Fuzzy Approach Model to Portfolio Risk Response Strategies

*Yaser Rahimi*

### **Abstract**

Risk management and control of project risks have been the intrinsic characteristics of high-rise building projects in a changing built environment. In this research, a novel bi-objective model for the best mixture of projects is proposed. The first objective focuses on maximizing profits and efficiency of risk responses, and the second objective aims at minimizing project direct cost including machinery, human, and material costs to implement proper risk responses over a planning horizon under uncertainty. In this model, risks of the projects are controlled by time, quality, and cost constraints, and the most optimum risk response strategies (RRSs) are selected to eliminate or reduce the impacts of the risks. Thus, the combination of optimum projects with the best RRSs can be selected for an organizational portfolio model. Finally, to assess the solution method and the proposed model, the empirical result and sensitivity analysis are carried out. Ten large-scale high-rise building projects and their associated risks are evaluated as cases in this study.

**Keywords:** building engineering, fuzzy system, portfolio selection, project risk management

### **1. Introduction**

The purpose of the risk management framework is to assist the organization in integrating risk management into significant activities and functions. The effectiveness of risk management will depend on its integration into the governance of the organization, including decision-making. This requires support from stakeholders, particularly top management. Framework development encompasses integrating, designing, implementing, evaluating and improving risk management across the organization.

Managing risks at all levels is an active process involving continuous planning, analysis, response, and monitoring and control. The execution of response strategies should be anticipatory and implemented by trigger events that launch response actions before the risk materializes so that opportunities (positive risks) may be enhanced or threats (negative risks) may be diminished. Even within an active process of continuous risk identification, risk management at the program, project, and operations areas are traditionally approached from a prescriptive, processbased perspective. However, within complex systems such as portfolios, risks may not be managed in the traditional or simple sense. Complexity requires a less prescriptive approach. In many cases, the execution of risk response strategies at the portfolio level involves the establishment of projects within the portfolio's component programs or as part of continuing operations to address specific opportunities or threats (positive or negative risks) that have either materialized or have

had a significant increase in the probability of occurrence as indicated by a trigger event occurring. There is an important distinction between portfolio risk management and risk management at the program or project level. In many cases, the portfolio manager should delegate risk response measures to subordinate programs or projects within the portfolio. A desired outcome from portfolio risk management is to utilize a structured risk planning and response effort in order to reduce management inaction and decision delay. Risk identification analysis and response planning acknowledge the limits of data and the lack of clear, unambiguous, and actionable information concerning many management factors at the portfolio level. Various possible risk scenarios are studied and response plans developed to limit the impact of the data and information disconnect described above. Through portfolio risk management, senior leadership and portfolio management staff are provided with courses of action or management options that assist in making decisions involving risk with incomplete information.

The importance of an appropriate selection of one project due to the combination of the selected projects for successful portfolio management is inevitable. Many companies try to implement a group of relevant projects as a portfolio to satisfy their synergy and economize their cost through efficient project management. Furthermore, it is needed to manage the risks of each project through the standard risk management process after the creation of the appropriate portfolio. The portfolio has an important role in managing a group of relevant projects so that they bring benefits and values. In the portfolio level, risk management requires a balanced attitude and management judgment exercises in two stages: the first stage is associated with the portfolio creation phase and the second one is allocated to the implementation phase of portfolio projects. We only benefit from the synergy and saving resulted from the portfolios projects management in the case of active risk management. A risk strategy response (RSS) is one of the most important processes of risk management. Therefore, selecting the appropriate projects and managing project risks are simultaneously two appropriate approaches to increase both revenue and profits of project-based organizations. In this research, the main aim is to choose an optimum portfolio of project investment considering its risk response cost and multi-term planning. Project portfolio selection observes the organization's objectives in a planning horizon without outpacing available resources. Schniederjans and Santhanam [1] classified the system's objectives and preferences as financial benefits, intangible benefits, availability of resources, and risk level of the project portfolio, so project risk assessment was a key element in their study [2].

Badri et al. [3] presented a binary goal programming model for the project selection of an information system. Wei and Chang [4] presented a portfolio choice model based on enterprise strategy considering customer's resource and capability, project performance and project delivery, and project risk constraints. Project risks are categorized into three types: market risk, technical risk, financial risk. In any aspect of a project, risk can emerge. The nature of risk is uncertainty. For each project, risks should be identified and analyzed, and to cope with these risks, proper RRSs must be employed [5–11]. Tang et al. [9] developed a new solution method to the lean 6-sigma portfolio management as a binary quadratic programming problem. Muriana and Vizzini [12] presented a certain method to determine the risk of the Work Progress Status for assessing and preventing project risk.

On the other hand, Rahimi et al. [13] proposed a mathematical model, in which different risks are considered for activities so that different responses can be selected for each risk. Also, the risk responses are not considered as independent, and responses are associated with each other. Indeed, choosing the responses, which overlap each other, can affect their results, time, cost, and quality of the project. The objective function used different evaluation criteria and tried to choose the optimum responses, which maximizes these evaluation criteria. Ben-David and Raz

#### *Fuzzy Approach Model to Portfolio Risk Response Strategies DOI: http://dx.doi.org/10.5772/intechopen.95009*

[14] considered the cost of implementing strategies and incorporated them into an RRS selection problem. Ben-David et al. [15] extended their previous work by providing a mathematical model that facilitates computer implementation of the model. Because of the risk abatement actions, a selection problem is a complex one. Therefore, they proposed a branch-and-bound algorithm and two heuristic algorithms [16, 17]. Zhang and Fan [18] integrated all three key elements in project management (i.e., project expenditure, project planning horizon, and project quality). They proposed a new efficient solution for the mathematical model of the RRS.

Reviewing the aforementioned discussions and literature, we understand that there are gaps in (1) selecting the best projects portfolio that the effect of risk in selected projects is controlled [18], and (2) selecting projects to check the balance between the total cost of the selected projects and the profit of the selected projects, and all the predicted risk response effects. Furthermore, some of the parameters in the real-world are uncertain and can cause a high degree of uncertainty on a designed network [19].

To overcome and fulfill these gaps, for the first time, we develop a mathematical model for selecting the best projects and control risks of each selected projects under uncertainty. In this research, we investigate the trade-off between the total cost of the selected projects including all three types of resources (e.g., human, machine, raw materials) and implanting proper risk responses-and the net profit of the selected projects, and all the approximated risk response effects. The important items which this research contributes are as follows:

