Using probability impact matrix (PIM) in analyzing risk factors affecting the success of oil and gas construction projects in Yemen

Mining algorithm of accumulation sequence of unbalanced data based on probability matrix decomposition

Due to the inherent characteristics of accumulation sequence of unbalanced data, the mining results of this kind of data are often affected by a large number of categories, resulting in the decline of mining performance. To solve the above problems, the performance of data cumulative sequence mining is optimized. The algorithm for mining cumulative sequence of unbalanced data based on probability matrix decomposition is studied. The natural nearest neighbor of a few samples in the unbalanced data cumulative sequence is determined, and the few samples in the unbalanced data cumulative sequence are clustered according to the natural nearest neighbor relationship. In the same cluster, new samples are generated from the core points of dense regions and non core points of sparse regions, and then new samples are added to the original data accumulation sequence to balance the data accumulation sequence. The probability matrix decomposition method is used to generate two random number matrices with Gaussian distribution in the cumulative sequence of balanced data, and the linear combination of low dimensional eigenvectors is used to explain the preference of specific users for the data sequence; At the same time, from a global perspective, the AdaBoost idea is used to adaptively adjust the sample weight and optimize the probability matrix decomposition algorithm. Experimental results show that the algorithm can effectively generate new samples, improve the imbalance of data accumulation sequence, and obtain more accurate mining results. Optimizing global errors as well as more efficient single-sample errors. When the decomposition dimension is 5, the minimum RMSE is obtained. The proposed algorithm has good classification performance for the cumulative sequence of balanced data, and the average ranking of index F value, G mean and AUC is the best.

The Current Risk Management Practices and Knowledge in the Construction Industry

Construction is a critical sector of any economy in terms of value production, labor, and contributing to the gross national product. Managing risk is a relatively young area in Yemen’s construction sector, but it is gaining traction as building activity and competition rise. Construction firms mitigate risk by using a variety of risk management methods. Therefore, there is a need to assess these procedures in order to detect shortcomings. This research aims to establish the existing risk management strategies used in Yemeni building projects. Survey questionnaires were used to collect data. Respondents were drawn from Yemeni construction businesses. Risk management is not executed systematically, intentionally, or continuously, and most firms’ risk management procedures are reactive, semipermanent, informal, and unstructured, with no or few dedicated resources to address risks. This strategy is inconsistent with generally accepted risk management principles. Nonetheless, the findings suggest a general understanding of risk management and a willingness to learn from previous errors. The study of the findings suggests that risk identification approaches such as judgment and historical data are employed for risk analysis, and that the industry typically attempts to avoid or transfer risks in Yemeni building projects. The results shed light on the shortcomings of Yemen’s project management practices. To guarantee that construction projects obtain maximum value for money, project managers of big construction businesses in Yemen need a strong understanding of and training in globally accepted systematic risk management procedures. Finally, this study can help future stakeholders determine how to work together to manage risk.

A review on prospective risks and mitigation for oil and gas projects: implication for Indian CGD companies

Purpose

This study aims to focus on identifying the business risks that cause a delay in the oil and gas projects and suggest the way forward toward the better development of the city gas distribution (CGD) sector in India by suggesting the appropriate mitigation strategies.

Design/methodology/approach

The study is a systematic review of literature on risks causing a delay in oil and gas projects. Comprehensive literature was carried out following a seven-step model to develop an exhaustive list of risk classifications and factors, risk identification methods and strategies to mitigate the risks. Weighted average ranking method is used to identify the top ten risks affecting oil and gas projects.

Findings

This research identifies the top ten risks frequently impacting the oil and gas projects, which are project cost, improper project management, change in economic parameters, currency exchange rate, government regulations and laws, contractor and subcontractors issues, lack of skilled labor, delay in approvals, health and safety issues and force majeure. These risks are primarily responsible for cost overrun and project delay. Additionally, this study recommends the implementation of joint risk management to avoid CGD project delay.

Originality/value

The CGD industry is in the growing stage with many projects under construction. However, there is a lack of research to manage risks in the CGD project. This study contributes to the limited literature available on risk management in oil and gas projects. Additionally, it highlights the need for further research to explore the different risks factors affecting the CGD business and its operations and subsequently develop appropriate mitigation strategies.

Hybrid fuzzy MCDM and FMEA integrating with linear programming approach for the health and safety executive risks: a case study

Purpose

The purpose of this study is to present a new failure mode and effects analysis (FMEA) approach based on fuzzy multi-criteria decision-making (MCDM) methods and multi-objective programming model for risk assessment in the planning phase of the oil and gas construction projects (OGCP) in Iran.

Design/methodology/approach

This research contains multiple steps. First, 19 major potential health and safety executive (HSE) risks in OGCP were classified into six categories with the Delphi method. These factors were distinguished by the review of project documentation, checklist analysis and consulting with experts. Then, using the fuzzy SWARA method, the authors calculated the weights of major HSE risks. Subsequently, FMEA and PROMETHEE approaches were used to identify the priority of main risk factors. Eventually, a binary multi-objective linear programming approach was developed to select the risk response strategies, and an augmented e-constraint method (AECM) was used.

Findings

Regarding the project triple well-known constraints of time, cost and quality, which organizations usually confront, the HSE risks of OGCP were identified and prioritized. Also, the appropriate risk response strategies were also suggested to the managers to be adopted regarding the situations.

Originality/value

The present research points at the HSE risks’ assessment integrating the fuzzy FMEA, step-wise weight assessment ratio analysis and PROMETHEE techniques with the AECM. Further to the authors’ knowledge, the quantitative assessment of the HSE risks of OGCP has not been done using the combination of the fuzzy FMEA, MCDM and AECMs.