An Interval‐Based Evolutionary Approach to Portfolio Optimization of New Product Development Projects

Abstract

The growth of large enterprises in the manufacturing market commonly depends on good New Product Development (NPD) projects; these projects represent a strategy to overcome competitors inside a competitive environment. The management of such projects is usually complex and involves risk due to the changing and conflicting environment. The approaches that tackle the problem lack an explicit consideration of the DM’s attitude facing uncertainty and imprecision related to the risk and particularly in the presence of time-interdependencies. This paper proposes a model of the time-related effects, under imperfect knowledge, and their influence in choosing optimal NPD portfolios. The proposed approach is an interval-based method to solve NPD portfolio optimization problems under different forms of imperfect knowledge. This approach has the advantage of a unified and simple way to model the different sources of imprecision, vagueness, uncertainty, and arbitrariness. The attitude of the DM facing the imperfect knowledge is adjusted by using some meaningful parameters. The research focuses particularly in creating a method useful for risk-averse DMs. The proposal was tested through an experimental design that compared the results achieved by the new method against the expected value in portfolios. The results revealed that high levels of conservatism might prevent wasting resources in failed projects.

An adaptive metaheuristic approach for risk-budgeted portfolio optimization

“…A new decision-making approach regarding multiple risks involved in portfolio optimization was initiated by Salehpoor and Zavardehi [11] by endorsing cardinality restraints, which are referred to as hybrid or mixed meta-heuristic algorithms. Next, Fernandez et al [12] proposed a model of time-related effects under imperfect knowledge and its impact on selecting optimal new product development portfolios. Hu et al [13] conducted comparative experiments on multi-swarm multi-objective optimization evolutionary algorithms based on p-optimality criteria (p-MSMOEAs), while several multiple objective evolutionary algorithms (MOEAs) were evaluated based on six mathematical benchmarking functions with two portfolio samples.…”