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SummaryThis paper presents a reliable, easy and more objective approach for ranking and determining preference in a multi-criteria decision-making problem within the shipping industry. Through the integration of the improved score function, fuzzy Shannon's entropy method and the interval-valued intuitionistic fuzzy M-TOPSIS method, for ranking and for representing the aggregated effect of positive and negative evaluations in the performance ratings of the alternatives based on interval-valued intuitionistic fuzzy set (IVIFS) data. The integration of the improved score function, fuzzy Shannon's entropy method and the intervalvalued intuitionistic fuzzy M-TOPSIS method in this paper has provided a whole new approach for solving multi-criteria decision-making problems. The improved score function which is applied to the calculation of the separation measures of each alternative from the positive and negative ideal solutions. Reflect and model the fuzziness and hesitation of the decision-maker subjective assessment, while the fuzzy Shannon's entropy method is been used for calculating the criteria weight. The proposed method has successfully been applied to rank and determined the most appropriate shipping partner for a shipping company located in Malaysia, and for a modified hypothetical example which is based on the selection of a preferred Ship as a reference for a new design. The model has been compared with existing model and we can conclude, it provides a better alternative method for ranking and for the determination of preference in a multi-criteria decision-making problem.

A flexible model which is based on a Triangular intuitionistic flexibility ranking and aggregating (TIFRA) operator is proposed for failure detection and reliability management in a Wind Turbine system. The model which is employed when there are limited research data and valid source of information, uses expert-based knowledge/opinion for failure detection and reliability management. The results from the study concludes that, the most important area affected by failure with respect to the failure criteria used, includes; oil level sensor tilt sensors for tower installation and accelerometers for tower sway (A2), Pressure sensor for blade monitoring (A3), and the Pitch actuator (A4). The main advantage of the proposed method is that it provides advanced information about faults that identifies the intensity of the system behavior also; the method provides a more complete view of the reliability management and root cause of failure in the Wind Turbine (WT) system using the flexibility parameter in the model.

Socio-technical and economic attributes consideration are very important during a renewable energy technology selection for a community. When decision-makers considered these attributes under a dynamic nature, they arrive at a robust decision. Hence, this study proposes an integrated model for renewable energy technologies evaluation under a dynamic condition. We developed the model using dynamic intuitionistic fuzzy Einstein geometric averaging operator, intuitionistic fuzzy entropy, and the intuitionistic fuzzy technique for order of preference by similarity to ideal solution method (TOPSIS). This model's applicability was tested using five renewable energy technologies-solar (PT 1), wind (PT 2), hydroelectricity (PT 3), geothermal (PT 4) and biomass (PT 5) and five attributes (risk factor, payback reliability, social benefit, change in demand and cost). Based on five energy experts, from academia and industry, opinions, the proposed model identified biomass energy technology as the most suitable energy technology. Three existing multi-criteria models were used to verify the proposed model; the proposed model performance was consistent with the existing models' results. From most suitable the least suitable, the model ranked these technologies PT 5 > PT 2 > PT 3 > PT 1 > PT 4 .

The rapid development of modern wind turbine technology has led to increase demand for improving system reliability and practical concern for robust fault monitoring scheme. This paper presents the investigation of a 5MW Dynamic Wind Turbine Energy System that was designed to sustain condition monitoring and fault diagnosis with the goal of improving the reliability operations of universal practical control systems. A hybrid stochastic technique is proposed based on an augmented observer combined with eigenstructure assignment for the parameterisation and the genetic algorithm (GA) optimisation to address the attenuation of uncertainty mostly generated by disturbances. Scenarios-based are employed to explore sensor and actuator faults that have direct and indirect impacts on modern wind turbine system, based on monitoring components that are prone to malfunction. The analysis is aimed to determine the effect of concerned simulated faults from uncertainty in respect to environmental disturbances mostly challenged in real-world operations. The efficiency of the proposed approach will improve the reliability performance of wind turbine system states and diagnose uncertain faults simultaneously. The simulation outcomes illustrate the robustness of the dynamic turbine systems with a diagnostic performance to advance the practical solutions for improving reliable systems.