Multiple attribute decision-making model for artificially intelligent last-mile delivery robots selection in neutrosophic square root environment

• Published in: Engineering applications of artificial intelligence 2024-10, Vol.136, p.108878, Article 108878
• Main Authors: Palanikumar, Murugan, Jana, Chiranjibe, Hezam, Ibrahim M., Foul, Abdelaziz, Simic, Vladimir, Pamucar, Dragan
• Format: Article
• Language: English
• Subjects: Euclidean distance; Hamming distance; Multiple-attribute decision-making; Robotic intelligence; Square root neutrosophic sets
• ISSN: 0952-1976
• DOI: 10.1016/j.engappai.2024.108878

We introduce novel methodological techniques for decision-making with multiple attributes utilizing logarithmic square root neutrosophic vague sets. One important thing is that we improved decision-making by adding logarithmic square root neutrosophic ambiguous weighted operators. Logarithmic square root, neutrosophic imprecise weighted averaging, geometric procedures, and expanded versions of these are some of the data processing methodologies that we explore. The use of Hamming distances and Euclidean distances in decision-making situations is illustrated by real-world instances. To clarify the basic properties of these sets, the research uses an algebraic framework. Numerous domains make use of neural networks, including translation, medical diagnosis, and picture and speech recognition. Developing multipurpose artificially intelligent robots with analytical, functional, visual, interactive, and textual capabilities relies heavily on the synergy between computer science and machine tool technology. This is especially true when it comes to the evolution of artificial intelligence. The operating procedures, expenses, time, and externalizes of an artificially intelligent robot system should be considered while assessing its quality. Finding the best answer from a list of possibilities is made easier with the help of expert views and established criteria. By comparing them to other methods, we verify and show that the suggested models work. The study’s findings highlight the importance of the research.