Abstract
Two important goals in project management are the maximization of the net present value (NPV) and project value, a more recent target. The former is a well-documented objective in project scheduling, and both are project evaluation tools used by decision makers. The literature has focused on the maximization of the project NPV problem and on project value as separate research tracks, but consideration of the tradeoff between both goals offers decision makers a more thorough evaluation of a project when weighing project alternatives. This paper introduces a novel formulation of the maximization problem that includes both a robust formulation of NPV and project value, develops algorithms to solve it, and illustrates the tradeoff between both objectives. The proposed mixed integer program (MIP) features a multimode setting, where the selection of an activity mode will impact cost, duration, resource usage and project value, and stochastic activity durations. To solve the problem, this study offers an innovative reinforcement learning (RL) based algorithm. The solution can be used to plot the efficient frontier between the robust NPV and the project value. Computational experiments revealed that the algorithm performs well compared to tabu search and an MIP solution using a commercial solver, and that the RL actions can be leveraged for coping with positive and negative cashflows. The utility of our work lies in its ability to respond to decision makers’ information needs, providing a framework for tradeoff analysis to select the most adequate project plan that satisfies stakeholders’ requirements.
Original language | English |
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Pages (from-to) | 1 |
Number of pages | 1 |
Journal | IEEE Access |
DOIs | |
State | Accepted/In press - 2023 |
Keywords
- Costs
- Integer programming
- Integer programming
- Project management
- Quality function deployment
- Reinforcement learning
- Schedules
- Simulation
- Stakeholders
- Stochastic processes
- project management
- project scheduling
- reinforcement learning
- simulation
ASJC Scopus subject areas
- General Computer Science
- General Materials Science
- General Engineering