In recent years, sudden global energy demand has led to the gradual exhaustion of fossil fuel, the world’s main energy resource. With the negative impact of fossil fuel on the environment, governments and organizations have increased R&D funding on renewable energy resources such as solar and wave energy. Vietnam has a great potential for developing wave energy projects owing to the presence of a long coastline and vast ocean. Choosing an optimal location for wave-based power plant projects is a multicriteria decision that requires understanding the quantitative and qualitative elements for assessing the balance of factors when trying to reach the most accurate result. This study proposes a multi-criteria decision-making (MCDM) model, fuzzy-analytic hierarchical process (FAHP), and weighted aggregated sum product assessment (WASPAS) in evaluating potential wave energy stations at the Vietnamese coastline. The authors identify all criteria and sub-criteria affecting the wave power plant location selection process through literature review and expert interview. Selection criteria include wave height, the distance between two waves, number of waves, wind speed, wind duration, ocean depth, turbulence, water quality, coastal erosion, shipping density, protection laws, labor resources, safety conditions, and other related factors. FAHP was used to determining the weights of the identified criteria in the first stage of this study. Finally, the WASPAS model was employed to rank all the alternatives involved in making an effective decision. This study aimed to develop a tool to enhance decision-making when solving fuzzy multi-criteria problems. We propose a real-world model for the effectiveness of the proposed model.
While striving to satisfy owners' needs, to minimize construction cost and pursue the maximum profits under a fixed contract amount and duration is always one of the most important objectives of contractors. Only a sound work plan, which includes optimal working sequences and perfect timing for executing each individual activity, can enhance the work efficiency and enable contractors to fulfill the contract at the lowest cost. This research develops a contractors' optimal S-curve model, which can optimize the allocation of resources along the project schedule, under major assumptions that for a specific task, there is a trade-off relationship in terms of work productivity between two different types of resources and there are costs for resource mobilization and demobilization. The S-curve established can be used as a baseline to measure the extra cost caused by changes and disruptions in the course of construction and the overall project performance at the completion of the project.
The outbreak of the novel coronavirus pneumonia (COVID-19) in 2019 and the 2022 war in Ukraine have had profound global impacts on travel and logistics, disrupted the material supply chain, significantly influenced the cost and progress of construction projects, and further impacted the operational effectiveness of firms. Despite some existing studies providing valuable insights into the impact of COVID-19 on the construction industry, there remain research gaps that need to be addressed. Prior studies have mainly focused on the immediate impact factors of the pandemic, such as supply chain disruptions and workforce shortages, and strategies for effectively reducing or eliminating these risks. However, there is a need for research that delves into the long-term implications of these disruptions. So far, no relevant research has quantified the broader impact of the epidemic. Thus, this study aims to analyze the effects of the pandemic and the war on 136 construction industry professionals, their projects, and firms through literature review, questionnaire surveys, and expert interviews. The study compiles a list of significant risk factors for construction projects between 2019–2022, including their probability of occurrence, impact over time, and overall cost. The study also analyzes and discusses the impact of these high-risk factors as of 2022. To quantify the impact, cost, and level of exposure to these risks suffered by actual construction projects over this period, the Monte Carlo simulation method is introduced. This approach provides contractors with early prediction of risks and appropriate responses to mitigate risks.
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