<strong>Project Portfolio Optimization under Temporal Constraints with uncertainty</strong>

Abstract

The definition of more realistic scenarios of instances for the Project Portfolio Optimization (PPO) of new product developments usually should involve precedence relations that generate effects related to time-interdependence among different projects.The study of time -interdependences, or time effects, on the selection of projects captures our attention because they affect the problem objective functions. Three different moments have been identified as usually present in any project: 1) the estimated completion time; 2) the moment in which the competence become significant; and, 3) the moment in which the developed product becomes old. A PPO under such temporal constraints (denoted PPOTC) could face risk because the lack of reliable data derived from long lead times of projects, or by a complex market and technology dynamics; such imperfect knowledge could cause variability in the benefits and requirements of a PPOTC. In this sense, this research proposes the design of a methodology based on intervals for PPOTC under uncertainty, and the study of their influence in choosing optimal project portfolios of new product developments. The advantage of this approach is a unified and simple way to model the different sources of imprecision, uncertainty and arbitrariness. Also, the model is parametrized such that the attitude of the DM facing the imperfect knowledge can be adjusted by using two meaningful parameters.

<strong>Modeling Personality Influence over Preference Model&rsquo;s Parameter Values</strong>

“…Such approaches have allowed the development of computable preference models, based on a predefined set of parameters that reflect the interests of a DM. The most practical way that can be used to set the parameter values for that preference model is through Preference Disaggregation Methods (Rangel-Valdez et al, 2015;Cruz-Reyes et al, 2017), which are methods that based on a battery of examples provided by the DM elicits the entire set of parameters.…”

Hyperheuristics for indirect elicitation of outranking model&rsquo;s parameters in Project Portfolio Optimization

“…In these approaches, the provision of preference information is far simpler for a DM because it can be done based on decisions taken in the past or formulated recently by means of manageable examples. So far, PDMs have been implemented using evolutionary metaheuristics (Rangel-Valdez et al, 2015;Cruz-Reyes et al, 2017), and they have shown their effectiveness on the parameter elicitation for outranking approaches used in the solution of the Portfolio Selection Problem (PSP) with the methods ELECTRE as preference models (cf. Roy, 1991).…”