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The optimization and tradeoff of cost-time-quality-risk in one dimension and this four-dimensional problem in ambiguous mode and risk can be neither predicted nor estimated. This study aims to solve this problem and rank fuzzy numbers using an innovative algorithm “STHD” and a special technique “radius of gyration” (ROG) for fuzzy answers, respectively.

First, it is the optimization of a fully fuzzy four-dimensional problem which has never been dealt with in regard to risk in ambiguous mode and complexities. Therefore, the risk is a parameter which has been examined neither in probability and estimableness mode nor in the ambiguous mode so far. Second, it is a fully fuzzy tradeoff which, based on the principle of incompatibility “Zadeh, 1973”, proposes that when the complexity of a system surpasses the limited point, it becomes impossible to define the performance of that system accurately, precisely and meaningfully. The authors believe that this principle is the source of fuzzy logic. Third, for calculating and ranking fuzzy numbers of answers, a special technique for fuzzy numbers has been used. Fourth, For the sake of ease, precision and efficiency, an innovative algorithm called the technique of hunting dolphins “STHD” has been used. Finally, the problem is very close to reality. By applying risk in ambiguous mode, the problem has been realistically looked at.

The results showed that the algorithm was highly robust, with its performance depending very little on the regulation of the parameters. Ranking fuzzy numbers using the ROG indicated the flexibility of fuzzy logic, and it was also determined that the most appropriate regulations were to ensure low time, risk and cost but maximum quality in calculations, which were produced non-uniformly based on the levels of Pareto answers.

The ROG and Chanas Fuzzy Critical Path Method as developed by other researchers have been used. Despite the increase in limitations, parameters can develop. The originality of this study with regard to evaluating the results of tradeoff combinatorial optimization is upon decision-making which has a special and highly strategic role in the fate of the project, with the research been conducted with a special approach and different tools in a fully fuzzy environment.

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

The aim of this paper is to predict and minimize the risks of oil, gas and petrochemical projects. Besides, reducing the likelihood of occurrence and minimizing risks impact on the projects to reduce the probable costs and improve the economic situation is another purpose of this paper.

This paper provides a fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) – a technique that assist to solve decision-making problems – and IP (Impact & Probability) table methods to identify and analyze critical risks in energy projects, and then fuzzy Binary Logistic Regression (BLR) in order to predict the probability of each level of risk for more efficient risk management in projects. Furthermore, in this paper, the fuzzy BLR (FBLR) is optimized such that the probability of a high level of risk for the implementation of the project has been minimized using meta-heuristic algorithm.

The results from the point of view of experts show that combination of fuzzy DEMATEL with FBLR approach as well as using SA algorithm, in order to optimize the high level of risks, can provide a smart approach to managing risks with more success.

The application of the proposed method is illustrated via a real data set from energy projects.

We propose combined fuzzy DEMATEL and FBLR methods to predict and optimize the risks of the energy projects, which is the innovation of this paper.

IT projects carry high risk of failure due to the existence of great obstacles during the planning, application and development phase. The projects' risks are multi-dimensional, and they must be assessed by multi-attribute decision-making methods. The purpose of this article is to provide analytic tools to evaluate the learning organization's IT project risks under incomplete and vague information. It was also aimed to place the risk in a proper category and predict the level of it in advance to develop strategies to counteract the high-risk factors.

In this study, three mutual approaches were used to analyze the organizations IT applications. These are enterprise analysis for determination of information requirements of organization, payback method for IT project financial analysis and risk assessment using fuzzy sets and systems. The developed fuzzy model is an expert system which can predict the category of risk for IT projects in learning organizations.

This study revealed that the greatest obstacles to IT project success were the lack of organizational learning, resistance to change, etc. User involvement limitation was found to be also one of the common reasons of IT project failure. The information sharing policy was determined to increase productivity of employees in offices and to decide the creators and the users of knowledge. Learning is a continuous process for organizational transformation. Individual and organizational learning were searched to minimize the level of risks factors and learning culture. IT project risks were categorized properly using fuzzy sets and systems to reduce or even eliminate high risks.

The paper is original and gives the first such work for industry.

This paper aims to evaluate the risk factors and determines the overall risk level (ORL) of public-private-partnership (PPP) power projects in Ghana using fuzzy synthetic evaluation methodology (FSEM).

In this paper review of literature led to the development of a 67-factor risk list which was ranked by experts and industry practitioners through a questionnaire survey.

These factors were grouped into principal risk factors (PRFs) using component analysis and they served as the input variables for fuzzy analysis. The seven components were: Contract and Payment risks, Environmental risks, Financial and Cost risks, Legal and Guarantee risks, Operation risks, Socio-Political and Performance risks (SPR) and Tender and Negotiation risks. Study showed that the ORL of Ghanaian PPP power projects is high implying they are risky to both the public and private sectors. Fuzzy analysis also confirmed SPR as the most critical principal factor.

This study is significant and demonstrates that fuzzy methodology can be used as a useful risk evaluation tool and risk assessment framework for private investors, policy makers and public sector.

The purpose of this paper is to present a newly developed fuzzy-set based model for estimating, allocating, depleting, and managing contingency fund over the life cycle of construction projects.

