Article Fingerprint
ReserarchID
N0D2I
Crossref Journal DOI 10.17406/gjre
Print ISSN 0975-5861
e-ISSN 2249-4596
Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.
Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.
This paper attempted to assess the attitudes of students in
Advances in technology have created the potential for a new
Inclusion has become a priority on the global educational agenda,
The flexible job-shop scheduling problem (FJSP) is one of the challenging optimization problems as they occupy very large search space. Solving this kind of problems with conventional methods are obsolete now as the Internet of Things (IoT) has changed scheduling platform by means of cloud computing and advanced data analytics. Genetic Algorithms (GAs) is a popular modern tool for machine scheduling problems and in this work, a scheduling algorithm has been developed to minimize total tardiness and make span time of parallel machines which is promoting overall economic sustainability. The algorithm consists of a machine selection module (MSM) that helps to select the right machine on the right time with the help of global selection (GS) technique by generating high quality initial population. To represent an optimized solution of the FJSP, an improved chromosome representation is used while adopting uniform crossover and mutation operator.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.
