A multi objective-BSC model for new product development project portfolio selection

Autor: Seyyed Hassan Ghodsypour, Darya Abbasi, Maryam Ashrafi
Rok vydání: 2020
Předmět:
Zdroj: Expert Systems with Applications. 162:113757
ISSN: 0957-4174
Popis: The most significant factor for the survival of an enterprise under a high level of competition is new product development (NPD). As a result, selecting a potential NPD project portfolio to gain competitive advantage has become a major concern to enterprises. However, selection of an NPD project portfolio is intricate due to multiple selection criteria and factors. This study focuses on optimizing an NPD project portfolio selection problem. To this end, Balance Score Card (BSC) is employed as a comprehensive framework to define NPD project selection criteria. Afterward, a multi-objective mathematical model is formulated that attempts to maximize total outcome, to minimize total risk, and to maximize strategic advantages. Our proposed model also takes into account suppliers, consumer demands, and project interdependencies. Because of the NP-hardness of the proposed model, two multi-objective metaheuristic algorithms, multi-objective particle swarm optimization (MOPSO), and non dominated sorting genetic algorithm (NSGA-II) are applied to solve the proposed model. It should be noted that the performance of algorithms is evaluated using the ɛ-constraint method and enhanced using response surface methodology (RSM). Finally, several numerical examples of different sizes are generated to compare the performance of metaheuristic solution methods based on four comparing metrics. Computational results show that NSGA-II outperforms MOPSO in terms of all the evaluation metrics.
Databáze: OpenAIRE
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