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235. An efficient linear reformulation for project portfolio optimization problems
Invited abstract in session TB-60: Projects, risk and law, stream Project Management and Scheduling.
Tuesday, 10:30-12:00Room: S09 (building: 101)
Authors (first author is the speaker)
| 1. | Ming-Hua Lin
|
| Department of Urban Industrial Management and Marketing, University of Taipei | |
| 2. | Jung-Fa Tsai
|
| Business Management, National Taipei University of Technology |
Abstract
With the rapid evolution of technology and the constant changes in the business environment, many enterprises assign different tasks as projects. Therefore, project management performance is critical to the success of the enterprise. There are usually multiple projects executed concurrently in the organizations. Project portfolio management significantly influences project management performance due to the limited available resources and the interdependency between projects. Project portfolio selection is one of the most critical issues in the project portfolio management process. Allocating available resources to an optimal set of projects will result in the most benefits. This study considers a project portfolio selection problem that discusses selecting an optimal subset of projects from a set of candidate projects for maximizing benefits with an effective and efficient use of limited resources. The original mathematical model of the project portfolio selection problem is a nonlinear integer programming problem that is hard to solve for finding a globally optimal solution. This study utilizes a novel linearization approach that efficiently transforms the project portfolio selection problem with cross-product terms as a mixed-integer linear program solvable for finding a globally optimal solution. Compared with current methods, the proposed method uses much fewer continuous variables to linearly reformulate the problem. Numerical examples are presented to demonstrate that t
Keywords
- Project Management and Scheduling
- Programming, Nonlinear
- Mathematical Programming
Status: accepted
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