A New Multi-Objective Model for R&D Project Portfolio Selection Considering Potential Repetitive Projects and Sanction Impacts

Abstract

In today’s highly competitive marketplace, selecting an appropriate set of projects from a portfolio of candidate projects is vital for enterprises. An accurate selection of projects can steer a company to great success, while a careless selection may lead it to bankruptcy. Variability of project parameters such as benefit, cost, risk (failure probability), etc. during planning horizon makes this selection more complicated and increases the importance of an elaborate analysis. In this article, we studied a multi-objective R&D project portfolio selection problem. There is a conflicting desire to maximize expected net benefit and minimize risk in companies. From a novel perspective, the authors considered repetitive projects and variable amounts for aforementioned project parameters during planning horizon that could be an effect of sanctions, in our model that are features of real world problems. Due to NP-hardness of the problem and its high computational effort especially when the number of projects grows, we solved test problems of different sizes using a Multi-Objective Differential Evolution (MODE) algorithm to find pareto optimal solutions.

Project Portfolio Selection Problems: A Review of Models, Uncertainty Approaches, Solution Techniques, and Case Studies

“…Table 13 presents some of the scoring based methods used in PPS. Ghasemzadeh, Archer, and Iyogun (1999), Urli and Terrien (2010), Shou and Huang (2010), Zhu and Wang (2012), Yu, Wang, Wen, and Lai (2012), Nikkhahnasab and Najafi (2013), Tavana et al (2015), Hassanzadeh et al (2014), Wang and Song (2016) Integer Programming Doerner et al (2006), Li et al (2012), Rabbani et al (2013), Jafarzadeh et al…”

“…Table 15 provides a review of different approaches applied in PPS methods. Naderi 2013The imperialist competitive algorithm Rabbani et al (2013) Multi-objective differential evolution (MODE) Esfahani and Yousefi (2016) Harmony search algorithm Jingmei and Peng (2015) Improved quantum genetic algorithm Lifshits and Avdoshin (2016) SPEA II method Ghodoosi et al (2016) Multi-objective shuffle frog leaping algorithm (SFLA)…”

Industrial R&D project portfolio selection method using a multi-objective optimization program: A conceptual quantitative framework

“…Therefore, it must be emphasized that capabilities for the recommended PPM initiatives and structure are developed over time with a learning effect on PPM performance (Martinsuo, 2013). Therefore, by merging elements from the conceptual frameworks proposed by Meskendahl (2010), Litvinchev et al (2011), Rabbani et al (2013), Baqeri et al (2019) and Dixit and Tiwari (2020), the extensive research done in this context leads to a project selection and business evaluation method that can be applied to a range of multi-stage portfolio problems and handles uncertain and flexible parameters (Abbassi et al, 2014;Analia-Sánchez et al, 2014;Killen, 2017). Wang and Hwang (2007) argue that PPM can be divided into three categories: strategic management tools, benefit measurement methods, and mathematical programming approaches.…”

“…This paper is inspired by and builds on the strategic orientation concept of Meskendahl (2010) by incorporating business strategy and performance (Killen & Hunt, 2010;Meskendahl, 2010;Analia-Sánchez et al, 2014). By merging the framework of Meskendahl (2010) and framework elements from Litvinchev, López, Escalante and Mata (2011), Rabbani, Najjarbashi and Joudi (2013), Baqeri, Mohammadi and Gilani (2019) and Dixit and Tiwari (2020), this paper creates a multi-objective optimization program (MOOP) as a project portfolio selection method that is an accurate method for analysing R&D projects in a systematic manner to optimize corporate objectives. The objectives consider the values and risks of proposed projects in a multi-project, multi-opportunity context helping managements' decision making.…”

“…Figure 1 emphasizes the way this paper merges different frameworks (Meskendahl, 2010;Litvinchev et al, 2011;Rabbani et al, 2013;Baqeri et al, 2019;Dixit & Tiwari, 2020). However, the elements compiled within Figure 1 are not addressed in qualitative business extended research.…”