about project performance as well as financial and other relevant constraints. In this paper, we report how Robust Portfolio Modeling (RPM) has been used repeatedly at the Finnish Transport Agency (FTA) for bridge maintenance programming. At FTA, project selection decisions are guided by the RPMs Core Index values which are derived from portfolio-level computations and reflect incomplete information about the relative importance of evaluation criteria. To-date, this application has been rerun with fresh data for six consecutive years. By drawing on experiences from this application, we discuss preconditions for the successful use of RPM or other methods of Portfolio Decision Analysis in comparable settings. We also develop an approximative algorithm for computing non-dominated portfolios in large project selection problems. We report a repeated application of RPM to bridge maintenance project selection.We identify general features that contributed to the success of this application.We develop an algorithm for solving non-dominated portfolios in large RPM problems.