This research aimed to systematically review the development studies pertaining to forest biomass and bioenergy supply chain resilience (SCR). In this regard, a mixed procedure was implemented in order to explore and analyze the relevant publications, and to answer the research questions. First, the databases and journals working on forest biomass and bioenergy supply chains (SCs) were identified based on the indices of the review process and the indices of the barriers and enablers. Next, data refinement was employed to filter the publications into four levels and determine the semifinal cases. Moreover, the references of the semifinal publications were tracked in order to achieve the final cases. Consequently, 88 papers were determined as the final cases through which the barriers and enablers were explored and analyzed. Furthermore, in order to meet the research gap in this area and prove the connections of those barriers and enablers with the resilience capability, their relationships with the main resilience factors were investigated. According to the assessment, the findings of this research on the definition, barriers and enablers of forest biomass and bioenergy SCR can be applied as a basis for the comprehension and optimization of the structure of SCs in the forest biomass and bioenergy industries.
Managers require a good understanding about the nature of risks involved in a construction project because the duration, quality, and budget of projects can be affected by these risks. Thus, the identification of risks and the determination of their priorities in every phase of the construction can assist project managers in planning and taking proper actions against those risks. Therefore, prioritizing risks via the risk factors can increase the reliability of success. In this research, first the risks involved in construction projects has been identified and arranged in a systematic hierarchical structure. Next, based on the obtained data an Adaptive Neuro-Fuzzy Inference System (ANFIS) has been designed for the evaluation of project risks. In addition, a stepwise regression model has also been designed and its results are compared with the results of ANFIS. The results show that the ANFIS models are more satisfactory in the assessment of construction projects risks. Our proposed methodology can be applied by managers of construction projects and practitioners to assess of risk factor of construction projects in a proper manner.
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