Implementation of a fuzzy decision support system for selection of hand tractor assembly suppliers

Supplier selection is a complex problem in the current Industry 4.0 era. The large number of suppliers with different performance qualities makes it difficult for the company's internal parties to choose the appropriate supplier. The suitability of quality suppliers needed to supply raw materials needed by the industry is an important matter to be resolved. In the hand tractor assembly industry, this small and medium industry is also very dependent on the availability of supply materials, and of course it also depends on the selection of the supplier itself. In this study, the object of research is the 151 hand tractor assembly manufacturing industry using 10 main criteria for selecting suppliers. Each criterion has a weight that is calculated using the Fuzzy AHP method so as to obtain alternative supplier results that are displayed in order. A supplier is a company or individual that provides the raw materials needed by the company and its competitors to produce certain goods. In this study, a decision support system for supplier selection at the company has been developed using the AHP fuzzy method. The results of this study are expected to help companies in selecting suppliers that match the predetermined criteria. In building a decision support system that provides supplier recommendations, the authors apply the AHP fuzzy method in the process. With the Supplier Selection Decision Support System Using the AHP Fuzzy Method, it can help companies in selecting suppliers. Based on the research that has been done, suggestions can be made for a Decision Support System for Supplier Selection Using the Fuzzy AHP Method so that in the future it can facilitate the use of a more dynamic system

Image retrieval based on swarm intelligence

To keep pace with the development of modern technology in this information technology era, and the immense image databases, whether personal or commercial, are increasing, is requiring the management of these databases to strong and accurate systems to retrieve images with high efficiency. Because of the swarm intelligence algorithms are great importance in solving difficult problems and obtaining the best solutions. Here in this research, a proposed system is designed to retrieve color images based on swarm intelligence algorithms. Where the algorithm of the ant colony optimization (ACOM) and the intelligent water drop (IWDM) was used to improve the system's work by conducting the clustering process in these two methods on the features extracted by annular color moment method (ACM) to obtain clustered data, the amount of similarity between them and the query image, is calculated to retrieve images from the database, efficiently and in a short time. In addition, improving the work of these two methods by hybridizing them with fuzzy method, fuzzy gath geva clustering algorithm (FGCA) and obtaining two new high efficiency hybrid algorithms fuzzy ant colony optimization method (FACOM) and fuzzy intelligent water drop method (FIWDM) by retrieving images whose performance values are calculated by calculating the values of precision, recall and the f-measure. It proved its efficiency by comparing it with fuzzy method, FGCA and by methods of swarm intelligence without hybridization, and its work was excellent.

A Neutrosophic Fuzzy Optimisation Model for Optimal Sustainable Closed-Loop Supply Chain Network during COVID-19

In this paper, a sustainable closed-loop supply chain problem is modelled in conditions of uncertainty. Due to the COVID-19 pandemic situation, the designed supply chain network seeks to deliver medical equipment to hospitals on time within a defined time window to prevent overcrowding and virus transmission. In order to achieve a suitable model for designing a sustainable closed-loop supply chain network, important decisions such as locating potential facilities, optimal flow allocation, and vehicle routing have been made to prevent the congestion of vehicles and transmission of the COVID-19 virus. Since the amount of demand in hospitals for medical equipment is unknown, the fuzzy programming method is used to control uncertain demand, and to achieve an efficient solution to the decision-making problem, the neutrosophic fuzzy method is used. The results show that the designed model and the selected solution method (the neutrosophic fuzzy method) have led to a reduction in vehicle traffic by meeting the uncertain demand of hospitals in different time windows. In this way, both the chain network costs have been reduced and medical equipment has been transferred to hospitals with social distancing.

Sensory criteria assessment of fruit from Averrhoa bilimbi L. with comparison of Eckenrode and Fuzzy Eckenrode method

The fruit candy made of processed products made from the natural fruit of Averhoa bilimbi L. whose production was abundant, recently a few to utilization. The main objective of this study was to examine the assessment of sensory criteria fruit candy from A. bilimbi with comparison of eckenrode and fuzzy eckenrode method. The results showed fruit candy contains a higher value of colour (0.239), wherein the aroma (0.169), taste (0.183), texture (0.211), and the overall acceptance (0.197) using the eckenrode method. On the other hand, based on the fuzzy eckenrode method were shown the value of colour (0.224), taste (0.187), aroma (183), texture (0.2110 and overall acceptance (0.195). Although the comparison from eckenrode and fuzzy eckenrode method appear quite stable for criterias of sensory. Finding the optimum value for an aroma in the perception of colour from fruit candy higher to eckenrode than fuzzy method. The eckenrode method provided a dynamic space in a high-level decision-making system to differ significantly. Therefore, the eckenrode method was a quite simple method to establish decisions for weighting a more definite critera in decision making in sensory assessment to develop a natural product from A. bilimbi.