Abstract
Article history: Received March 25, 2015 Received in revised format June 1 2015 Accepted June 17 2015 Available online June 18 2015 One of the primary assumptions in many project portfolio selection is the availability of all parameters. However, in real-world cases, many parameters are under uncertainty and the exact values are unknown in advance. This paper presents a scenario based mathematical model for project portfolio selection when parameters are under uncertainty. The problem considers two objective functions where the first one maximizes the net present value while the second objective function is the minimization of the positive deviations from the allocation of resources. The second objective function is looking for project resource leveling. The resulted model is formulated as mixed integer programming and the problem is analyzed under different conditions. Growing Science Ltd. All rights reserved. 5 © 201
Highlights
This paper presents a scenario based mathematical model for project portfolio selection when parameters are under uncertainty
The problem considers two objective functions where the first one maximizes the net present value while the second objective function is the minimization of the positive deviations from the allocation of resources
The second objective function is looking for project resource leveling
Summary
Selecting the right portfolio often helps minimization of relevant costs, which could lead to better profitability and this has been used in many areas such as research and development (R&D) (Abbassi et al, 2014), information technology software development (Bardhan et al, 2010; Chiang & Nunez, 2013; Rahmani et al, 2012; Müller et al, 2015), etc. Ghorbani and Rabbani (2009) proposed a multi-objective algorithm for project selection problem by considering two objective functions to maximize total expected benefit of selected projects and minimize the summation of the absolute variation of devoted resource between each successive time periods. They presented a meta-heuristic multi-objective to determine diverse locally nondominated solutions. Many project portfolio selection problems are dealt with uncertain parameters and we need to use different techniques such as robust optimization to handle uncertainty with input parameters (Huang & Qiao, 2012). Many project portfolio selections are formulated as mixed integer programming and we need to use metaheuristics to find the near optimal solution for them (vom Brocke & Lippe, 2015). Rabbani et al (2010), for instance, presented a multi-objective particle swarm optimization for project selection problem
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