Fuzzy set theory is utilized in the design and development of proposed contingency modelling framework to incorporate uncertainties associated with the development phases of construction projects. A set of developed indices, measures, and ratios are introduced to quantify and characterize these uncertainties. The developed framework is designed to incorporate expert opinion and provide user-system interaction.

The results obtained from the application of the developed framework on actual project case not only illustrate its accuracy, but also demonstrate its capabilities for contingency management over life cycle of construction projects. Unlike other methods, the framework provides project managers with structured method for contingency depletion utilizing a set of depletion curves and selection factors.

The novelty of the developed framework lies not only in its new developments for contingency estimating but also its modelling for contingency allocation and depletion. It is expected to be of direct value to industry professionals and academics interested in contingency management over the entire life cycle of construction projects. The proposed framework provides management functions and features beyond those generated through Monte Carlo simulation and even those developed using fuzzy set theory.

Public-private partnership (PPP) power projects are associated with varying risk factors. This paper aims to develop a fuzzy quantitative risk allocation model (QRAM) to guide decision-making on risk allocation in PPP power projects in Ghana.

A total of 67 risk factors and 9 risk allocation criteria were established from literature and ranked in a two-round Delphi survey using questionnaires. The fuzzy synthetic evaluation method was used in developing the risk allocation model.

The model’s output variable is the risk allocation proportions between the public body and private body based on their capability to manage the risk factors. Out of the 37 critical risk factors, the public sector was allocated 12 risk factors with proportions = 50%, while the private sector was allocated 25 risk factors with proportions = 50%.

To the best of the authors’ knowledge, this research presents the first attempt in Ghana at endeavouring to develop a QRAM for PPP power projects. There is confidence in the model to efficiently allocate risks emanating from PPP power projects.

Successful implementation of construction projects is one of the crucial factors for the economic development of every country. The main part of the countries’ capital is allocated to civil and infrastructure projects annually, most of which are accomplished with delay. Construction projects are often criticized for overrunning time and budgets. Analyzing the factors causing delay is essential for omitting them and timely implementation of these projects. Due to the importance of oil projects, this study aims to investigate and analyze the factors causing a delay in Iran’s oil construction projects.

In this research, after a broad literature review, using the fuzzy Delphi method, a total of 75 delay factors were identified under 11 major categories of owner, contractor, consultant, equipment, labor, materials, design, contract and contractual relations, laws and regulations, environmental factors, and managerial factors. Then, by using the best-worst method, the factors were prioritized.

The results showed that sanction, governmental management systems, weak project management by the contractor, technical and managerial weaknesses of the consultant, financial problems and delay in payment by the owner, low efficiency of the equipment, low productivity of the workforce, changes in laws and regulations, inappropriate organizational structure linking to the project, changes in the design, and changes in the price of materials are the most crucial factors causing a delay in Iran’s oil construction projects.

These findings are expected to have significant contributions to Iran’s oil construction industry in controlling the time overruns in construction contracts.

The main contribution of this study is to develop a comprehensive framework in which, causes of delay in Iran’s oil construction projects are addressed and prioritized.

This paper aims to develop a process for prioritizing project risks that integrates the decision-maker's risk attitude, uncertainty about risks both in terms of the associated probability and impact ratings, and correlations across risk assessments.

This paper adopts a Monte Carlo Simulation-based approach to capture the uncertainty associated with project risks. Risks are prioritized based on their relative expected utility values. The proposed process is operationalized through a real application in the construction industry.

The proposed process helped in identifying low-probability, high-impact risks that were overlooked in the conventional risk matrix-based prioritization scheme. While considering the expected risk exposure of individual risks, none of the risks were located in the high-risk exposure zone; however, the proposed Monte Carlo Simulation-based approach revealed risks with a high probability of occurrence in the high-risk exposure zone. Using the expected utility-based approach alone in prioritizing risks may lead to ignoring few critical risks, which can only be captured through a rigorous simulation-based approach.

Monte Carlo Simulation has been used to aggregate the risk matrix-based data and disaggregate and map the resulting risk profiles with underlying distributions. The proposed process supported risk prioritization based on the decision-maker's risk attitude and identified low-probability, high-impact risks and high-probability, high-impact risks.

This paper investigates the risks involved in the Indian foodgrain supply chain (FSC) and proposes risk mitigation taxonomy to enable decision making.

This paper used failure mode and effect analysis (FMEA) for risk estimation. In the traditional FMEA, risk priority number (RPN) is evaluated by multiplying the probability of occurrence, severity and detection. Because of some drawbacks of the traditional FMEA, instead of calculating RPN, this paper prioritizes the FSC risk factors using fuzzy VIKOR. VIKOR is a multiple attribute decision-making technique which aims to rank FSC risk factors with respect to criteria.

The findings indicate that “technological risk” has a higher impact on the FSC, followed by natural disaster, communication failure, non-availability of procurement centers, malfunctioning in PDS and inadequate storage facility. Sensitivity analysis is performed to check the robustness of the results.

The outcomes of the study can help in deriving detailed risk mitigation strategy and risk mitigation taxonomy for the improved resilience of FSC.

Specifically, this research investigates the risks for foodgrains supply chain system for a developing country such as India, an area which has received limited attention in the present literature.