Abstract:
In the real world, risk and uncertainty are two natural properties in the implementation of Mega projects. Most projects fail to achieve the pre-determined objectives due to uncertainty. A linear integer programming optimization model was used in this work to solve a problem in order to choose the most appropriate risk responses for the project risks. A mathematical model, in which work structure breakdown, risk occurrences, risk reduction measures, and their effects are clearly related to each other, is proposed to evaluate and select the project risk responses. The model aims at optimization of defined criteria (objectives) of the project. Unlike similar previous studies, in this study, the relationship between risk responses during implementation has been considered. The model is capable of considering and optimizing different criteria in the objective function depending on the kind of project. In addition, a case study related to petroleum projects is presented, and the corresponding figures are analyzed.
Machine summary:
An optimal model for Project Risk Response Portfolio Selection (P2RPS) (Case study: Research institute of petroleum industry) Rahman Soofifard, Morteza Khakzar Bafruei Industrial Engineering Department, Institute for Technology Development, Sharif University of Technology, Tehran, Iran (Received: December 16, 2015; Revised: September 10, 2016; Accepted: September 17, 2016) Abstract In the real world, risk and uncertainty are two natural properties in the implementation of Mega projects.
A mathematical model, in which work structure breakdown, risk occurrences, risk reduction measures, and their effects are clearly related to each other, is proposed to evaluate and select the project risk responses.
The objective of risk management increases the possibility of project success by systematic identification and assessment of risks, presenting methods to avoid or reduce them, and maximizing opportunities (Chapman & Ward, 2003).
Considering the effects of these responses on the project objectives and the results of synergism between the responses, the numbers of risk responses optimizing the objective function are selected from a portfolio of identified responses using a mathematical optimization model.
The objective function is minimization of the costs of risk response implementation and its constraints including combination of strategies (Zhang & Fan, 2014).
Considering the parameters and variables of the problem, the Binary Integer Programming (BLP) model of this work is presented as follows: (View the image of this page) In this model, the objective function aims at optimizing the quantity obtained from each assessment criterion including the sum of effects resulting from the selection of each risk response in that criterion as well as the sum of effects of synergism for each risk.