4 research outputs found
Unmanned Aerial Vehicle Path Planning using Bat Algorithm
No full textUnmanned Aerial Vehicles (UAV) was introduced after World War II. In 1980’s UAV consider as important weapon system. Initially UAV needs initial position and target position. In this paper bat algorithm is proposed with mixed objective constraints which helps in directing the UAV. The process is initialized by generating the initial population of bat. Then by updating the population size and generation of bat the fitness value with minimum frequency is found that helps to avoid convergence among UAV. Finally the evaluation which gives minimum frequency is considered as optimal solution
Enhanced picture fuzzy and centrality based EDAS-BW approach and its application in healthcare
No full textPicture fuzzy set (PFS) is the generalization of intuitionistic fuzzy set (IFS) that allows the inclusion of a neutral degree along with the membership and non-membership degrees, which more effectively facilitates the expression of a range of human responses, encompassing affirmation, neutrality, negation and rejection. The challenges arise when the level of rejection is high. To overcome this hurdle, the concept of picture fuzzy set-triple parameters (PFS-TP) with the condition ζ⁎(x)+φ⁎(x)+ϖ⁎(x)=1 is introduced in this study by eliminating the refusal degree. To demonstrate the practicality of PFS-TP, a picture fuzzy decision-making model is designed by using evaluation based on distance from average solution (EDAS), Dempster-Shafer theory (DST) and the best-worst method (BWM). Moreover, the aggregation operator, score and accuracy functions are defined for PFS-TP to support the model. The EDAS and BWM methods are utilized to rank the alternatives and weight the criteria, DST helps to aggregate the ambiguous information. A digraph is constructed to identify influential alternatives using degree centrality. The model is applied to select the most effective COVID-19 diagnostic kit and prioritized RT-PCR as the effective kit. The obtained outcomes are corroborated through sensitivity and comparative analyses
Intuitionistic fuzzy MAUT-BW Delphi method for medication service robot selection during COVID-19
No full textCoronavirus Disease 2019 (COVID-19), a new illness caused by a novel coronavirus, a member of the corona family of viruses, is currently posing a threat to all people, and it has become a significant challenge for healthcare organizations. Robotics are used among other strategies, to lower COVID’s fatality and spread rates globally. The robot resembles the human body in shape and is a programmable mechanical device. As COVID is a highly contagious disease, the treatment for the critical stage COVID patients is decided to regulate through medication service robots (MSR). The use of service robots diminishes the spread of infection and human error and prevents frontline healthcare workers from exposing themselves to direct contact with the COVID illness. The selection of the most appropriate robot among different alternatives may be complex. So, there is a need for some mathematical tools for proper selection. Therefore, this study design the MAUT-BW Delphi method to analyze the selection of MSR for treating COVID patients using integrated fuzzy MCDM methods, and these alternatives are ranked by influencing criteria. The trapezoidal intuitionistic fuzzy numbers are beneficial and efficient for expressing vague information and are defuzzified using a novel algorithm called converting trapezoidal intuitionistic fuzzy numbers into crisp scores (CTrIFCS). The most suitable criteria are selected through the fuzzy Delphi method (FDM), and the selected criteria are weighted using the simplified best–worst method (SBWM). The performance between the alternatives and criteria is scrutinized under the multi-attribute utility theory (MAUT) method. Moreover, to assess the effectiveness of the proposed method, sensitivity and comparative analyses are conducted with the existing defuzzification techniques and distance measures. This study also adopt the idea of a correlation test to compare the performance of different defuzzification methods
A Fuzzy Hybrid MCDM Approach for Assessing the Emergency Department Performance during the COVID-19 Outbreak
No full textThe use of emergency departments (EDs) has increased during the COVID-19 outbreak, thereby evidencing the key role of these units in the overall response of healthcare systems to the current pandemic scenario. Nevertheless, several disruptions have emerged in the practical scenario including low throughput, overcrowding, and extended waiting times. Therefore, there is a need to develop strategies for upgrading the response of these units against the current pandemic. Given the above, this paper presents a hybrid fuzzy multicriteria decision-making model (MCDM) to evaluate the performance of EDs and create focused improvement interventions. First, the intuitionistic fuzzy analytic hierarchy process (IF-AHP) technique is used to estimate the relative priorities of criteria and sub-criteria considering uncertainty. Then, the intuitionistic fuzzy decision making trial and evaluation laboratory (IF-DEMATEL) is employed to calculate the interdependence and feedback between criteria and sub-criteria under uncertainty, Finally, the combined compromise solution (CoCoSo) is implemented to rank the EDs and detect their weaknesses to device suitable improvement plans. The aforementioned methodology was validated in three emergency centers in Turkey. The results revealed that the most important criterion in ED performance was ER facilities (14.4%), while Procedures and protocols evidenced the highest positive D + R value (18.239) among the dispatchers and is therefore deemed as the main generator within the performance network
