Robust and resilience budget allocation for projects with a risk-averse approach: A case study in healthcare projects

Abstract

One of the critical factors that force to complete projects in minimum time is Budget Allocation for Projects (BAP). This subject is vital in large-scale projects like provinces and healthcare projects that affect people's lives. The innovation of this research considers Robust, Resilience, and Risk-averse BAP (3RBAP). We utilize a hybrid robust stochastic optimization by considering worst-case in the objective function for robustness. This model minimizes the weighted expected and minimum progress functions to tackle budget fluctuation. The case study is about healthcare project construction. Finally, we assign the budget for healthcare projects with uncertain scenarios. After sensitivity analysis, we obtain that by employing 3R, we can see that the progress function is 17.07% less than without 3R. In addition, if the conservativity coefficient rises to 50%, the progress function will decrease to 7.32%, but the time computation changes a little. However, declining the resiliency coefficient makes decreases and disrupts the budget assignment. As a result, the progress function reduces. Finally, the time computation grows smoothly by increasing the scale of problems, and the progress function decreases. Because the number of projects is very much, maximizing the expected value of the progress function decreases. Eventually, by changing to exponential and sine functions, the progress function improved 41.46%.